added 14.2 and 14.3 for refactor prep

README.md

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+# scrape-giant
+
+CLI to pull purchase history from Giant and Costco websites and refine into a single product catalog for external analysis.
+
+Run each script step-by-step from the terminal.
+
+## What It Does
+
+1. `scrape_giant.py`: download Giant orders and items
+2. `enrich_giant.py`: normalize Giant line items
+3. `scrape_costco.py`: download Costco orders and items
+4. `enrich_costco.py`: normalize Costco line items
+5. `build_purchases.py`: combine retailer outputs into one purchase table
+6. `review_products.py`: review unresolved product matches in the terminal
+7. `report_pipeline_status.py`: show how many rows survive each stage
+
+## Requirements
+
+- Python 3.10+
+- Firefox installed with active Giant and Costco sessions
+
+## Install
+
+```bash
+python -m venv venv
+./venv/scripts/activate
+pip install -r requirements.txt
+```
+
+## Optional `.env`
+
+Current version works best with `.env` in the project root.  The scraper will prompt for these values if they are not found in the current browser session.  
+- `scrape_giant` prompts if `GIANT_USER_ID` or `GIANT_LOYALTY_NUMBER` is missing.
+- `scrape_costco` tries `.env` first, then Firefox local storage for session-backed values; `COSTCO_CLIENT_IDENTIFIER` should still be set explicitly.
+- Costco discount matching happens later in `enrich_costco.py`; you do not need to pre-clean discount lines by hand.
+
+```env
+GIANT_USER_ID=...
+GIANT_LOYALTY_NUMBER=...
+
+COSTCO_X_AUTHORIZATION=...
+COSTCO_X_WCS_CLIENTID=...
+COSTCO_CLIENT_IDENTIFIER=...
+```
+
+## Run Order
+
+Run the pipeline in this order:
+
+```bash
+python scrape_giant.py
+python enrich_giant.py
+python scrape_costco.py
+python enrich_costco.py
+python build_purchases.py
+python review_products.py
+python build_purchases.py
+python review_products.py --refresh-only
+python report_pipeline_status.py
+```
+
+Why run `build_purchases.py` twice:
+- first pass builds the current combined dataset and review queue inputs
+- `review_products.py` writes durable review decisions
+- second pass reapplies those decisions into the purchase output
+
+If you only want to refresh the queue without reviewing interactively:
+
+```bash
+python review_products.py --refresh-only
+```
+
+If you want a quick stage-by-stage accountability check:
+
+```bash
+python report_pipeline_status.py
+```
+
+## Key Outputs
+
+Giant:
+- `giant_output/orders.csv`
+- `giant_output/items.csv`
+- `giant_output/items_enriched.csv`
+
+Costco:
+- `costco_output/orders.csv`
+- `costco_output/items.csv`
+- `costco_output/items_enriched.csv`
+- `costco_output/items_enriched.csv` now preserves raw totals and matched net discount fields
+
+Combined:
+- `combined_output/purchases.csv`
+- `combined_output/review_queue.csv`
+- `combined_output/review_resolutions.csv`
+- `combined_output/canonical_catalog.csv`
+- `combined_output/product_links.csv`
+- `combined_output/comparison_examples.csv`
+- `combined_output/pipeline_status.csv`
+- `combined_output/pipeline_status.json`
+
+## Review Workflow
+
+Run `review_products.py` to cleanup unresolved or weakly unified items:
+- link an item to an existing canonical product
+- create a new canonical product
+- exclude an item
+- skip it for later
+Decisions are saved and reused on later runs.
+
+The review step is intentionally conservative:
+- weak exact-name matches stay in the queue instead of auto-creating canonical products
+- canonical names should describe stable product identity, not retailer packaging text
+
+## Notes
+- This project is designed around fragile retailer scraping flows, so the code favors explicit retailer-specific steps over heavy abstraction.
+- `scrape_giant.py` and `scrape_costco.py` are meant to work as standalone acquisition scripts.
+- Costco discount rows are preserved for auditability and also matched back to purchased items during enrichment.
+- `validate_cross_retailer_flow.py` is a proof/check script, not a required production step.
+
+## Test
+
+```bash
+./venv/bin/python -m unittest discover -s tests
+```
+
+## Project Docs
+
+- `pm/tasks.org`: task tracking
+- `pm/data-model.org`: current data model notes
+- `pm/review-workflow.org`: review and resolution workflow

agents.md

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+# agent rules
+
+## priorities
+- optimize for simplicity, boringness, and long-term maintainability
+- prefer minimal diffs; avoid refactors unless required for the active task
+
+## tech stack
+- python; pandas or polars
+- file storage: json and csv, no sqlite or databases
+- assume local virtual env is available and accessible
+- do not add new dependencies unless explicitly approved; if unavoidable, document justification in the active task notes
+
+## workflow
+- prefer direct argv commands (no bash -lc / compound shell chains) unless necessary
+- work on ONE task at a time unless explicitly instructed otherwise
+- at the start of work, state the task id you are executing
+- do not start work unless a task id is specified; if missing, choose the earliest unchecked task and say so
+- propose incremental steps
+- always include basic tests for core logic
+- when you complete a task:
+  - mark it [x] in pm/tasks.md
+  - fill in evidence with commit hash + commands run
+  - never mark complete unless acceptance criteria are met
+  - include date and time (HH:MM)

browser_session.py

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+import configparser
+import os
+import shutil
+import sqlite3
+import tempfile
+from pathlib import Path
+
+import browser_cookie3
+
+
+def find_firefox_profile_dir():
+    profiles_ini = firefox_profiles_root() / "profiles.ini"
+    parser = configparser.RawConfigParser()
+    if not profiles_ini.exists():
+        raise FileNotFoundError(f"Firefox profiles.ini not found at {profiles_ini}")
+
+    parser.read(profiles_ini, encoding="utf-8")
+    profiles = []
+    for section in parser.sections():
+        if not section.startswith("Profile"):
+            continue
+        path_value = parser.get(section, "Path", fallback="")
+        if not path_value:
+            continue
+        is_relative = parser.getboolean(section, "IsRelative", fallback=True)
+        profile_path = (
+            profiles_ini.parent / path_value if is_relative else Path(path_value)
+        )
+        profiles.append(
+            (
+                parser.getboolean(section, "Default", fallback=False),
+                profile_path,
+            )
+        )
+
+    if not profiles:
+        raise FileNotFoundError("No Firefox profiles found in profiles.ini")
+
+    profiles.sort(key=lambda item: (not item[0], str(item[1])))
+    return profiles[0][1]
+
+
+def firefox_profiles_root():
+    if os.name == "nt":
+        appdata = os.getenv("APPDATA", "").strip()
+        if not appdata:
+            raise FileNotFoundError("APPDATA is not set")
+        return Path(appdata) / "Mozilla" / "Firefox"
+    return Path.home() / ".mozilla" / "firefox"
+
+
+def load_firefox_cookies(domain_name, profile_dir):
+    cookie_file = Path(profile_dir) / "cookies.sqlite"
+    return browser_cookie3.firefox(cookie_file=str(cookie_file), domain_name=domain_name)
+
+
+def read_firefox_local_storage(profile_dir, origin_filter):
+    storage_root = profile_dir / "storage" / "default"
+    if not storage_root.exists():
+        return {}
+
+    for ls_path in storage_root.glob("*/ls/data.sqlite"):
+        origin = decode_firefox_origin(ls_path.parents[1].name)
+        if origin_filter.lower() not in origin.lower():
+            continue
+        return {
+            stringify_sql_value(row[0]): stringify_sql_value(row[1])
+            for row in query_sqlite(ls_path, "SELECT key, value FROM data")
+        }
+    return {}
+
+
+def read_firefox_webapps_store(profile_dir, origin_filter):
+    webapps_path = profile_dir / "webappsstore.sqlite"
+    if not webapps_path.exists():
+        return {}
+
+    values = {}
+    for row in query_sqlite(
+        webapps_path,
+        "SELECT originKey, key, value FROM webappsstore2",
+    ):
+        origin = stringify_sql_value(row[0])
+        if origin_filter.lower() not in origin.lower():
+            continue
+        values[stringify_sql_value(row[1])] = stringify_sql_value(row[2])
+    return values
+
+def query_sqlite(path, query):
+    copied_path = copy_sqlite_to_temp(path)
+    connection = None
+    cursor = None
+    try:
+        connection = sqlite3.connect(copied_path)
+        cursor = connection.cursor()
+        cursor.execute(query)
+        rows = cursor.fetchall()
+        return rows
+    except sqlite3.OperationalError:
+        return []
+    finally:
+        if cursor is not None:
+            cursor.close()
+        if connection is not None:
+            connection.close()
+        copied_path.unlink(missing_ok=True)
+
+
+def copy_sqlite_to_temp(path):
+    fd, tmp = tempfile.mkstemp(suffix=".sqlite")
+    os.close(fd)
+    shutil.copyfile(path, tmp)
+    return Path(tmp)
+
+def decode_firefox_origin(raw_origin):
+    origin = raw_origin.split("^", 1)[0]
+    return origin.replace("+++", "://")
+
+def stringify_sql_value(value):
+    if value is None:
+        return ""
+    if isinstance(value, bytes):
+        for encoding in ("utf-8", "utf-16-le", "utf-16"):
+            try:
+                return value.decode(encoding)
+            except UnicodeDecodeError:
+                continue
+        return value.decode("utf-8", errors="ignore")
+    return str(value)

build_canonical_layer.py

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+import click
+import re
+
+from layer_helpers import read_csv_rows, representative_value, stable_id, write_csv_rows
+
+
+CANONICAL_FIELDS = [
+    "canonical_product_id",
+    "canonical_name",
+    "product_type",
+    "brand",
+    "variant",
+    "size_value",
+    "size_unit",
+    "pack_qty",
+    "measure_type",
+    "normalized_quantity",
+    "normalized_quantity_unit",
+    "notes",
+    "created_at",
+    "updated_at",
+]
+
+CANONICAL_DROP_TOKENS = {"CT", "COUNT", "COUNTS", "DOZ", "DOZEN", "DOZ.", "PACK"}
+
+LINK_FIELDS = [
+    "observed_product_id",
+    "canonical_product_id",
+    "link_method",
+    "link_confidence",
+    "review_status",
+    "reviewed_by",
+    "reviewed_at",
+    "link_notes",
+]
+
+
+def to_float(value):
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def normalized_quantity(row):
+    size_value = to_float(row.get("representative_size_value"))
+    pack_qty = to_float(row.get("representative_pack_qty")) or 1.0
+    size_unit = row.get("representative_size_unit", "")
+    measure_type = row.get("representative_measure_type", "")
+
+    if size_value is not None and size_unit:
+        return format(size_value * pack_qty, "g"), size_unit
+
+    if row.get("representative_pack_qty") and measure_type == "count":
+        return row["representative_pack_qty"], "count"
+
+    if measure_type == "each":
+        return "1", "each"
+
+    return "", ""
+
+
+def auto_link_rule(observed_row):
+    if (
+        observed_row.get("is_fee") == "true"
+        or observed_row.get("is_discount_line") == "true"
+        or observed_row.get("is_coupon_line") == "true"
+    ):
+        return "", "", ""
+
+    if observed_row.get("representative_upc"):
+        return (
+            "exact_upc",
+            f"upc={observed_row['representative_upc']}",
+            "high",
+        )
+
+    if (
+        observed_row.get("representative_name_norm")
+        and observed_row.get("representative_size_value")
+        and observed_row.get("representative_size_unit")
+    ):
+        return (
+            "exact_name_size",
+            "|".join(
+                [
+                    f"name={observed_row['representative_name_norm']}",
+                    f"size={observed_row['representative_size_value']}",
+                    f"unit={observed_row['representative_size_unit']}",
+                    f"pack={observed_row['representative_pack_qty']}",
+                    f"measure={observed_row['representative_measure_type']}",
+                ]
+            ),
+            "high",
+        )
+
+    return "", "", ""
+
+
+def clean_canonical_name(name):
+    tokens = []
+    for token in re.sub(r"[^A-Z0-9\s]", " ", (name or "").upper()).split():
+        if token.isdigit():
+            continue
+        if token in CANONICAL_DROP_TOKENS:
+            continue
+        if re.fullmatch(r"\d+(?:PK|PACK)", token):
+            continue
+        if re.fullmatch(r"\d+DZ", token):
+            continue
+        tokens.append(token)
+    return " ".join(tokens).strip()
+
+
+def canonical_row_for_group(canonical_product_id, group_rows, link_method):
+    quantity_value, quantity_unit = normalized_quantity(
+        {
+            "representative_size_value": representative_value(
+                group_rows, "representative_size_value"
+            ),
+            "representative_size_unit": representative_value(
+                group_rows, "representative_size_unit"
+            ),
+            "representative_pack_qty": representative_value(
+                group_rows, "representative_pack_qty"
+            ),
+            "representative_measure_type": representative_value(
+                group_rows, "representative_measure_type"
+            ),
+        }
+    )
+    return {
+        "canonical_product_id": canonical_product_id,
+        "canonical_name": clean_canonical_name(
+            representative_value(group_rows, "representative_name_norm")
+        )
+        or representative_value(group_rows, "representative_name_norm"),
+        "product_type": "",
+        "brand": representative_value(group_rows, "representative_brand"),
+        "variant": representative_value(group_rows, "representative_variant"),
+        "size_value": representative_value(group_rows, "representative_size_value"),
+        "size_unit": representative_value(group_rows, "representative_size_unit"),
+        "pack_qty": representative_value(group_rows, "representative_pack_qty"),
+        "measure_type": representative_value(group_rows, "representative_measure_type"),
+        "normalized_quantity": quantity_value,
+        "normalized_quantity_unit": quantity_unit,
+        "notes": f"auto-linked via {link_method}",
+        "created_at": "",
+        "updated_at": "",
+    }
+
+
+def build_canonical_layer(observed_rows):
+    canonical_rows = []
+    link_rows = []
+    groups = {}
+
+    for observed_row in sorted(observed_rows, key=lambda row: row["observed_product_id"]):
+        link_method, group_key, confidence = auto_link_rule(observed_row)
+        if not group_key:
+            continue
+
+        canonical_product_id = stable_id("gcan", f"{link_method}|{group_key}")
+        groups.setdefault(canonical_product_id, {"method": link_method, "rows": []})
+        groups[canonical_product_id]["rows"].append(observed_row)
+        link_rows.append(
+            {
+                "observed_product_id": observed_row["observed_product_id"],
+                "canonical_product_id": canonical_product_id,
+                "link_method": link_method,
+                "link_confidence": confidence,
+                "review_status": "",
+                "reviewed_by": "",
+                "reviewed_at": "",
+                "link_notes": "",
+            }
+        )
+
+    for canonical_product_id, group in sorted(groups.items()):
+        canonical_rows.append(
+            canonical_row_for_group(
+                canonical_product_id, group["rows"], group["method"]
+            )
+        )
+
+    return canonical_rows, link_rows
+
+
+@click.command()
+@click.option(
+    "--observed-csv",
+    default="giant_output/products_observed.csv",
+    show_default=True,
+    help="Path to observed product rows.",
+)
+@click.option(
+    "--canonical-csv",
+    default="giant_output/products_canonical.csv",
+    show_default=True,
+    help="Path to canonical product output.",
+)
+@click.option(
+    "--links-csv",
+    default="giant_output/product_links.csv",
+    show_default=True,
+    help="Path to observed-to-canonical link output.",
+)
+def main(observed_csv, canonical_csv, links_csv):
+    observed_rows = read_csv_rows(observed_csv)
+    canonical_rows, link_rows = build_canonical_layer(observed_rows)
+    write_csv_rows(canonical_csv, canonical_rows, CANONICAL_FIELDS)
+    write_csv_rows(links_csv, link_rows, LINK_FIELDS)
+    click.echo(
+        f"wrote {len(canonical_rows)} canonical rows to {canonical_csv} and "
+        f"{len(link_rows)} links to {links_csv}"
+    )
+
+
+if __name__ == "__main__":
+    main()

build_observed_products.py

@@ -0,0 +1,172 @@
+from collections import defaultdict
+
+import click
+
+from layer_helpers import (
+    compact_join,
+    distinct_values,
+    first_nonblank,
+    read_csv_rows,
+    representative_value,
+    stable_id,
+    write_csv_rows,
+)
+
+
+OUTPUT_FIELDS = [
+    "observed_product_id",
+    "retailer",
+    "observed_key",
+    "representative_retailer_item_id",
+    "representative_upc",
+    "representative_item_name",
+    "representative_name_norm",
+    "representative_brand",
+    "representative_variant",
+    "representative_size_value",
+    "representative_size_unit",
+    "representative_pack_qty",
+    "representative_measure_type",
+    "representative_image_url",
+    "is_store_brand",
+    "is_fee",
+    "is_discount_line",
+    "is_coupon_line",
+    "first_seen_date",
+    "last_seen_date",
+    "times_seen",
+    "example_order_id",
+    "example_item_name",
+    "raw_name_examples",
+    "normalized_name_examples",
+    "example_prices",
+    "distinct_item_names_count",
+    "distinct_retailer_item_ids_count",
+    "distinct_upcs_count",
+]
+
+
+def build_observed_key(row):
+    if row.get("upc"):
+        return "|".join(
+            [
+                row["retailer"],
+                f"upc={row['upc']}",
+                f"name={row['item_name_norm']}",
+            ]
+        )
+
+    if row.get("retailer_item_id"):
+        return "|".join(
+            [
+                row["retailer"],
+                f"retailer_item_id={row['retailer_item_id']}",
+                f"name={row['item_name_norm']}",
+                f"discount={row.get('is_discount_line', 'false')}",
+                f"coupon={row.get('is_coupon_line', 'false')}",
+            ]
+        )
+
+    return "|".join(
+        [
+            row["retailer"],
+            f"name={row['item_name_norm']}",
+            f"size={row['size_value']}",
+            f"unit={row['size_unit']}",
+            f"pack={row['pack_qty']}",
+            f"measure={row['measure_type']}",
+            f"store_brand={row['is_store_brand']}",
+            f"fee={row['is_fee']}",
+        ]
+    )
+
+
+def build_observed_products(rows):
+    grouped = defaultdict(list)
+    for row in rows:
+        grouped[build_observed_key(row)].append(row)
+
+    observed_rows = []
+    for observed_key, group_rows in sorted(grouped.items()):
+        ordered = sorted(
+            group_rows,
+            key=lambda row: (row["order_date"], row["order_id"], int(row["line_no"])),
+        )
+        observed_rows.append(
+            {
+                "observed_product_id": stable_id("gobs", observed_key),
+                "retailer": ordered[0]["retailer"],
+                "observed_key": observed_key,
+                "representative_retailer_item_id": representative_value(
+                    ordered, "retailer_item_id"
+                ),
+                "representative_upc": representative_value(ordered, "upc"),
+                "representative_item_name": representative_value(ordered, "item_name"),
+                "representative_name_norm": representative_value(
+                    ordered, "item_name_norm"
+                ),
+                "representative_brand": representative_value(ordered, "brand_guess"),
+                "representative_variant": representative_value(ordered, "variant"),
+                "representative_size_value": representative_value(ordered, "size_value"),
+                "representative_size_unit": representative_value(ordered, "size_unit"),
+                "representative_pack_qty": representative_value(ordered, "pack_qty"),
+                "representative_measure_type": representative_value(
+                    ordered, "measure_type"
+                ),
+                "representative_image_url": first_nonblank(ordered, "image_url"),
+                "is_store_brand": representative_value(ordered, "is_store_brand"),
+                "is_fee": representative_value(ordered, "is_fee"),
+                "is_discount_line": representative_value(
+                    ordered, "is_discount_line"
+                ),
+                "is_coupon_line": representative_value(ordered, "is_coupon_line"),
+                "first_seen_date": ordered[0]["order_date"],
+                "last_seen_date": ordered[-1]["order_date"],
+                "times_seen": str(len(ordered)),
+                "example_order_id": ordered[0]["order_id"],
+                "example_item_name": ordered[0]["item_name"],
+                "raw_name_examples": compact_join(
+                    distinct_values(ordered, "item_name"), limit=4
+                ),
+                "normalized_name_examples": compact_join(
+                    distinct_values(ordered, "item_name_norm"), limit=4
+                ),
+                "example_prices": compact_join(
+                    distinct_values(ordered, "line_total"), limit=4
+                ),
+                "distinct_item_names_count": str(
+                    len(distinct_values(ordered, "item_name"))
+                ),
+                "distinct_retailer_item_ids_count": str(
+                    len(distinct_values(ordered, "retailer_item_id"))
+                ),
+                "distinct_upcs_count": str(len(distinct_values(ordered, "upc"))),
+            }
+        )
+
+    observed_rows.sort(key=lambda row: row["observed_product_id"])
+    return observed_rows
+
+
+@click.command()
+@click.option(
+    "--items-enriched-csv",
+    default="giant_output/items_enriched.csv",
+    show_default=True,
+    help="Path to enriched Giant item rows.",
+)
+@click.option(
+    "--output-csv",
+    default="giant_output/products_observed.csv",
+    show_default=True,
+    help="Path to observed product output.",
+)
+def main(items_enriched_csv, output_csv):
+    rows = read_csv_rows(items_enriched_csv)
+    observed_rows = build_observed_products(rows)
+    write_csv_rows(output_csv, observed_rows, OUTPUT_FIELDS)
+    click.echo(f"wrote {len(observed_rows)} rows to {output_csv}")
+
+
+if __name__ == "__main__":
+    main()

build_purchases.py

@@ -0,0 +1,418 @@
+from decimal import Decimal
+from pathlib import Path
+
+import click
+
+import build_canonical_layer
+import build_observed_products
+import validate_cross_retailer_flow
+from enrich_giant import format_decimal, to_decimal
+from layer_helpers import read_csv_rows, stable_id, write_csv_rows
+
+
+PURCHASE_FIELDS = [
+    "purchase_date",
+    "retailer",
+    "order_id",
+    "line_no",
+    "observed_item_key",
+    "observed_product_id",
+    "canonical_product_id",
+    "review_status",
+    "resolution_action",
+    "raw_item_name",
+    "normalized_item_name",
+    "image_url",
+    "retailer_item_id",
+    "upc",
+    "qty",
+    "unit",
+    "pack_qty",
+    "size_value",
+    "size_unit",
+    "measure_type",
+    "line_total",
+    "unit_price",
+    "matched_discount_amount",
+    "net_line_total",
+    "store_name",
+    "store_number",
+    "store_city",
+    "store_state",
+    "price_per_each",
+    "price_per_each_basis",
+    "price_per_count",
+    "price_per_count_basis",
+    "price_per_lb",
+    "price_per_lb_basis",
+    "price_per_oz",
+    "price_per_oz_basis",
+    "is_discount_line",
+    "is_coupon_line",
+    "is_fee",
+    "raw_order_path",
+]
+
+EXAMPLE_FIELDS = [
+    "example_name",
+    "canonical_product_id",
+    "giant_purchase_date",
+    "giant_raw_item_name",
+    "giant_price_per_lb",
+    "costco_purchase_date",
+    "costco_raw_item_name",
+    "costco_price_per_lb",
+    "notes",
+]
+
+CATALOG_FIELDS = [
+    "canonical_product_id",
+    "canonical_name",
+    "category",
+    "product_type",
+    "brand",
+    "variant",
+    "size_value",
+    "size_unit",
+    "pack_qty",
+    "measure_type",
+    "notes",
+    "created_at",
+    "updated_at",
+]
+
+RESOLUTION_FIELDS = [
+    "observed_product_id",
+    "canonical_product_id",
+    "resolution_action",
+    "status",
+    "resolution_notes",
+    "reviewed_at",
+]
+
+
+def decimal_or_zero(value):
+    return to_decimal(value) or Decimal("0")
+
+
+def derive_metrics(row):
+    line_total = to_decimal(row.get("net_line_total") or row.get("line_total"))
+    qty = to_decimal(row.get("qty"))
+    pack_qty = to_decimal(row.get("pack_qty"))
+    size_value = to_decimal(row.get("size_value"))
+    picked_weight = to_decimal(row.get("picked_weight"))
+    size_unit = row.get("size_unit", "")
+
+    price_per_each = row.get("price_per_each", "")
+    price_per_lb = row.get("price_per_lb", "")
+    price_per_oz = row.get("price_per_oz", "")
+    price_per_count = ""
+
+    basis_each = ""
+    basis_count = ""
+    basis_lb = ""
+    basis_oz = ""
+
+    if price_per_each:
+        basis_each = "line_total_over_qty"
+    elif line_total is not None and qty not in (None, 0):
+        price_per_each = format_decimal(line_total / qty)
+        basis_each = "line_total_over_qty"
+
+    if line_total is not None and pack_qty not in (None, 0):
+        total_count = pack_qty * (qty or Decimal("1"))
+        if total_count not in (None, 0):
+            price_per_count = format_decimal(line_total / total_count)
+            basis_count = "line_total_over_pack_qty"
+
+    if picked_weight not in (None, 0):
+        price_per_lb = format_decimal(line_total / picked_weight) if line_total is not None else ""
+        price_per_oz = (
+            format_decimal((line_total / picked_weight) / Decimal("16"))
+            if line_total is not None
+            else ""
+        )
+        basis_lb = "picked_weight_lb"
+        basis_oz = "picked_weight_lb_to_oz"
+    elif line_total is not None and size_value not in (None, 0):
+        total_units = size_value * (pack_qty or Decimal("1")) * (qty or Decimal("1"))
+        if size_unit == "lb" and total_units not in (None, 0):
+            per_lb = line_total / total_units
+            price_per_lb = format_decimal(per_lb)
+            price_per_oz = format_decimal(per_lb / Decimal("16"))
+            basis_lb = "parsed_size_lb"
+            basis_oz = "parsed_size_lb_to_oz"
+        elif size_unit == "oz" and total_units not in (None, 0):
+            per_oz = line_total / total_units
+            price_per_oz = format_decimal(per_oz)
+            price_per_lb = format_decimal(per_oz * Decimal("16"))
+            basis_lb = "parsed_size_oz_to_lb"
+            basis_oz = "parsed_size_oz"
+
+    return {
+        "price_per_each": price_per_each,
+        "price_per_each_basis": basis_each,
+        "price_per_count": price_per_count,
+        "price_per_count_basis": basis_count,
+        "price_per_lb": price_per_lb,
+        "price_per_lb_basis": basis_lb,
+        "price_per_oz": price_per_oz,
+        "price_per_oz_basis": basis_oz,
+    }
+
+
+def order_lookup(rows, retailer):
+    return {
+        (retailer, row["order_id"]): row
+        for row in rows
+    }
+
+
+def read_optional_csv_rows(path):
+    path = Path(path)
+    if not path.exists():
+        return []
+    return read_csv_rows(path)
+
+
+def load_resolution_lookup(resolution_rows):
+    lookup = {}
+    for row in resolution_rows:
+        if not row.get("observed_product_id"):
+            continue
+        lookup[row["observed_product_id"]] = row
+    return lookup
+
+
+def merge_catalog_rows(existing_rows, auto_rows):
+    merged = {}
+    for row in auto_rows + existing_rows:
+        canonical_product_id = row.get("canonical_product_id", "")
+        if canonical_product_id:
+            merged[canonical_product_id] = row
+    return sorted(merged.values(), key=lambda row: row["canonical_product_id"])
+
+
+def catalog_row_from_canonical(row):
+    return {
+        "canonical_product_id": row.get("canonical_product_id", ""),
+        "canonical_name": row.get("canonical_name", ""),
+        "category": row.get("category", ""),
+        "product_type": row.get("product_type", ""),
+        "brand": row.get("brand", ""),
+        "variant": row.get("variant", ""),
+        "size_value": row.get("size_value", ""),
+        "size_unit": row.get("size_unit", ""),
+        "pack_qty": row.get("pack_qty", ""),
+        "measure_type": row.get("measure_type", ""),
+        "notes": row.get("notes", ""),
+        "created_at": row.get("created_at", ""),
+        "updated_at": row.get("updated_at", ""),
+    }
+
+
+def build_link_state(enriched_rows):
+    observed_rows = build_observed_products.build_observed_products(enriched_rows)
+    canonical_rows, link_rows = build_canonical_layer.build_canonical_layer(observed_rows)
+    giant_row, costco_row = validate_cross_retailer_flow.find_proof_pair(observed_rows)
+    canonical_rows, link_rows, _proof_rows = validate_cross_retailer_flow.merge_proof_pair(
+        canonical_rows,
+        link_rows,
+        giant_row,
+        costco_row,
+    )
+
+    observed_id_by_key = {
+        row["observed_key"]: row["observed_product_id"] for row in observed_rows
+    }
+    canonical_id_by_observed = {
+        row["observed_product_id"]: row["canonical_product_id"] for row in link_rows
+    }
+    return observed_rows, canonical_rows, link_rows, observed_id_by_key, canonical_id_by_observed
+
+
+def build_purchase_rows(
+    giant_enriched_rows,
+    costco_enriched_rows,
+    giant_orders,
+    costco_orders,
+    resolution_rows,
+):
+    all_enriched_rows = giant_enriched_rows + costco_enriched_rows
+    (
+        observed_rows,
+        canonical_rows,
+        link_rows,
+        observed_id_by_key,
+        canonical_id_by_observed,
+    ) = build_link_state(all_enriched_rows)
+    resolution_lookup = load_resolution_lookup(resolution_rows)
+    for observed_product_id, resolution in resolution_lookup.items():
+        action = resolution.get("resolution_action", "")
+        status = resolution.get("status", "")
+        if status != "approved":
+            continue
+        if action in {"link", "create"} and resolution.get("canonical_product_id"):
+            canonical_id_by_observed[observed_product_id] = resolution["canonical_product_id"]
+        elif action == "exclude":
+            canonical_id_by_observed[observed_product_id] = ""
+    orders_by_id = {}
+    orders_by_id.update(order_lookup(giant_orders, "giant"))
+    orders_by_id.update(order_lookup(costco_orders, "costco"))
+
+    purchase_rows = []
+    for row in sorted(
+        all_enriched_rows,
+        key=lambda item: (item["order_date"], item["retailer"], item["order_id"], int(item["line_no"])),
+    ):
+        observed_key = build_observed_products.build_observed_key(row)
+        observed_product_id = observed_id_by_key.get(observed_key, "")
+        order_row = orders_by_id.get((row["retailer"], row["order_id"]), {})
+        metrics = derive_metrics(row)
+        resolution = resolution_lookup.get(observed_product_id, {})
+        purchase_rows.append(
+            {
+                "purchase_date": row["order_date"],
+                "retailer": row["retailer"],
+                "order_id": row["order_id"],
+                "line_no": row["line_no"],
+                "observed_item_key": row["observed_item_key"],
+                "observed_product_id": observed_product_id,
+                "canonical_product_id": canonical_id_by_observed.get(observed_product_id, ""),
+                "review_status": resolution.get("status", ""),
+                "resolution_action": resolution.get("resolution_action", ""),
+                "raw_item_name": row["item_name"],
+                "normalized_item_name": row["item_name_norm"],
+                "image_url": row.get("image_url", ""),
+                "retailer_item_id": row["retailer_item_id"],
+                "upc": row["upc"],
+                "qty": row["qty"],
+                "unit": row["unit"],
+                "pack_qty": row["pack_qty"],
+                "size_value": row["size_value"],
+                "size_unit": row["size_unit"],
+                "measure_type": row["measure_type"],
+                "line_total": row["line_total"],
+                "unit_price": row["unit_price"],
+                "matched_discount_amount": row.get("matched_discount_amount", ""),
+                "net_line_total": row.get("net_line_total", ""),
+                "store_name": order_row.get("store_name", ""),
+                "store_number": order_row.get("store_number", ""),
+                "store_city": order_row.get("store_city", ""),
+                "store_state": order_row.get("store_state", ""),
+                "is_discount_line": row["is_discount_line"],
+                "is_coupon_line": row["is_coupon_line"],
+                "is_fee": row["is_fee"],
+                "raw_order_path": row["raw_order_path"],
+                **metrics,
+            }
+        )
+    return purchase_rows, observed_rows, canonical_rows, link_rows
+
+
+def apply_manual_resolutions_to_links(link_rows, resolution_rows):
+    link_by_observed = {row["observed_product_id"]: dict(row) for row in link_rows}
+    for resolution in resolution_rows:
+        if resolution.get("status") != "approved":
+            continue
+        observed_product_id = resolution.get("observed_product_id", "")
+        action = resolution.get("resolution_action", "")
+        if not observed_product_id:
+            continue
+        if action == "exclude":
+            link_by_observed.pop(observed_product_id, None)
+            continue
+        if action in {"link", "create"} and resolution.get("canonical_product_id"):
+            link_by_observed[observed_product_id] = {
+                "observed_product_id": observed_product_id,
+                "canonical_product_id": resolution["canonical_product_id"],
+                "link_method": f"manual_{action}",
+                "link_confidence": "high",
+                "review_status": resolution.get("status", ""),
+                "reviewed_by": "",
+                "reviewed_at": resolution.get("reviewed_at", ""),
+                "link_notes": resolution.get("resolution_notes", ""),
+            }
+    return sorted(link_by_observed.values(), key=lambda row: row["observed_product_id"])
+
+
+def build_comparison_examples(purchase_rows):
+    giant_banana = None
+    costco_banana = None
+    for row in purchase_rows:
+        if row.get("normalized_item_name") != "BANANA":
+            continue
+        if not row.get("canonical_product_id"):
+            continue
+        if row["retailer"] == "giant" and row.get("price_per_lb"):
+            giant_banana = row
+        if row["retailer"] == "costco" and row.get("price_per_lb"):
+            costco_banana = row
+
+    if not giant_banana or not costco_banana:
+        return []
+
+    return [
+        {
+            "example_name": "banana_price_per_lb",
+            "canonical_product_id": giant_banana["canonical_product_id"],
+            "giant_purchase_date": giant_banana["purchase_date"],
+            "giant_raw_item_name": giant_banana["raw_item_name"],
+            "giant_price_per_lb": giant_banana["price_per_lb"],
+            "costco_purchase_date": costco_banana["purchase_date"],
+            "costco_raw_item_name": costco_banana["raw_item_name"],
+            "costco_price_per_lb": costco_banana["price_per_lb"],
+            "notes": "Example comparison using normalized price_per_lb across Giant and Costco",
+        }
+    ]
+
+
+@click.command()
+@click.option("--giant-items-enriched-csv", default="giant_output/items_enriched.csv", show_default=True)
+@click.option("--costco-items-enriched-csv", default="costco_output/items_enriched.csv", show_default=True)
+@click.option("--giant-orders-csv", default="giant_output/orders.csv", show_default=True)
+@click.option("--costco-orders-csv", default="costco_output/orders.csv", show_default=True)
+@click.option("--resolutions-csv", default="combined_output/review_resolutions.csv", show_default=True)
+@click.option("--catalog-csv", default="combined_output/canonical_catalog.csv", show_default=True)
+@click.option("--links-csv", default="combined_output/product_links.csv", show_default=True)
+@click.option("--output-csv", default="combined_output/purchases.csv", show_default=True)
+@click.option("--examples-csv", default="combined_output/comparison_examples.csv", show_default=True)
+def main(
+    giant_items_enriched_csv,
+    costco_items_enriched_csv,
+    giant_orders_csv,
+    costco_orders_csv,
+    resolutions_csv,
+    catalog_csv,
+    links_csv,
+    output_csv,
+    examples_csv,
+):
+    resolution_rows = read_optional_csv_rows(resolutions_csv)
+    purchase_rows, _observed_rows, canonical_rows, link_rows = build_purchase_rows(
+        read_csv_rows(giant_items_enriched_csv),
+        read_csv_rows(costco_items_enriched_csv),
+        read_csv_rows(giant_orders_csv),
+        read_csv_rows(costco_orders_csv),
+        resolution_rows,
+    )
+    existing_catalog_rows = read_optional_csv_rows(catalog_csv)
+    merged_catalog_rows = merge_catalog_rows(
+        existing_catalog_rows,
+        [catalog_row_from_canonical(row) for row in canonical_rows],
+    )
+    link_rows = apply_manual_resolutions_to_links(link_rows, resolution_rows)
+    example_rows = build_comparison_examples(purchase_rows)
+    write_csv_rows(catalog_csv, merged_catalog_rows, CATALOG_FIELDS)
+    write_csv_rows(links_csv, link_rows, build_canonical_layer.LINK_FIELDS)
+    write_csv_rows(output_csv, purchase_rows, PURCHASE_FIELDS)
+    write_csv_rows(examples_csv, example_rows, EXAMPLE_FIELDS)
+    click.echo(
+        f"wrote {len(purchase_rows)} purchase rows to {output_csv}, "
+        f"{len(merged_catalog_rows)} catalog rows to {catalog_csv}, "
+        f"and {len(example_rows)} comparison examples to {examples_csv}"
+    )
+
+
+if __name__ == "__main__":
+    main()

build_review_queue.py

@@ -0,0 +1,175 @@
+from collections import defaultdict
+from datetime import date
+
+import click
+
+from layer_helpers import compact_join, distinct_values, read_csv_rows, stable_id, write_csv_rows
+
+
+OUTPUT_FIELDS = [
+    "review_id",
+    "queue_type",
+    "retailer",
+    "observed_product_id",
+    "canonical_product_id",
+    "reason_code",
+    "priority",
+    "raw_item_names",
+    "normalized_names",
+    "upc",
+    "image_url",
+    "example_prices",
+    "seen_count",
+    "status",
+    "resolution_notes",
+    "created_at",
+    "updated_at",
+]
+
+
+def existing_review_state(path):
+    try:
+        rows = read_csv_rows(path)
+    except FileNotFoundError:
+        return {}
+    return {row["review_id"]: row for row in rows}
+
+
+def review_reasons(observed_row):
+    reasons = []
+    if (
+        observed_row["is_fee"] == "true"
+        or observed_row.get("is_discount_line") == "true"
+        or observed_row.get("is_coupon_line") == "true"
+    ):
+        return reasons
+    if observed_row["distinct_upcs_count"] not in {"", "0", "1"}:
+        reasons.append(("multiple_upcs", "high"))
+    if observed_row["distinct_item_names_count"] not in {"", "0", "1"}:
+        reasons.append(("multiple_raw_names", "medium"))
+    if not observed_row["representative_image_url"]:
+        reasons.append(("missing_image", "medium"))
+    if not observed_row["representative_upc"]:
+        reasons.append(("missing_upc", "high"))
+    if not observed_row["representative_name_norm"]:
+        reasons.append(("missing_normalized_name", "high"))
+    return reasons
+
+
+def build_review_queue(observed_rows, item_rows, existing_rows, today_text):
+    by_observed = defaultdict(list)
+    for row in item_rows:
+        observed_id = row.get("observed_product_id", "")
+        if observed_id:
+            by_observed[observed_id].append(row)
+
+    queue_rows = []
+    for observed_row in observed_rows:
+        reasons = review_reasons(observed_row)
+        if not reasons:
+            continue
+
+        related_items = by_observed.get(observed_row["observed_product_id"], [])
+        raw_names = compact_join(distinct_values(related_items, "item_name"), limit=5)
+        norm_names = compact_join(
+            distinct_values(related_items, "item_name_norm"), limit=5
+        )
+        example_prices = compact_join(
+            distinct_values(related_items, "line_total"), limit=5
+        )
+
+        for reason_code, priority in reasons:
+            review_id = stable_id(
+                "rvw",
+                f"{observed_row['observed_product_id']}|{reason_code}",
+            )
+            prior = existing_rows.get(review_id, {})
+            queue_rows.append(
+                {
+                    "review_id": review_id,
+                    "queue_type": "observed_product",
+                    "retailer": observed_row["retailer"],
+                    "observed_product_id": observed_row["observed_product_id"],
+                    "canonical_product_id": prior.get("canonical_product_id", ""),
+                    "reason_code": reason_code,
+                    "priority": priority,
+                    "raw_item_names": raw_names,
+                    "normalized_names": norm_names,
+                    "upc": observed_row["representative_upc"],
+                    "image_url": observed_row["representative_image_url"],
+                    "example_prices": example_prices,
+                    "seen_count": observed_row["times_seen"],
+                    "status": prior.get("status", "pending"),
+                    "resolution_notes": prior.get("resolution_notes", ""),
+                    "created_at": prior.get("created_at", today_text),
+                    "updated_at": today_text,
+                }
+            )
+
+    queue_rows.sort(key=lambda row: (row["priority"], row["reason_code"], row["review_id"]))
+    return queue_rows
+
+
+def attach_observed_ids(item_rows, observed_rows):
+    observed_by_key = {row["observed_key"]: row["observed_product_id"] for row in observed_rows}
+    attached = []
+    for row in item_rows:
+        observed_key = "|".join(
+            [
+                row["retailer"],
+                f"upc={row['upc']}",
+                f"name={row['item_name_norm']}",
+            ]
+        ) if row.get("upc") else "|".join(
+            [
+                row["retailer"],
+                f"retailer_item_id={row.get('retailer_item_id', '')}",
+                f"name={row['item_name_norm']}",
+                f"size={row['size_value']}",
+                f"unit={row['size_unit']}",
+                f"pack={row['pack_qty']}",
+                f"measure={row['measure_type']}",
+                f"store_brand={row['is_store_brand']}",
+                f"fee={row['is_fee']}",
+                f"discount={row.get('is_discount_line', 'false')}",
+                f"coupon={row.get('is_coupon_line', 'false')}",
+            ]
+        )
+        enriched = dict(row)
+        enriched["observed_product_id"] = observed_by_key.get(observed_key, "")
+        attached.append(enriched)
+    return attached
+
+
+@click.command()
+@click.option(
+    "--observed-csv",
+    default="giant_output/products_observed.csv",
+    show_default=True,
+    help="Path to observed product rows.",
+)
+@click.option(
+    "--items-enriched-csv",
+    default="giant_output/items_enriched.csv",
+    show_default=True,
+    help="Path to enriched Giant item rows.",
+)
+@click.option(
+    "--output-csv",
+    default="giant_output/review_queue.csv",
+    show_default=True,
+    help="Path to review queue output.",
+)
+def main(observed_csv, items_enriched_csv, output_csv):
+    observed_rows = read_csv_rows(observed_csv)
+    item_rows = read_csv_rows(items_enriched_csv)
+    item_rows = attach_observed_ids(item_rows, observed_rows)
+    existing_rows = existing_review_state(output_csv)
+    today_text = str(date.today())
+    queue_rows = build_review_queue(observed_rows, item_rows, existing_rows, today_text)
+    write_csv_rows(output_csv, queue_rows, OUTPUT_FIELDS)
+    click.echo(f"wrote {len(queue_rows)} rows to {output_csv}")
+
+
+if __name__ == "__main__":
+    main()

collect_costco_web.py

@@ -0,0 +1,65 @@
+import click
+
+import scrape_costco
+
+
+@click.command()
+@click.option(
+    "--outdir",
+    default="data/costco-web",
+    show_default=True,
+    help="Directory for Costco raw and collected outputs.",
+)
+@click.option(
+    "--document-type",
+    default="all",
+    show_default=True,
+    help="Summary document type.",
+)
+@click.option(
+    "--document-sub-type",
+    default="all",
+    show_default=True,
+    help="Summary document sub type.",
+)
+@click.option(
+    "--window-days",
+    default=92,
+    show_default=True,
+    type=int,
+    help="Maximum number of days to request per summary window.",
+)
+@click.option(
+    "--months-back",
+    default=36,
+    show_default=True,
+    type=int,
+    help="How many months of receipts to enumerate back from today.",
+)
+@click.option(
+    "--firefox-profile-dir",
+    default=None,
+    help="Firefox profile directory to use for cookies and session storage.",
+)
+def main(
+    outdir,
+    document_type,
+    document_sub_type,
+    window_days,
+    months_back,
+    firefox_profile_dir,
+):
+    scrape_costco.run_collection(
+        outdir=outdir,
+        document_type=document_type,
+        document_sub_type=document_sub_type,
+        window_days=window_days,
+        months_back=months_back,
+        firefox_profile_dir=firefox_profile_dir,
+        orders_filename="collected_orders.csv",
+        items_filename="collected_items.csv",
+    )
+
+
+if __name__ == "__main__":
+    main()

collect_giant_web.py

@@ -0,0 +1,34 @@
+import click
+
+import scrape_giant
+
+
+@click.command()
+@click.option("--user-id", default=None, help="Giant user id.")
+@click.option("--loyalty", default=None, help="Giant loyalty number.")
+@click.option(
+    "--outdir",
+    default="data/giant-web",
+    show_default=True,
+    help="Directory for raw json and collected csv outputs.",
+)
+@click.option(
+    "--sleep-seconds",
+    default=1.5,
+    show_default=True,
+    type=float,
+    help="Delay between order detail requests.",
+)
+def main(user_id, loyalty, outdir, sleep_seconds):
+    scrape_giant.run_collection(
+        user_id,
+        loyalty,
+        outdir,
+        sleep_seconds,
+        orders_filename="collected_orders.csv",
+        items_filename="collected_items.csv",
+    )
+
+
+if __name__ == "__main__":
+    main()

enrich_costco.py

@@ -0,0 +1,365 @@
+import csv
+import json
+import re
+from collections import defaultdict
+from pathlib import Path
+
+import click
+
+from enrich_giant import (
+    OUTPUT_FIELDS,
+    derive_normalized_quantity,
+    derive_price_fields,
+    format_decimal,
+    normalization_identity,
+    normalize_number,
+    normalize_unit,
+    normalize_whitespace,
+    singularize_tokens,
+    to_decimal,
+)
+
+
+PARSER_VERSION = "costco-enrich-v1"
+RETAILER = "costco"
+DEFAULT_INPUT_DIR = Path("costco_output/raw")
+DEFAULT_OUTPUT_CSV = Path("costco_output/items_enriched.csv")
+
+CODE_TOKEN_RE = re.compile(
+    r"\b(?:SL\d+|T\d+H\d+|P\d+(?:/\d+)?|W\d+T\d+H\d+|FY\d+|CSPC#|C\d+T\d+H\d+|EC\d+T\d+H\d+|\d+X\d+)\b"
+)
+PACK_FRACTION_RE = re.compile(r"(?<![A-Z0-9])(\d+)\s*/\s*(\d+(?:\.\d+)?)\s*(OZ|LB|LBS|CT)\b")
+HASH_SIZE_RE = re.compile(r"(?<![A-Z0-9])(\d+(?:\.\d+)?)#\b")
+PACK_DASH_RE = re.compile(r"(?<![A-Z0-9])(\d+)\s*-\s*PACK\b")
+PACK_WORD_RE = re.compile(r"(?<![A-Z0-9])(\d+)\s*PACK\b")
+SIZE_RE = re.compile(r"(?<![A-Z0-9])(\d+(?:\.\d+)?)\s*(OZ|LB|LBS|CT|KG|G)\b")
+DISCOUNT_TARGET_RE = re.compile(r"^/\s*(\d+)\b")
+
+
+def clean_costco_name(name):
+    cleaned = normalize_whitespace(name).upper().replace('"', "")
+    cleaned = CODE_TOKEN_RE.sub(" ", cleaned)
+    cleaned = re.sub(r"\s*/\s*\d+(?:\.\d+)?\s*(KG|G)\b", " ", cleaned)
+    cleaned = normalize_whitespace(cleaned)
+    return cleaned
+
+
+def combine_description(item):
+    return normalize_whitespace(
+        " ".join(
+            str(part).strip()
+            for part in [item.get("itemDescription01"), item.get("itemDescription02")]
+            if part
+        )
+    )
+
+
+def parse_costco_size_and_pack(cleaned_name):
+    pack_qty = ""
+    size_value = ""
+    size_unit = ""
+
+    match = PACK_FRACTION_RE.search(cleaned_name)
+    if match:
+        pack_qty = normalize_number(match.group(1))
+        size_value = normalize_number(match.group(2))
+        size_unit = normalize_unit(match.group(3))
+        return size_value, size_unit, pack_qty
+
+    match = HASH_SIZE_RE.search(cleaned_name)
+    if match:
+        size_value = normalize_number(match.group(1))
+        size_unit = "lb"
+
+    match = PACK_DASH_RE.search(cleaned_name) or PACK_WORD_RE.search(cleaned_name)
+    if match:
+        pack_qty = normalize_number(match.group(1))
+
+    matches = list(SIZE_RE.finditer(cleaned_name))
+    if matches:
+        last = matches[-1]
+        unit = last.group(2)
+        size_value = normalize_number(last.group(1))
+        size_unit = "count" if unit == "CT" else normalize_unit(unit)
+
+    return size_value, size_unit, pack_qty
+
+
+def normalize_costco_name(cleaned_name):
+    brand = ""
+    base = cleaned_name
+    if base.startswith("KS "):
+        brand = "KS"
+        base = normalize_whitespace(base[3:])
+
+    size_value, size_unit, pack_qty = parse_costco_size_and_pack(base)
+    if size_value and size_unit:
+        if pack_qty:
+            base = PACK_FRACTION_RE.sub(" ", base)
+        else:
+            base = SIZE_RE.sub(" ", base)
+    base = HASH_SIZE_RE.sub(" ", base)
+    base = PACK_DASH_RE.sub(" ", base)
+    base = PACK_WORD_RE.sub(" ", base)
+    base = normalize_whitespace(base)
+    tokens = []
+    for token in base.split():
+        if token in {"ORG"}:
+            continue
+        if token in {"PEANUT", "BUTTER"} and "JIF" in base:
+            continue
+        tokens.append(token)
+    base = singularize_tokens(" ".join(tokens))
+    return normalize_whitespace(base), brand, size_value, size_unit, pack_qty
+
+
+def guess_measure_type(size_unit, pack_qty, is_discount_line):
+    if is_discount_line:
+        return "each"
+    if size_unit in {"lb", "oz", "g", "kg"}:
+        return "weight"
+    if size_unit in {"ml", "l", "qt", "pt", "gal", "fl_oz"}:
+        return "volume"
+    if size_unit == "count" or pack_qty:
+        return "count"
+    return "each"
+
+
+def derive_costco_prices(item, measure_type, size_value, size_unit, pack_qty):
+    line_total = to_decimal(item.get("amount"))
+    qty = to_decimal(item.get("unit"))
+    parsed_size = to_decimal(size_value)
+    parsed_pack = to_decimal(pack_qty) or 1
+
+    price_per_each = ""
+    price_per_lb = ""
+    price_per_oz = ""
+    if line_total is None:
+        return price_per_each, price_per_lb, price_per_oz
+
+    if measure_type in {"each", "count"} and qty not in (None, 0):
+        price_per_each = format_decimal(line_total / qty)
+
+    if parsed_size not in (None, 0):
+        total_units = parsed_size * parsed_pack * (qty or 1)
+        if size_unit == "lb":
+            per_lb = line_total / total_units
+            price_per_lb = format_decimal(per_lb)
+            price_per_oz = format_decimal(per_lb / 16)
+        elif size_unit == "oz":
+            per_oz = line_total / total_units
+            price_per_oz = format_decimal(per_oz)
+            price_per_lb = format_decimal(per_oz * 16)
+
+    return price_per_each, price_per_lb, price_per_oz
+
+
+def is_discount_item(item):
+    amount = to_decimal(item.get("amount")) or 0
+    unit = to_decimal(item.get("unit")) or 0
+    description = combine_description(item)
+    return amount < 0 or unit < 0 or description.startswith("/")
+
+
+def discount_target_id(raw_name):
+    match = DISCOUNT_TARGET_RE.match(normalize_whitespace(raw_name))
+    if not match:
+        return ""
+    return match.group(1)
+
+
+def parse_costco_item(order_id, order_date, raw_path, line_no, item):
+    raw_name = combine_description(item)
+    cleaned_name = clean_costco_name(raw_name)
+    item_name_norm, brand_guess, size_value, size_unit, pack_qty = normalize_costco_name(
+        cleaned_name
+    )
+    is_discount_line = is_discount_item(item)
+    is_coupon_line = "true" if raw_name.startswith("/") else "false"
+    measure_type = guess_measure_type(size_unit, pack_qty, is_discount_line)
+    price_per_each, price_per_lb, price_per_oz = derive_costco_prices(
+        item, measure_type, size_value, size_unit, pack_qty
+    )
+    normalized_row_id = f"{RETAILER}:{order_id}:{line_no}"
+    normalized_quantity, normalized_quantity_unit = derive_normalized_quantity(
+        size_value,
+        size_unit,
+        pack_qty,
+        measure_type,
+    )
+    identity_key, normalization_basis = normalization_identity(
+        {
+            "retailer": RETAILER,
+            "normalized_row_id": normalized_row_id,
+            "upc": "",
+            "retailer_item_id": str(item.get("itemNumber", "")),
+            "item_name_norm": item_name_norm,
+            "size_value": size_value,
+            "size_unit": size_unit,
+            "pack_qty": pack_qty,
+        }
+    )
+    price_fields = derive_price_fields(
+        price_per_each,
+        price_per_lb,
+        price_per_oz,
+        str(item.get("amount", "")),
+        str(item.get("unit", "")),
+        pack_qty,
+    )
+
+    return {
+        "retailer": RETAILER,
+        "order_id": str(order_id),
+        "line_no": str(line_no),
+        "normalized_row_id": normalized_row_id,
+        "normalized_item_id": f"cnorm:{identity_key}",
+        "normalization_basis": normalization_basis,
+        "observed_item_key": normalized_row_id,
+        "order_date": normalize_whitespace(order_date),
+        "retailer_item_id": str(item.get("itemNumber", "")),
+        "pod_id": "",
+        "item_name": raw_name,
+        "upc": "",
+        "category_id": str(item.get("itemDepartmentNumber", "")),
+        "category": str(item.get("transDepartmentNumber", "")),
+        "qty": str(item.get("unit", "")),
+        "unit": str(item.get("itemIdentifier", "")),
+        "unit_price": str(item.get("itemUnitPriceAmount", "")),
+        "line_total": str(item.get("amount", "")),
+        "picked_weight": "",
+        "mvp_savings": "",
+        "reward_savings": "",
+        "coupon_savings": str(item.get("amount", "")) if is_discount_line else "",
+        "coupon_price": "",
+        "matched_discount_amount": "",
+        "net_line_total": str(item.get("amount", "")) if not is_discount_line else "",
+        "image_url": "",
+        "raw_order_path": raw_path.as_posix(),
+        "item_name_norm": item_name_norm,
+        "brand_guess": brand_guess,
+        "variant": "",
+        "size_value": size_value,
+        "size_unit": size_unit,
+        "pack_qty": pack_qty,
+        "measure_type": measure_type,
+        "normalized_quantity": normalized_quantity,
+        "normalized_quantity_unit": normalized_quantity_unit,
+        "is_store_brand": "true" if brand_guess else "false",
+        "is_item": "false" if is_discount_line else "true",
+        "is_fee": "false",
+        "is_discount_line": "true" if is_discount_line else "false",
+        "is_coupon_line": is_coupon_line,
+        **price_fields,
+        "parse_version": PARSER_VERSION,
+        "parse_notes": "",
+    }
+
+
+def match_costco_discounts(rows):
+    rows_by_order = defaultdict(list)
+    for row in rows:
+        rows_by_order[row["order_id"]].append(row)
+
+    for order_rows in rows_by_order.values():
+        purchase_rows_by_item_id = defaultdict(list)
+        for row in order_rows:
+            if row.get("is_discount_line") == "true":
+                continue
+            retailer_item_id = row.get("retailer_item_id", "")
+            if retailer_item_id:
+                purchase_rows_by_item_id[retailer_item_id].append(row)
+
+        for row in order_rows:
+            if row.get("is_discount_line") != "true":
+                continue
+            target_id = discount_target_id(row.get("item_name", ""))
+            if not target_id:
+                continue
+            matches = purchase_rows_by_item_id.get(target_id, [])
+            if len(matches) != 1:
+                row["parse_notes"] = normalize_whitespace(
+                    f"{row.get('parse_notes', '')};discount_target_unmatched={target_id}"
+                ).strip(";")
+                continue
+
+            purchase_row = matches[0]
+            matched_discount = to_decimal(row.get("line_total"))
+            gross_total = to_decimal(purchase_row.get("line_total"))
+            existing_discount = to_decimal(purchase_row.get("matched_discount_amount")) or 0
+            if matched_discount is None or gross_total is None:
+                continue
+
+            total_discount = existing_discount + matched_discount
+            purchase_row["matched_discount_amount"] = format_decimal(total_discount)
+            purchase_row["net_line_total"] = format_decimal(gross_total + total_discount)
+            purchase_row["parse_notes"] = normalize_whitespace(
+                f"{purchase_row.get('parse_notes', '')};matched_discount={target_id}"
+            ).strip(";")
+            row["parse_notes"] = normalize_whitespace(
+                f"{row.get('parse_notes', '')};matched_to_item={target_id}"
+            ).strip(";")
+
+
+def iter_costco_rows(raw_dir):
+    for path in discover_json_files(raw_dir):
+        if path.name in {"summary.json", "summary_requests.json"}:
+            continue
+        payload = json.loads(path.read_text(encoding="utf-8"))
+        if not isinstance(payload, dict):
+            continue
+        receipts = payload.get("data", {}).get("receiptsWithCounts", {}).get("receipts", [])
+        for receipt in receipts:
+            order_id = receipt["transactionBarcode"]
+            order_date = receipt.get("transactionDate", "")
+            for line_no, item in enumerate(receipt.get("itemArray", []), start=1):
+                yield parse_costco_item(order_id, order_date, path, line_no, item)
+
+
+def discover_json_files(raw_dir):
+    raw_dir = Path(raw_dir)
+    candidates = sorted(raw_dir.glob("*.json"))
+    if candidates:
+        return candidates
+    if raw_dir.name == "raw" and raw_dir.parent.exists():
+        return sorted(raw_dir.parent.glob("*.json"))
+    return []
+
+
+def build_items_enriched(raw_dir):
+    rows = list(iter_costco_rows(raw_dir))
+    match_costco_discounts(rows)
+    rows.sort(key=lambda row: (row["order_date"], row["order_id"], int(row["line_no"])))
+    return rows
+
+
+def write_csv(path, rows):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=OUTPUT_FIELDS)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+@click.command()
+@click.option(
+    "--input-dir",
+    default=str(DEFAULT_INPUT_DIR),
+    show_default=True,
+    help="Directory containing Costco raw order json files.",
+)
+@click.option(
+    "--output-csv",
+    default=str(DEFAULT_OUTPUT_CSV),
+    show_default=True,
+    help="CSV path for enriched Costco item rows.",
+)
+def main(input_dir, output_csv):
+    click.echo("legacy entrypoint: prefer normalize_costco_web.py for data-model outputs")
+    rows = build_items_enriched(Path(input_dir))
+    write_csv(Path(output_csv), rows)
+    click.echo(f"wrote {len(rows)} rows to {output_csv}")
+
+
+if __name__ == "__main__":
+    main()

enrich_giant.py

@@ -0,0 +1,562 @@
+import csv
+import json
+import re
+from decimal import Decimal, InvalidOperation, ROUND_HALF_UP
+from pathlib import Path
+
+import click
+
+
+PARSER_VERSION = "giant-enrich-v1"
+RETAILER = "giant"
+DEFAULT_INPUT_DIR = Path("giant_output/raw")
+DEFAULT_OUTPUT_CSV = Path("giant_output/items_enriched.csv")
+
+OUTPUT_FIELDS = [
+    "retailer",
+    "order_id",
+    "line_no",
+    "normalized_row_id",
+    "normalized_item_id",
+    "normalization_basis",
+    "observed_item_key",
+    "order_date",
+    "retailer_item_id",
+    "pod_id",
+    "item_name",
+    "upc",
+    "category_id",
+    "category",
+    "qty",
+    "unit",
+    "unit_price",
+    "line_total",
+    "picked_weight",
+    "mvp_savings",
+    "reward_savings",
+    "coupon_savings",
+    "coupon_price",
+    "matched_discount_amount",
+    "net_line_total",
+    "image_url",
+    "raw_order_path",
+    "item_name_norm",
+    "brand_guess",
+    "variant",
+    "size_value",
+    "size_unit",
+    "pack_qty",
+    "measure_type",
+    "normalized_quantity",
+    "normalized_quantity_unit",
+    "is_store_brand",
+    "is_item",
+    "is_fee",
+    "is_discount_line",
+    "is_coupon_line",
+    "price_per_each",
+    "price_per_each_basis",
+    "price_per_count",
+    "price_per_count_basis",
+    "price_per_lb",
+    "price_per_lb_basis",
+    "price_per_oz",
+    "price_per_oz_basis",
+    "parse_version",
+    "parse_notes",
+]
+
+STORE_BRAND_PREFIXES = {
+    "SB": "SB",
+    "NP": "NP",
+}
+
+DROP_TOKENS = {"FRESH"}
+
+ABBREVIATIONS = {
+    "APPLE": "APPLE",
+    "APPLES": "APPLES",
+    "APLE": "APPLE",
+    "BASIL": "BASIL",
+    "BLK": "BLACK",
+    "BNLS": "BONELESS",
+    "BRWN": "BROWN",
+    "CARROTS": "CARROTS",
+    "CHDR": "CHEDDAR",
+    "CHICKEN": "CHICKEN",
+    "CHOC": "CHOCOLATE",
+    "CHS": "CHEESE",
+    "CHSE": "CHEESE",
+    "CHZ": "CHEESE",
+    "CILANTRO": "CILANTRO",
+    "CKI": "COOKIE",
+    "CRSHD": "CRUSHED",
+    "FLR": "FLOUR",
+    "FRSH": "FRESH",
+    "GALA": "GALA",
+    "GRAHM": "GRAHAM",
+    "HOT": "HOT",
+    "HRSRDSH": "HORSERADISH",
+    "IMP": "IMPORTED",
+    "IQF": "IQF",
+    "LENTILS": "LENTILS",
+    "LG": "LARGE",
+    "MLK": "MILK",
+    "MSTRD": "MUSTARD",
+    "ONION": "ONION",
+    "ORG": "ORGANIC",
+    "PEPPER": "PEPPER",
+    "PEPPERS": "PEPPERS",
+    "POT": "POTATO",
+    "POTATO": "POTATO",
+    "PPR": "PEPPER",
+    "RICOTTA": "RICOTTA",
+    "ROASTER": "ROASTER",
+    "ROTINI": "ROTINI",
+    "SCE": "SAUCE",
+    "SLC": "SLICED",
+    "SPINCH": "SPINACH",
+    "SPNC": "SPINACH",
+    "SPINACH": "SPINACH",
+    "SQZ": "SQUEEZE",
+    "SWT": "SWEET",
+    "THYME": "THYME",
+    "TOM": "TOMATO",
+    "TOMS": "TOMATOES",
+    "TRTL": "TORTILLA",
+    "VEG": "VEGETABLE",
+    "VINEGAR": "VINEGAR",
+    "WHT": "WHITE",
+    "WHOLE": "WHOLE",
+    "YLW": "YELLOW",
+    "YLWGLD": "YELLOW_GOLD",
+}
+
+FEE_PATTERNS = [
+    re.compile(r"\bBAG CHARGE\b"),
+    re.compile(r"\bDISC AT TOTAL\b"),
+]
+
+SIZE_RE = re.compile(r"(?<![A-Z0-9])(\d+(?:\.\d+)?)(?:\s*)(OZ|Z|LB|LBS|ML|L|FZ|FL OZ|QT|PT|GAL|GA)\b")
+PACK_RE = re.compile(r"(?<![A-Z0-9])(\d+(?:\.\d+)?)(?:\s*)(CT|PK|PKG|PACK)\b")
+
+
+def to_decimal(value):
+    if value in ("", None):
+        return None
+
+    try:
+        return Decimal(str(value))
+    except (InvalidOperation, ValueError):
+        return None
+
+
+def format_decimal(value, places=4):
+    if value is None:
+        return ""
+
+    quant = Decimal("1").scaleb(-places)
+    normalized = value.quantize(quant, rounding=ROUND_HALF_UP).normalize()
+    return format(normalized, "f")
+
+
+def normalize_whitespace(value):
+    return " ".join(str(value or "").strip().split())
+
+
+def clean_item_name(name):
+    cleaned = normalize_whitespace(name).upper()
+    cleaned = re.sub(r"^\+", "", cleaned)
+    cleaned = re.sub(r"^PLU#\d+\s*", "", cleaned)
+    cleaned = cleaned.replace("#", " ")
+    return normalize_whitespace(cleaned)
+
+
+def extract_store_brand_prefix(cleaned_name):
+    for prefix, brand in STORE_BRAND_PREFIXES.items():
+        if cleaned_name == prefix or cleaned_name.startswith(f"{prefix} "):
+            return prefix, brand
+    return "", ""
+
+
+def extract_image_url(item):
+    image = item.get("image")
+    if isinstance(image, dict):
+        for key in ["xlarge", "large", "medium", "small"]:
+            value = image.get(key)
+            if value:
+                return value
+    if isinstance(image, str):
+        return image
+    return ""
+
+
+def parse_size_and_pack(cleaned_name):
+    size_value = ""
+    size_unit = ""
+    pack_qty = ""
+
+    size_matches = list(SIZE_RE.finditer(cleaned_name))
+    if size_matches:
+        match = size_matches[-1]
+        size_value = normalize_number(match.group(1))
+        size_unit = normalize_unit(match.group(2))
+
+    pack_matches = list(PACK_RE.finditer(cleaned_name))
+    if pack_matches:
+        match = pack_matches[-1]
+        pack_qty = normalize_number(match.group(1))
+
+    return size_value, size_unit, pack_qty
+
+
+def normalize_number(value):
+    decimal = to_decimal(value)
+    if decimal is None:
+        return ""
+    return format(decimal.normalize(), "f")
+
+
+def normalize_unit(unit):
+    collapsed = normalize_whitespace(unit).upper()
+    return {
+        "Z": "oz",
+        "OZ": "oz",
+        "FZ": "fl_oz",
+        "FL OZ": "fl_oz",
+        "LB": "lb",
+        "LBS": "lb",
+        "ML": "ml",
+        "L": "l",
+        "QT": "qt",
+        "PT": "pt",
+        "GAL": "gal",
+        "GA": "gal",
+    }.get(collapsed, collapsed.lower())
+
+
+def strip_measure_tokens(cleaned_name):
+    without_sizes = SIZE_RE.sub(" ", cleaned_name)
+    without_measures = PACK_RE.sub(" ", without_sizes)
+    return normalize_whitespace(without_measures)
+
+
+def expand_token(token):
+    return ABBREVIATIONS.get(token, token)
+
+
+def normalize_item_name(cleaned_name):
+    prefix, _brand = extract_store_brand_prefix(cleaned_name)
+    base = cleaned_name
+    if prefix:
+        base = normalize_whitespace(base[len(prefix):])
+
+    base = strip_measure_tokens(base)
+    expanded_tokens = []
+    for token in base.split():
+        expanded = expand_token(token)
+        if expanded in DROP_TOKENS:
+            continue
+        expanded_tokens.append(expanded)
+    expanded = " ".join(token for token in expanded_tokens if token)
+    return singularize_tokens(normalize_whitespace(expanded))
+
+
+def singularize_tokens(text):
+    singular_map = {
+        "APPLES": "APPLE",
+        "BANANAS": "BANANA",
+        "BERRIES": "BERRY",
+        "EGGS": "EGG",
+        "LEMONS": "LEMON",
+        "LIMES": "LIME",
+        "MANDARINS": "MANDARIN",
+        "PEPPERS": "PEPPER",
+        "STRAWBERRIES": "STRAWBERRY",
+    }
+    tokens = [singular_map.get(token, token) for token in text.split()]
+    return normalize_whitespace(" ".join(tokens))
+
+
+def guess_measure_type(item, size_unit, pack_qty):
+    unit = normalize_whitespace(item.get("lbEachCd")).upper()
+    picked_weight = to_decimal(item.get("totalPickedWeight"))
+    qty = to_decimal(item.get("shipQy"))
+
+    if unit == "LB" or (picked_weight is not None and picked_weight > 0 and unit != "EA"):
+        return "weight"
+    if size_unit in {"lb", "oz"}:
+        return "weight"
+    if size_unit in {"ml", "l", "qt", "pt", "gal", "fl_oz"}:
+        return "volume"
+    if pack_qty:
+        return "count"
+    if unit == "EA" or (qty is not None and qty > 0):
+        return "each"
+    return ""
+
+
+def is_fee_item(cleaned_name):
+    return any(pattern.search(cleaned_name) for pattern in FEE_PATTERNS)
+
+
+def derive_prices(item, measure_type, size_value="", size_unit="", pack_qty=""):
+    qty = to_decimal(item.get("shipQy"))
+    line_total = to_decimal(item.get("groceryAmount"))
+    picked_weight = to_decimal(item.get("totalPickedWeight"))
+    parsed_size = to_decimal(size_value)
+    parsed_pack = to_decimal(pack_qty) or Decimal("1")
+
+    price_per_each = ""
+    price_per_lb = ""
+    price_per_oz = ""
+
+    if line_total is None:
+        return price_per_each, price_per_lb, price_per_oz
+
+    if measure_type == "each" and qty not in (None, Decimal("0")):
+        price_per_each = format_decimal(line_total / qty)
+
+    if measure_type == "count" and qty not in (None, Decimal("0")):
+        price_per_each = format_decimal(line_total / qty)
+
+    if measure_type == "weight" and picked_weight not in (None, Decimal("0")):
+        per_lb = line_total / picked_weight
+        price_per_lb = format_decimal(per_lb)
+        price_per_oz = format_decimal(per_lb / Decimal("16"))
+        return price_per_each, price_per_lb, price_per_oz
+
+    if measure_type == "weight" and parsed_size not in (None, Decimal("0")) and qty not in (None, Decimal("0")):
+        total_units = qty * parsed_pack * parsed_size
+        if size_unit == "lb":
+            per_lb = line_total / total_units
+            price_per_lb = format_decimal(per_lb)
+            price_per_oz = format_decimal(per_lb / Decimal("16"))
+        elif size_unit == "oz":
+            per_oz = line_total / total_units
+            price_per_oz = format_decimal(per_oz)
+            price_per_lb = format_decimal(per_oz * Decimal("16"))
+
+    return price_per_each, price_per_lb, price_per_oz
+
+
+def derive_normalized_quantity(size_value, size_unit, pack_qty, measure_type):
+    parsed_size = to_decimal(size_value)
+    parsed_pack = to_decimal(pack_qty) or Decimal("1")
+
+    if parsed_size not in (None, Decimal("0")) and size_unit:
+        return format_decimal(parsed_size * parsed_pack), size_unit
+    if parsed_pack not in (None, Decimal("0")) and measure_type == "count":
+        return format_decimal(parsed_pack), "count"
+    if measure_type == "each":
+        return "1", "each"
+    return "", ""
+
+
+def derive_price_fields(price_per_each, price_per_lb, price_per_oz, line_total, qty, pack_qty):
+    line_total_decimal = to_decimal(line_total)
+    qty_decimal = to_decimal(qty)
+    pack_decimal = to_decimal(pack_qty)
+    price_per_count = ""
+    price_per_count_basis = ""
+    if line_total_decimal is not None and qty_decimal not in (None, Decimal("0")) and pack_decimal not in (
+        None,
+        Decimal("0"),
+    ):
+        price_per_count = format_decimal(line_total_decimal / (qty_decimal * pack_decimal))
+        price_per_count_basis = "line_total_over_pack_qty"
+
+    return {
+        "price_per_each": price_per_each,
+        "price_per_each_basis": "line_total_over_qty" if price_per_each else "",
+        "price_per_count": price_per_count,
+        "price_per_count_basis": price_per_count_basis,
+        "price_per_lb": price_per_lb,
+        "price_per_lb_basis": "parsed_or_picked_weight" if price_per_lb else "",
+        "price_per_oz": price_per_oz,
+        "price_per_oz_basis": "parsed_or_picked_weight" if price_per_oz else "",
+    }
+
+
+def normalization_identity(row):
+    if row.get("upc"):
+        return f"{row['retailer']}|upc={row['upc']}", "exact_upc"
+    if row.get("retailer_item_id"):
+        return f"{row['retailer']}|retailer_item_id={row['retailer_item_id']}", "exact_retailer_item_id"
+    if row.get("item_name_norm"):
+        return (
+            "|".join(
+                [
+                    row["retailer"],
+                    f"name={row['item_name_norm']}",
+                    f"size={row.get('size_value', '')}",
+                    f"unit={row.get('size_unit', '')}",
+                    f"pack={row.get('pack_qty', '')}",
+                ]
+            ),
+            "exact_name_size_pack",
+        )
+    return row["normalized_row_id"], "row_identity"
+
+
+def parse_item(order_id, order_date, raw_path, line_no, item):
+    cleaned_name = clean_item_name(item.get("itemName", ""))
+    size_value, size_unit, pack_qty = parse_size_and_pack(cleaned_name)
+    prefix, brand_guess = extract_store_brand_prefix(cleaned_name)
+    normalized_name = normalize_item_name(cleaned_name)
+    measure_type = guess_measure_type(item, size_unit, pack_qty)
+    price_per_each, price_per_lb, price_per_oz = derive_prices(
+        item,
+        measure_type,
+        size_value=size_value,
+        size_unit=size_unit,
+        pack_qty=pack_qty,
+    )
+    is_fee = is_fee_item(cleaned_name)
+    parse_notes = []
+
+    if prefix:
+        parse_notes.append(f"store_brand_prefix={prefix}")
+    if is_fee:
+        parse_notes.append("fee_item")
+    if size_value and not size_unit:
+        parse_notes.append("size_without_unit")
+
+    normalized_row_id = f"{RETAILER}:{order_id}:{line_no}"
+    normalized_quantity, normalized_quantity_unit = derive_normalized_quantity(
+        size_value,
+        size_unit,
+        pack_qty,
+        measure_type,
+    )
+    identity_key, normalization_basis = normalization_identity(
+        {
+            "retailer": RETAILER,
+            "normalized_row_id": normalized_row_id,
+            "upc": stringify(item.get("primUpcCd")),
+            "retailer_item_id": stringify(item.get("podId")),
+            "item_name_norm": normalized_name,
+            "size_value": size_value,
+            "size_unit": size_unit,
+            "pack_qty": pack_qty,
+        }
+    )
+    price_fields = derive_price_fields(
+        price_per_each,
+        price_per_lb,
+        price_per_oz,
+        stringify(item.get("groceryAmount")),
+        stringify(item.get("shipQy")),
+        pack_qty,
+    )
+
+    return {
+        "retailer": RETAILER,
+        "order_id": str(order_id),
+        "line_no": str(line_no),
+        "normalized_row_id": normalized_row_id,
+        "normalized_item_id": f"gnorm:{identity_key}",
+        "normalization_basis": normalization_basis,
+        "observed_item_key": normalized_row_id,
+        "order_date": normalize_whitespace(order_date),
+        "retailer_item_id": stringify(item.get("podId")),
+        "pod_id": stringify(item.get("podId")),
+        "item_name": stringify(item.get("itemName")),
+        "upc": stringify(item.get("primUpcCd")),
+        "category_id": stringify(item.get("categoryId")),
+        "category": stringify(item.get("categoryDesc")),
+        "qty": stringify(item.get("shipQy")),
+        "unit": stringify(item.get("lbEachCd")),
+        "unit_price": stringify(item.get("unitPrice")),
+        "line_total": stringify(item.get("groceryAmount")),
+        "picked_weight": stringify(item.get("totalPickedWeight")),
+        "mvp_savings": stringify(item.get("mvpSavings")),
+        "reward_savings": stringify(item.get("rewardSavings")),
+        "coupon_savings": stringify(item.get("couponSavings")),
+        "coupon_price": stringify(item.get("couponPrice")),
+        "matched_discount_amount": "",
+        "net_line_total": stringify(item.get("totalPrice")),
+        "image_url": extract_image_url(item),
+        "raw_order_path": raw_path.as_posix(),
+        "item_name_norm": normalized_name,
+        "brand_guess": brand_guess,
+        "variant": "",
+        "size_value": size_value,
+        "size_unit": size_unit,
+        "pack_qty": pack_qty,
+        "measure_type": measure_type,
+        "normalized_quantity": normalized_quantity,
+        "normalized_quantity_unit": normalized_quantity_unit,
+        "is_store_brand": "true" if bool(prefix) else "false",
+        "is_item": "false" if is_fee else "true",
+        "is_fee": "true" if is_fee else "false",
+        "is_discount_line": "false",
+        "is_coupon_line": "false",
+        **price_fields,
+        "parse_version": PARSER_VERSION,
+        "parse_notes": ";".join(parse_notes),
+    }
+
+
+def stringify(value):
+    if value is None:
+        return ""
+    return str(value)
+
+
+def iter_order_rows(raw_dir):
+    for path in sorted(raw_dir.glob("*.json")):
+        if path.name == "history.json":
+            continue
+
+        payload = json.loads(path.read_text(encoding="utf-8"))
+        order_id = payload.get("orderId", path.stem)
+        order_date = payload.get("orderDate", "")
+
+        for line_no, item in enumerate(payload.get("items", []), start=1):
+            yield parse_item(order_id, order_date, path, line_no, item)
+
+
+def build_items_enriched(raw_dir):
+    rows = list(iter_order_rows(raw_dir))
+    rows.sort(key=lambda row: (row["order_date"], row["order_id"], int(row["line_no"])))
+    return rows
+
+
+def write_csv(path, rows):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=OUTPUT_FIELDS)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+@click.command()
+@click.option(
+    "--input-dir",
+    default=str(DEFAULT_INPUT_DIR),
+    show_default=True,
+    help="Directory containing Giant raw order json files.",
+)
+@click.option(
+    "--output-csv",
+    default=str(DEFAULT_OUTPUT_CSV),
+    show_default=True,
+    help="CSV path for enriched Giant item rows.",
+)
+def main(input_dir, output_csv):
+    click.echo("legacy entrypoint: prefer normalize_giant_web.py for data-model outputs")
+    raw_dir = Path(input_dir)
+    output_path = Path(output_csv)
+
+    if not raw_dir.exists():
+        raise click.ClickException(f"input dir does not exist: {raw_dir}")
+
+    rows = build_items_enriched(raw_dir)
+    write_csv(output_path, rows)
+
+    click.echo(f"wrote {len(rows)} rows to {output_path}")
+
+
+if __name__ == "__main__":
+    main()

layer_helpers.py

@@ -0,0 +1,54 @@
+import csv
+import hashlib
+from collections import Counter
+from pathlib import Path
+
+
+def read_csv_rows(path):
+    path = Path(path)
+    with path.open(newline="", encoding="utf-8") as handle:
+        return list(csv.DictReader(handle))
+
+
+def write_csv_rows(path, rows, fieldnames):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def stable_id(prefix, raw_key):
+    digest = hashlib.sha1(str(raw_key).encode("utf-8")).hexdigest()[:12]
+    return f"{prefix}_{digest}"
+
+
+def first_nonblank(rows, field):
+    for row in rows:
+        value = row.get(field, "")
+        if value:
+            return value
+    return ""
+
+
+def representative_value(rows, field):
+    values = [row.get(field, "") for row in rows if row.get(field, "")]
+    if not values:
+        return ""
+    counts = Counter(values)
+    return sorted(counts.items(), key=lambda item: (-item[1], item[0]))[0][0]
+
+
+def distinct_values(rows, field):
+    return sorted({row.get(field, "") for row in rows if row.get(field, "")})
+
+
+def compact_join(values, limit=3):
+    unique = []
+    seen = set()
+    for value in values:
+        if value and value not in seen:
+            seen.add(value)
+            unique.append(value)
+    return " | ".join(unique[:limit])

normalize_costco_web.py

@@ -0,0 +1,28 @@
+from pathlib import Path
+
+import click
+
+import enrich_costco
+
+
+@click.command()
+@click.option(
+    "--input-dir",
+    default="data/costco-web/raw",
+    show_default=True,
+    help="Directory containing Costco raw order json files.",
+)
+@click.option(
+    "--output-csv",
+    default="data/costco-web/normalized_items.csv",
+    show_default=True,
+    help="CSV path for normalized Costco item rows.",
+)
+def main(input_dir, output_csv):
+    rows = enrich_costco.build_items_enriched(Path(input_dir))
+    enrich_costco.write_csv(Path(output_csv), rows)
+    click.echo(f"wrote {len(rows)} rows to {output_csv}")
+
+
+if __name__ == "__main__":
+    main()

normalize_giant_web.py

@@ -0,0 +1,28 @@
+from pathlib import Path
+
+import click
+
+import enrich_giant
+
+
+@click.command()
+@click.option(
+    "--input-dir",
+    default="data/giant-web/raw",
+    show_default=True,
+    help="Directory containing Giant raw order json files.",
+)
+@click.option(
+    "--output-csv",
+    default="data/giant-web/normalized_items.csv",
+    show_default=True,
+    help="CSV path for normalized Giant item rows.",
+)
+def main(input_dir, output_csv):
+    rows = enrich_giant.build_items_enriched(Path(input_dir))
+    enrich_giant.write_csv(Path(output_csv), rows)
+    click.echo(f"wrote {len(rows)} rows to {output_csv}")
+
+
+if __name__ == "__main__":
+    main()

pm/data-model.org

@@ -0,0 +1,346 @@
+* Grocery data model and file layout
+
+This document defines the shared file layout and stable CSV schemas for the
+grocery pipeline.
+Goals:
+- Ensure data gathering is separate from analysis
+- Enable multiple data gathering methods
+- One layer for review and analysis  
+
+** Design Rules
+- Raw retailer exports remain the source of truth.
+- Retailer parsing is isolated to retailer-specific files and ids.
+- Cross-retailer product layers begin only after retailer-specific normalization.
+- CSV schemas are stable and additive: new columns may be appended, but
+   existing columns should not be repurposed.
+- Unknown values should be left blank rather than guessed.
+
+*** Retailer-specific data:
+- raw json payloads
+- retailer order ids
+- retailer line numbers
+- retailer category ids and names
+- retailer item names
+- retailer image urls
+- comparison-ready normalized quantity basis fields
+  
+*** Review/Combined data:
+- catalog of reviewed products
+- links from normalized retailer items to catalog
+- human review state for unresolved cases
+
+
+* Pipeline
+Each step can be run alone if its dependents exist.
+Each retail provider script must produce deterministic line-item outputs, and
+normalization may assign within-retailer product identity only when the
+retailer itself provides strong evidence.
+
+Key: 
+- (1) input
+- [1] output
+ 
+** 1. Collect
+Get raw receipt/visit and item data from a retailer.
+Scraping is unique to a Retailer and method (e.g., Giant-Web and Giant-Scan).
+Preserve complete raw data and preserve fidelity.
+Avoid interpretation beyond basic data flattening.
+ - (1) Source access (Varies, eg header data, auth for API access)
+ - [1] collected visits from each retailer
+ - [2] collected items from each retailer
+ - [3] any other raw data that supports [1] and [2]; explicit source (eventual receipt scan?)
+   
+** 2. Normalize
+Parse and extract structured facts from retailer-specific raw data
+  to create a standardized item format for that retailer.
+Strictly dependent on Collect method and output.
+ - Extract quantity, size, pack, pricing, variant
+ - Add discount line items to product line items using upc/retail_item_id and concurrence
+ - Cleanup naming to facilitate later matching
+ - Assign retailer-level `normalized_item_id` only when evidence is deterministic
+ - Never use fuzzy or semantic matching here
+ - (1) collected items from each retailer
+ - (2) collected visits from each retailer
+ - [1] normalized items from each retailer
+
+** 3. Review/Combine (Canonicalization)
+Decide whether two normalized retailer items are "the same product";
+ match items across retailers using algo/logic and human review.
+Create catalog linked to normalized retailer items.
+ - Review operates on distinct `normalized_item_id` values, not individual purchase rows
+ - Cross-retailer identity decisions happen only here
+ - Asking human to create a canonical/catalog item with:
+   - friendly/catalog_name: "bell pepper"; "milk"
+   - category: "produce"; "dairy"
+   - product_type: "pepper"; "milk"
+   - ? variant? "whole, "skim", "2pct"
+ - Then link the group of items to that catalog item.
+ - (1) normalized items from each retailer
+ - [1] review queue of items to be reviewed
+ - [2] catalog (lookup table) of confirmed normalized retailer items and catalog_id
+ - [3] purchase list of normalized items , pivot-ready
+   
+** Unresolved Issues
+1. need central script to orchestrate; metadata belongs there and nowhere else
+2. `LIME` and `LIME . / .` appearing in the catalog: names must come from review-approved names, not raw strings
+
+
+* Directory Layout
+Use one top-level data root:
+#+begin_example
+main.py
+collect_<retailer>_<method>.py
+normalize_<retailer>_<method>.py
+review.py
+data/
+  <retailer-method>/
+    raw/  # unmodified retailer payloads exactly as fetched
+      <order_id.json> 
+    collected_items.csv # one row per retailer line item w/ retailer-native values
+    collected_orders.csv # one row per receipt/visit, flattened from raw order data
+    normalized_items.csv # parsed retailer-specific line items with normalized fields
+  costco-web/ # sample
+    raw/
+      orders/
+        history.json
+        <order_id>.json
+    collected_items.csv
+    collected_orders.csv
+    normalized_items.csv
+  review/
+    review_queue.csv # Human review queue for unresolved matching/parsing cases.
+    product_links.csv # Links from normalized retailer items to catalog items.
+  catalog.csv  # Cross-retailer product catalog entities used for comparison.
+  purchases.csv
+#+end_example
+
+Notes:
+- The current repo still uses transitional root-level scripts and output folders.
+- This layout is the target structure for the refactor, not a claim that migration is already complete.
+
+* Schemas
+** `data/<retailer-method>/collected_items.csv`
+One row per retailer line item.
+| key                | definition                                 |
+|--------------------+--------------------------------------------|
+| `retailer` PK      | retailer slug                              |
+| `order_id` PK      | retailer order id                          |
+| `line_no`  PK      | stable line number within order export     |
+| `order_date`       | copied from order when available           |
+| `retailer_item_id` | retailer-native item id when available     |
+| `pod_id`           | retailer pod/item id                       |
+| `item_name`        | raw retailer item name                     |
+| `upc`              | retailer UPC or PLU value                  |
+| `category_id`      | retailer category id                       |
+| `category`         | retailer category description              |
+| `qty`              | retailer quantity field                    |
+| `unit`             | retailer unit code such as `EA` or `LB`    |
+| `unit_price`       | retailer unit price field                  |
+| `line_total`       | retailer extended price field              |
+| `picked_weight`    | retailer picked weight field               |
+| `mvp_savings`      | retailer savings field                     |
+| `reward_savings`   | retailer rewards savings field             |
+| `coupon_savings`   | retailer coupon savings field              |
+| `coupon_price`     | retailer coupon price field                |
+| `image_url`        | raw retailer image url when present        |
+| `raw_order_path`   | relative path to source order payload      |
+| `is_discount_line` | retailer adjustment or discount-line flag  |
+| `is_coupon_line`   | coupon-like line flag when distinguishable |
+
+** `data/<retailer-method>/collected_orders.csv`
+One row per order/visit/receipt.
+| key                       | definition                                      |
+|---------------------------+-------------------------------------------------|
+| `retailer` PK             | retailer slug such as `giant`                   |
+| `order_id` PK             | retailer order or visit id                      |
+| `order_date`              | order date in `YYYY-MM-DD` when available       |
+| `delivery_date`           | fulfillment date in `YYYY-MM-DD` when available |
+| `service_type`            | retailer service type such as `INSTORE`         |
+| `order_total`             | order total as provided by retailer             |
+| `payment_method`          | retailer payment label                          |
+| `total_item_count`        | total line count or item count from retailer    |
+| `total_savings`           | total savings as provided by retailer           |
+| `your_savings_total`      | savings field from retailer when present        |
+| `coupons_discounts_total` | coupon/discount total from retailer             |
+| `store_name`              | retailer store name                             |
+| `store_number`            | retailer store number                           |
+| `store_address1`          | street address                                  |
+| `store_city`              | city                                            |
+| `store_state`             | state or province                               |
+| `store_zipcode`           | postal code                                     |
+| `refund_order`            | retailer refund flag                            |
+| `ebt_order`               | retailer EBT flag                               |
+| `raw_history_path`        | relative path to source history payload         |
+| `raw_order_path`          | relative path to source order payload           |
+
+** `data/<retailer-method>/normalized_items.csv`
+One row per retailer line item after deterministic parsing. Preserve raw
+fields from `collected_items.csv` and add parsed fields that make later review
+and grouping easier. Normalization may assign retailer-level identity when the
+evidence is deterministic and retailer-scoped.
+
+| key                        | definition                                                       |
+|----------------------------+------------------------------------------------------------------|
+| `retailer` PK              | retailer slug                                                    |
+| `order_id` PK              | retailer order id                                                |
+| `line_no` PK               | line number within order                                         |
+| `normalized_row_id`        | stable row key, typically `<retailer>:<order_id>:<line_no>`      |
+| `normalized_item_id`       | stable retailer-level item identity when deterministic grouping is supported |
+| `normalization_basis`      | basis used to assign `normalized_item_id`                        |
+| `retailer_item_id`         | retailer-native item id                                          |
+| `item_name`                | raw retailer item name                                           |
+| `item_name_norm`           | normalized retailer item name                                    |
+| `brand_guess`              | parsed brand guess                                               |
+| `variant`                  | parsed variant text                                              |
+| `size_value`               | parsed numeric size value                                        |
+| `size_unit`                | parsed size unit such as `oz`, `lb`, `fl_oz`                     |
+| `pack_qty`                 | parsed pack or count guess                                       |
+| `measure_type`             | `each`, `weight`, `volume`, `count`, or blank                    |
+| `normalized_quantity`      | numeric comparison basis derived during normalization            |
+| `normalized_quantity_unit` | basis unit such as `oz`, `lb`, `count`, or blank                 |
+| `is_item`                  | item flag                                                        |
+| `is_store_brand`           | store-brand guess                                                |
+| `is_fee`                   | fee or non-product flag                                          |
+| `is_discount_line`         | discount or adjustment-line flag                                 |
+| `is_coupon_line`           | coupon-like line flag                                            |
+| `matched_discount_amount`  | matched discount value carried onto purchased row when supported |
+| `net_line_total`           | line total after matched discount when supported                 |
+| `price_per_each`           | derived per-each price when supported                            |
+| `price_per_each_basis`     | source basis for `price_per_each`                                |
+| `price_per_count`          | derived per-count price when supported                           |
+| `price_per_count_basis`    | source basis for `price_per_count`                               |
+| `price_per_lb`             | derived per-pound price when supported                           |
+| `price_per_lb_basis`       | source basis for `price_per_lb`                                  |
+| `price_per_oz`             | derived per-ounce price when supported                           |
+| `price_per_oz_basis`       | source basis for `price_per_oz`                                  |
+| `image_url`                | best available retailer image url                                |
+| `raw_order_path`           | relative path to source order payload                            |
+| `parse_version`            | parser version string for reruns                                 |
+| `parse_notes`              | optional non-fatal parser notes                                  |
+
+Notes:
+- `normalized_row_id` identifies the purchase row; `normalized_item_id` identifies a repeated retailer item when strong retailer evidence supports grouping.
+- Valid `normalization_basis` values should be explicit, e.g. `exact_upc`, `exact_retailer_item_id`, `exact_name_size_pack`, or `approved_retailer_alias`.
+- Do not use fuzzy or semantic matching to assign `normalized_item_id`.
+- Discount/coupon rows may remain as standalone normalized rows for auditability even when their amounts are attached to a purchased row via `matched_discount_amount`.
+- Cross-retailer identity is handled later in review/combine via `catalog.csv` and `product_links.csv`.
+
+** `data/review/product_links.csv`
+One row per review-approved link from a normalized retailer item to a catalog item.
+Many normalized retailer items may link to the same catalog item.
+
+| key                     | definition                                  |
+|-------------------------+---------------------------------------------|
+| `normalized_item_id` PK | normalized retailer item id                 |
+| `catalog_id` PK         | linked catalog product id                   |
+| `link_method`           | `manual`, `exact_upc`, `exact_name_size`, etc. |
+| `link_confidence`       | optional confidence label                   |
+| `review_status`         | `pending`, `approved`, `rejected`, or blank |
+| `reviewed_by`           | reviewer id or initials                     |
+| `reviewed_at`           | review timestamp or date                    |
+| `link_notes`            | optional notes                              |
+
+** `data/review/review_queue.csv`
+One row per issue needing human review.
+
+| key                  | definition                                          |
+|----------------------+-----------------------------------------------------|
+| `review_id` PK       | stable review row id                                |
+| `queue_type`         | `link_candidate`, `parse_issue`, `catalog_cleanup`  |
+| `retailer`           | retailer slug when applicable                       |
+| `normalized_item_id` | normalized retailer item id when review is item-level |
+| `normalized_row_id`  | normalized row id when review is row-specific       |
+| `catalog_id`         | candidate canonical id                              |
+| `reason_code`        | machine-readable review reason                      |
+| `priority`           | optional priority label                             |
+| `raw_item_names`     | compact list of example raw names                   |
+| `normalized_names`   | compact list of example normalized names            |
+| `upc`                | example UPC/PLU                                     |
+| `image_url`          | example image url                                   |
+| `example_prices`     | compact list of example prices                      |
+| `seen_count`         | count of related rows                               |
+| `status`             | `pending`, `approved`, `rejected`, `deferred`       |
+| `resolution_notes`   | reviewer notes                                      |
+| `created_at`         | creation timestamp or date                          |
+| `updated_at`         | last update timestamp or date                       |
+** `data/catalog.csv`
+One row per cross-retailer catalog product.
+| key                        | definition                             |
+|----------------------------+----------------------------------------|
+| `catalog_id` PK            | stable catalog product id              |
+| `catalog_name`             | human-reviewed product name            |
+| `product_type`             | generic product eg `apple`, `milk`     |
+| `category`                 | broad section eg `produce`, `dairy`    |
+| `brand`                    | canonical brand when applicable        |
+| `variant`                  | canonical variant                      |
+| `size_value`               | normalized size value                  |
+| `size_unit`                | normalized size unit                   |
+| `pack_qty`                 | normalized pack/count                  |
+| `measure_type`             | normalized measure type                |
+| `normalized_quantity`      | numeric comparison basis value         |
+| `normalized_quantity_unit` | basis unit such as `oz`, `lb`, `count` |
+| `notes`                    | optional human notes                   |
+| `created_at`               | creation timestamp or date             |
+| `updated_at`               | last update timestamp or date          |
+
+Notes:
+- Do not auto-create new catalog rows from weak normalized names alone.
+- Do not encode packaging/count into `catalog_name` unless it is essential to product identity.
+- `catalog_name` should come from review-approved naming, not raw retailer strings.
+
+** `data/purchases.csv`
+One row per purchased item (i.e., `is_item`==true from normalized layer), with
+catalog attributes denormalized in and discounts already applied.
+
+| key                        | definition                                                     |
+|----------------------------+----------------------------------------------------------------|
+| `purchase_date`            | date of purchase (from order)                                  |
+| `retailer`                 | retailer slug                                                  |
+| `order_id`                 | retailer order id                                              |
+| `line_no`                  | line number within order                                       |
+| `normalized_row_id`        | `<retailer>:<order_id>:<line_no>`                              |
+| `normalized_item_id`       | retailer-level normalized item identity                        |
+| `catalog_id`               | linked catalog product id                                      |
+| `catalog_name`             | catalog product name for analysis                              |
+| `catalog_product_type`     | broader product family (e.g., `egg`, `milk`)                   |
+| `catalog_category`         | category such as `produce`, `dairy`                            |
+| `catalog_brand`            | canonical brand when applicable                                |
+| `catalog_variant`          | canonical variant when applicable                              |
+| `raw_item_name`            | original retailer item name                                    |
+| `normalized_item_name`     | cleaned/normalized retailer item name                          |
+| `retailer_item_id`         | retailer-native item id                                        |
+| `upc`                      | UPC/PLU when available                                         |
+| `qty`                      | retailer quantity field                                        |
+| `unit`                     | retailer unit (e.g., `EA`, `LB`)                               |
+| `pack_qty`                 | parsed pack/count                                              |
+| `size_value`               | parsed size value                                              |
+| `size_unit`                | parsed size unit                                               |
+| `measure_type`             | `each`, `weight`, `volume`, `count`                            |
+| `normalized_quantity`      | normalized comparison quantity                                 |
+| `normalized_quantity_unit` | unit for normalized quantity                                   |
+| `unit_price`               | retailer unit price                                            |
+| `line_total`               | original retailer extended price (pre-discount)                |
+| `matched_discount_amount`  | discount amount matched from discount lines                    |
+| `net_line_total`           | effective price after discount (`line_total` + discounts)      |
+| `store_name`               | retailer store name                                            |
+| `store_city`               | store city                                                     |
+| `store_state`              | store state                                                    |
+| `price_per_each`           | derived per-each price                                         |
+| `price_per_each_basis`     | source basis for per-each calc                                 |
+| `price_per_count`          | derived per-count price                                        |
+| `price_per_count_basis`    | source basis for per-count calc                                |
+| `price_per_lb`             | derived per-pound price                                        |
+| `price_per_lb_basis`       | source basis for per-pound calc                                |
+| `price_per_oz`             | derived per-ounce price                                        |
+| `price_per_oz_basis`       | source basis for per-ounce calc                                |
+| `is_fee`                   | true if row represents non-product fee                         |
+| `raw_order_path`           | relative path to original order payload                        |
+
+Notes:
+- Only rows that represent purchased items should appear here.
+- `line_total` preserves retailer truth; `net_line_total` is what you actually paid.
+- catalog fields are denormalized in to make pivoting trivial.
+- no discount/coupon rows exist here; their effects are carried via `matched_discount_amount`.
+- review/link decisions should apply at the `normalized_item_id` level, then fan out to all purchase rows sharing that id.
+
+* /

pm/review-workflow.org

@@ -0,0 +1,73 @@
+* review and item-resolution workflow
+
+This document defines the durable review workflow for unresolved observed
+products.
+
+** persistent files
+
+- `combined_output/purchases.csv`
+  Flat normalized purchase log. This is the review input because it retains:
+  - raw item name
+  - normalized item name
+  - observed product id
+  - canonical product id when resolved
+  - retailer/order/date/price context
+- `combined_output/review_queue.csv`
+  Current unresolved observed products grouped for review.
+- `combined_output/review_resolutions.csv`
+  Durable mapping decisions from observed products to canonical products.
+- `combined_output/canonical_catalog.csv`
+  Durable canonical item catalog used by manual review and later purchase-log
+  rebuilds.
+
+There is no separate alias file in v1. `review_resolutions.csv` is the mapping
+layer from observed products to canonical product ids.
+
+** workflow
+
+1. Run `build_purchases.py`
+   This refreshes the purchase log and seeds/updates the canonical catalog from
+   current auto-linked canonical rows.
+2. Run `review_products.py`
+   This rebuilds `review_queue.csv` from unresolved purchase rows and prompts in
+   the terminal for one observed product at a time.
+3. Choose one of:
+   - link to existing canonical
+   - create new canonical
+   - exclude
+   - skip
+4. `review_products.py` writes decisions immediately to:
+   - `review_resolutions.csv`
+   - `canonical_catalog.csv` when a new canonical item is created
+5. Rerun `build_purchases.py`
+   This reapplies approved resolutions so the final normalized purchase log now
+   carries the reviewed `canonical_product_id`.
+
+** what the human edits
+
+The primary interface is terminal prompts in `review_products.py`.
+
+The human provides:
+- existing canonical id when linking
+- canonical name/category/product type when creating a new canonical item
+- optional resolution notes
+
+The generated CSVs remain editable by hand if needed, but the intended workflow
+is terminal-first.
+
+** durability
+
+- Resolutions are keyed by `observed_product_id`, not by one-off text
+  substitution.
+- Canonical products are keyed by stable `canonical_product_id`.
+- Future runs reuse approved mappings through `review_resolutions.csv`.
+
+** retention of audit fields
+
+The final `purchases.csv` retains:
+- `raw_item_name`
+- `normalized_item_name`
+- `canonical_product_id`
+
+This preserves the raw receipt description, the deterministic parser output, and
+the human-approved canonical identity in one flat purchase log.

pm/scrape-giant.org

@@ -1,107 +0,0 @@
-* python setup
-venv install playwright, pandas
-playwright install
-1. scrape - raw giant json
-2. enrich -
-   cols:
-item_name_norm
-brand_guess
-size_value
-size_unit
-pack_qty
-variant
-is_store_brand
-is_fee
-measure_type
-price_per_lb
-price_per_oz
-price_per_each
-image_url
-
-normalize abbreviationsta
-extract size like 12z, 10ct, 5lb
-detect fees like bag charges
-infer whether something is sold by each vs weight
-carry forward image url   
-
-3. build observed-product atble from enriched items
-   
-
-* item:
-get:
-  /api/v6.0/user/369513017/order/history/detail/69a2e44a16be1142e74ad3cc
-
-headers:
-  request:
-GET /api/v6.0/user/369513017/order/history/detail/69a2e44a16be1142e74ad3cc?isInStore=true HTTP/2
-Host: giantfood.com
-User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:148.0) Gecko/20100101 Firefox/148.0
-Accept: application/json, text/plain, */*
-Accept-Language: en-US,en;q=0.9
-Accept-Encoding: gzip, deflate, br, zstd
-DNT: 1
-Sec-GPC: 1
-Connection: keep-alive
-Referer: https://giantfood.com/account/history/invoice/in-store
-Cookie: datadome=rDtvd3J2hO5AeghJMSFRRxGc6ifKCQYgMLcqPNr9rWiz2rdcXb032AY6GIZn8tUmYB96BKKbzh3_jSjEzYWLj8hDjl3oGYYAiu4jwdaxpf3vh2v4f7KH7kbqgsMWpkjt; cf_clearance=WEPyQokx9f0qoyS4Svsw4EkZ1TYOxjOwcUHspT3.rXw-1773348940-1.2.1.1-fPvERGxBlFUaBW83sUppbUWpwvFG7mZivag5vBvZb3kxUQv2WSVIV1tON0HV2n8bkVY0U8_BBl62a00Np.oJylYQcGME540gZlYEoL.gMs4WynLqApFe5BOXAEwOm01_6h6b62H90bl4ypRehVb_TXEi4qHaPLVSZhjZK_h.fv6RBqjgYch2j_8XnHe5HXvLziVjl1k2aJskozqy04KOyeHyc3OyIPTZd5On_KAzFIM; dvrctk=MnjKJVShVraEtbrBkkxWxLaZrXnIGNQlwB7QtZVPFeA=; __cflb=0H28vXMLFyydRmDMNgcPHijM6auXkCspCkuh58tVuJ3; __cf_bm=C6QbqiEvbbwdrYBpoJOkcWcedf60vcOfPfTPPbZzKbM-1773348202-1.0.1.1-cSHoYwi8ZjIHTdBItXQP_iXJdRJS6FYjFsGdl1eGHvS5pgfbcT4Lg19P6UStX.bZz1u0OXiS5ykdipPBtwP6OvZr68k4XSmjYpir05jNLhw; _dd_s=rum=0&expire=1773349846445; ppdtk=Uog72CR22mD85C7U4iZHlgOQeRmvHEYp0OdQc+0lEes1c5/LeqGT+ZUlXpSC6FpW; cartId=3820547
-Sec-Fetch-Dest: empty
-Sec-Fetch-Mode: cors
-Sec-Fetch-Site: same-origin
-Priority: u=0
-TE: trailers
-
-  response:
-HTTP/2 200 
-date: Thu, 12 Mar 2026 20:55:47 GMT
-content-type: application/json
-server: cloudflare
-cf-ray: 9db5b3a5d84aff28-IAD
-cf-cache-status: DYNAMIC
-content-encoding: gzip
-set-cookie: datadome=MXMri0hss6PlQ0_oS7gG2iMdOKnNkbDmGvOxelgN~nCcupgkJQOqjcjcgdprIaI7hSlt_w8E9Ri_RAzPFrGqtUfqAJ_szB_aNZ2FdC26qmI3870Nn4~T0vtx8Gj3dEZR; Max-Age=31536000; Domain=.giantfood.com; Path=/; Secure; SameSite=Lax
-strict-transport-security: max-age=31536000; includeSubDomains
-vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, accept-encoding
-accept-ch: Sec-CH-UA,Sec-CH-UA-Mobile,Sec-CH-UA-Platform,Sec-CH-UA-Arch,Sec-CH-UA-Full-Version-List,Sec-CH-UA-Model,Sec-CH-Device-Memory
-x-datadome: protected
-request-context: appId=cid-v1:75750625-0c81-4f08-9f5d-ce4f73198e54
-X-Firefox-Spdy: h2
-
-* history:
-GET
-  https://giantfood.com/api/v6.0/user/369513017/order/history?filter=instore&loyaltyNumber=440155630880
-
-headers:
-  request:
-GET /api/v6.0/user/369513017/order/history?filter=instore&loyaltyNumber=440155630880 HTTP/2
-Host: giantfood.com
-User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:148.0) Gecko/20100101 Firefox/148.0
-Accept: application/json, text/plain, */*
-Accept-Language: en-US,en;q=0.9
-Accept-Encoding: gzip, deflate, br, zstd
-DNT: 1
-Sec-GPC: 1
-Connection: keep-alive
-Referer: https://giantfood.com/account/history/invoice/in-store
-Cookie: datadome=OH2XjtCoI6XjE3Qsz_b0F1YULKLatAC0Ea~VMeDGBP0N9Z~CeI3RqEbvkGmNW_VCOU~vRb6p0kqibvF2tLbWnzyAGIdO7jsC41KiYbp7USpJDnefZhIg0e1ypAugvDSw; cf_clearance=WEPyQokx9f0qoyS4Svsw4EkZ1TYOxjOwcUHspT3.rXw-1773348940-1.2.1.1-fPvERGxBlFUaBW83sUppbUWpwvFG7mZivag5vBvZb3kxUQv2WSVIV1tON0HV2n8bkVY0U8_BBl62a00Np.oJylYQcGME540gZlYEoL.gMs4WynLqApFe5BOXAEwOm01_6h6b62H90bl4ypRehVb_TXEi4qHaPLVSZhjZK_h.fv6RBqjgYch2j_8XnHe5HXvLziVjl1k2aJskozqy04KOyeHyc3OyIPTZd5On_KAzFIM; dvrctk=MnjKJVShVraEtbrBkkxWxLaZrXnIGNQlwB7QtZVPFeA=; __cflb=0H28vXMLFyydRmDMNgcPHijM6auXkCspCkuh58tVuJ3; __cf_bm=C6QbqiEvbbwdrYBpoJOkcWcedf60vcOfPfTPPbZzKbM-1773348202-1.0.1.1-cSHoYwi8ZjIHTdBItXQP_iXJdRJS6FYjFsGdl1eGHvS5pgfbcT4Lg19P6UStX.bZz1u0OXiS5ykdipPBtwP6OvZr68k4XSmjYpir05jNLhw; _dd_s=rum=0&expire=1773349842848; ppdtk=Uog72CR22mD85C7U4iZHlgOQeRmvHEYp0OdQc+0lEes1c5/LeqGT+ZUlXpSC6FpW; cartId=3820547
-Sec-Fetch-Dest: empty
-Sec-Fetch-Mode: cors
-Sec-Fetch-Site: same-origin
-Priority: u=0
-TE: trailers
-  
-
-  response:
-  HTTP/2 200 
-date: Thu, 12 Mar 2026 20:55:43 GMT
-content-type: application/json
-server: cloudflare
-cf-ray: 9db5b38f7eebff28-IAD
-cf-cache-status: DYNAMIC
-content-encoding: gzip
-set-cookie: datadome=rDtvd3J2hO5AeghJMSFRRxGc6ifKCQYgMLcqPNr9rWiz2rdcXb032AY6GIZn8tUmYB96BKKbzh3_jSjEzYWLj8hDjl3oGYYAiu4jwdaxpf3vh2v4f7KH7kbqgsMWpkjt; Max-Age=31536000; Domain=.giantfood.com; Path=/; Secure; SameSite=Lax
-strict-transport-security: max-age=31536000; includeSubDomains
-vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, accept-encoding
-accept-ch: Sec-CH-UA,Sec-CH-UA-Mobile,Sec-CH-UA-Platform,Sec-CH-UA-Arch,Sec-CH-UA-Full-Version-List,Sec-CH-UA-Model,Sec-CH-Device-Memory
-x-datadome: protected
-request-context: appId=cid-v1:75750625-0c81-4f08-9f5d-ce4f73198e54
-X-Firefox-Spdy: h2

pm/task-sample.org

@@ -0,0 +1,22 @@
+#+title: Task Log
+#+updated: [2026-03-18 Wed 14:19]
+
+Use the template below, which should be a top-level org-mode header.
+
+* [ ] M.m.m: Task Title (estimate # commits)
+replace the old observed/canonical workflow with a review-first pipeline that groups normalized rows only during review/combine and links them to catalog items
+
+** Acceptance Criteria
+1. Criterion
+   - expanded data
+2. Criterion 
+
+- pm note: amplifying information
+
+** evidence
+- commit: abc123, bcd234
+- tests: 
+- datetime: [2026-03-18 Wed 14:15]
+
+** notes    
+- explanation of work done, decisions made, reasoning

pm/tasks.org

@@ -1,4 +1,6 @@
-* [ ] t1.1: harden giant receipt fetch cli (2-4 commits)
+#+title: Scrape-Giant Task Log
+
+* [X] t1.1: harden giant receipt fetch cli (2-4 commits)
 ** acceptance criteria
 - giant scraper runs from cli with prompts or env-backed defaults for `user_id` and `loyalty`
 - script reuses current browser session via firefox cookies + `curl_cffi`
@@ -12,11 +14,11 @@
 - raw json archive remains source of truth
 
 ** evidence
-- commit:
+- commit: `d57b9cf` on branch `cx`
-- tests:
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python scraper.py --help`; verified `.env` loading via `scraper.load_config()`
-- date:
+- date: 2026-03-14
 
-* [ ] t1.2: define grocery data model and file layout (1-2 commits)
+* [X] t1.2: define grocery data model and file layout (1-2 commits)
 ** acceptance criteria
 - decide and document the files/directories for:
   - retailer raw exports
@@ -28,15 +30,15 @@
 - explicitly separate retailer-specific parsing from cross-retailer canonicalization
 
 ** notes
-- this is the guardrail task so we don’t make giant-specific hacks the system of record
+- this is the guardrail task so we don't make giant-specific hacks the system of record
 - keep schema minimal but extensible
 
 ** evidence
-- commit:
+- commit: `42dbae1` on branch `cx`
-- tests:
+- tests: reviewed `giant_output/raw/history.json`, one sample raw order json, `giant_output/orders.csv`, `giant_output/items.csv`; documented schemas in `pm/data-model.org`
-- date:
+- date: 2026-03-15
 
-* [ ] t1.3: build giant parser/enricher from raw json (2-4 commits)
+* [X] t1.3: build giant parser/enricher from raw json (2-4 commits)
 ** acceptance criteria
 - parser reads giant raw order json files
 - outputs `items_enriched.csv`
@@ -54,11 +56,11 @@
 - parser should preserve ambiguity rather than hallucinating precision
 
 ** evidence
-- commit:
+- commit: `14f2cc2` on branch `cx`
-- tests:
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python enrich_giant.py`; verified `giant_output/items_enriched.csv` on real raw data
-- date:
+- date: 2026-03-16
 
-* [ ] t1.4: generate observed-product layer from enriched items (2-3 commits)
+* [X] t1.4: generate observed-product layer from enriched items (2-3 commits)
 
 ** acceptance criteria
 - distinct observed products are generated from enriched giant items
@@ -76,11 +78,11 @@
 - likely key is some combo of retailer + upc + normalized name
 
 ** evidence
-- commit:
+- commit: `dc39214` on branch `cx`
-- tests:
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python build_observed_products.py`; verified `giant_output/products_observed.csv`
-- date:
+- date: 2026-03-16
 
-* [ ] t1.5: build review queue for unresolved or low-confidence products (1-3 commits)
+* [X] t1.5: build review queue for unresolved or low-confidence products (1-3 commits)
 
 ** acceptance criteria
 - produce a review file containing observed products needing manual review
@@ -98,11 +100,11 @@
 - optimize for “approve once, remember forever”
 
 ** evidence
-- commit:
+- commit: `9b13ec3` on branch `cx`
-- tests:
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python build_review_queue.py`; verified `giant_output/review_queue.csv`
-- date:
+- date: 2026-03-16
 
-* [ ] t1.6: create canonical product layer and observed→canonical links (2-4 commits)
+* [X] t1.6: create canonical product layer and observed→canonical links (2-4 commits)
 
 ** acceptance criteria
 - define and create `products_canonical.csv`
@@ -120,11 +122,11 @@
 - do not require llm assistance for v1
 
 ** evidence
-- commit:
+- commit: `347cd44` on branch `cx`
-- tests:
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python build_canonical_layer.py`; verified seeded `giant_output/products_canonical.csv` and `giant_output/product_links.csv`
-- date:
+- date: 2026-03-16
 
-* [ ] t1.7: implement auto-link rules for easy matches (2-3 commits)
+* [X] t1.7: implement auto-link rules for easy matches (2-3 commits)
 
 ** acceptance criteria
 - auto-link can match observed products to canonical products using deterministic rules
@@ -139,53 +141,536 @@
 - false positives are worse than unresolved items
 
 ** evidence
-- commit:
+- commit: `385a31c` on branch `cx`
-- tests:
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python build_canonical_layer.py`; verified auto-linked `giant_output/products_canonical.csv` and `giant_output/product_links.csv`
-- date:
+- date: 2026-03-16
 
-* [ ] t1.8: support costco raw ingest path (2-5 commits)
+* [X] t1.8: support costco raw ingest path (2-5 commits)
 
 ** acceptance criteria
 - add a costco-specific raw ingest/export path
-- output costco line items into the same shared raw/enriched schema family
+- fetch costco receipt summary and receipt detail payloads from graphql endpoint
+- persist raw json under `costco_output/raw/orders.csv` and `./items.csv`, same format as giant
+- costco-native identifiers such as `transactionBarcode` as order id and `itemNumber` as retailer item id
+- preserve discount/coupon rows rather than dropping
+
+** notes
+- focus on raw costco acquisistion and flattening
+- do not force costco identifiers into `upc`
+- bearer/auth values should come from local env, not source
+
+** evidence
+- commit: `da00288` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python scrape_costco.py --help`; verified `costco_output/raw/*.json`, `costco_output/orders.csv`, and `costco_output/items.csv` from the local sample payload
+- date: 2026-03-16
+
+* [X] t1.8.1: support costco parser/enricher path (2-4 commits)
+
+** acceptance criteria
+- add a costco-specific enrich step producing `costco_output/items_enriched.csv`
+- output rows into the same shared enriched schema family as Giant
+- support costco-specific parsing for:
+  - `itemDescription01` + `itemDescription02`
+  - `itemNumber` as `retailer_item_id`
+  - discount lines / negative rows
+  - common size patterns such as `25#`, `48 OZ`, `2/24 OZ`, `6-PACK`
+- preserve obvious unknowns as blank rather than guessed values
+
+** notes
+- this is the real schema compatibility proof, not raw ingest alone
+- expect weaker identifiers than Giant
+
+** evidence
+- commit: `da00288` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python enrich_costco.py`; verified `costco_output/items_enriched.csv`
+- date: 2026-03-16
+* [X] t1.8.2: validate cross-retailer observed/canonical flow (1-3 commits)
+
+** acceptance criteria
+- feed Giant and Costco enriched rows through the same observed/canonical pipeline
 - confirm at least one product class can exist as:
-  - giant observed product
+  - Giant observed product
-  - costco observed product
+  - Costco observed product
   - one shared canonical product
+- document the exact example used for proof
 
 ** notes
-- this is the proof that the architecture generalizes
+- keep this to one or two well-behaved product classes first
-- don’t chase perfection before the second retailer lands
+- apples, eggs, bananas, or flour are better than weird prepared foods
+
+** evidence
+- commit: `da00288` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python validate_cross_retailer_flow.py`; proof example: Giant `FRESH BANANA` and Costco `BANANAS 3 LB / 1.36 KG` share one canonical in `combined_output/proof_examples.csv`
+- date: 2026-03-16
+* [X] t1.8.3: extend shared schema for retailer-native ids and adjustment lines (1-2 commits)
+
+** acceptance criteria
+- add shared fields needed for non-upc retailers, including:
+  - `retailer_item_id`
+  - `is_discount_line`
+  - `is_coupon_line` or equivalent if needed
+- keep `upc` nullable across the pipeline
+- update downstream builders/tests to accept retailers with blank `upc`
+
+** notes
+- this prevents costco from becoming a schema hack
+- do this once instead of sprinkling exceptions everywhere
+
+** evidence
+- commit: `9497565` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; verified shared enriched fields in `giant_output/items_enriched.csv` and `costco_output/items_enriched.csv`
+- date: 2026-03-16
+* [X] t1.8.4: verify and correct costco receipt enumeration (1–2 commits)
+
+** acceptance criteria
+- confirm graphql summary query returns all expected receipts
+- compare `inWarehouse` count vs number of `receipts` returned
+- widen or parameterize date window if necessary; website shows receipts in 3-month windows
+- persist request metadata (`startDate`, `endDate`, `documentType`, `documentSubType`)
+- emit warning when receipt counts mismatch
+
+** notes
+- goal is to confirm we are enumerating all receipts before parsing
+- do not expand schema or parser logic in this task
+- keep changes limited to summary query handling and diagnostics
+
+** evidence
+- commit: `ac82fa6` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python scrape_costco.py --help`; reviewed the sample Costco summary request in `pm/scrape-giant.org` against `costco_output/raw/summary.json` and added 3-month window chunking plus mismatch diagnostics
+- date: 2026-03-16
+* [X] t1.8.5: refactor costco scraper auth and UX with giant scraper
+
+** acceptance criteria
+- remove manual auth env vars
+- load costco cookies from firefox session
+- require only logged-in browser
+- replace start/end date flags with --months-back
+- maintain same raw output structure
+- ensure summary_lookup keys are collision-safe by using a composite key (transactionBarcode + transactionDateTime) instead of transactionBarcode alone 
+
+** notes
+- align Costco acquisition ergonomics with the Giant scraper
+- keep downstream Costco parsing and shared schemas unchanged
+
+** evidence
+- commit: `c0054dc` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python scrape_costco.py --help`; verified Costco summary/detail flattening now uses composite receipt keys in unit tests
+- date: 2026-03-16
+* [X] t1.8.6: add browser session helper (2-4 commits)
+
+** acceptance criteria
+- create a separate Python module/script that extracts firefox browser session data needed for giant and costco scrapers.
+- support Firefox and Costco first, including:
+  - loading cookies via existing browser-cookie approach
+  - reading browser storage needed for dynamic auth headers (e.g. Costco bearer token)
+  - copying locked browser sqlite/db files to a temp location before reading when necessary
+- expose a small interface usable by scrapers, e.g. cookie jar + storage/header values
+- keep retailer-specific parsing of extracted session data outside the low-level browser access layer
+- structure the helper so Chromium-family browser support can be added later without changing scraper call sites
+
+** notes
+- goal is to replace manual `.env` copying of volatile browser-derived auth data
+- session bootstrap only, not full browser automation
+- prefer one shared helper over retailer-specific ad hoc storage reads
+- Firefox only; Chromium support later
+
+** evidence
+- commit: `7789c2e` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python scrape_giant.py --help`; `./venv/bin/python scrape_costco.py --help`; verified Firefox storage token extraction and locked-db copy behavior in unit tests
+- date: 2026-03-16
+* [X] t1.8.7: simplify costco session bootstrap and remove over-abstraction (2-4 commits)
+
+** acceptance criteria
+- make `scrape_costco.py` readable end-to-end without tracing through multiple partial bootstrap layers
+- keep `browser_session.py` limited to low-level browser data access only:
+  - firefox profile discovery
+  - cookie loading
+  - storage reads
+  - sqlite copy/read helpers
+- remove or sharply reduce `retailer_sessions.py` so retailer-specific header extraction lives with the retailer scraper or in a very small retailer-specific helper
+- make session bootstrap flow explicit and linear:
+  - load browser context
+  - extract costco auth values
+  - build request headers
+  - build requests session
+- eliminate inconsistent/obsolete function signatures and dead call paths (e.g. mixed `build_session(...)` calling conventions, stale fallback branches, mismatched `build_headers(...)` args)
+- add one focused bootstrap debug print showing whether cookies, authorization, client id, and client identifier were found
+- preserve current working behavior where available; this is a refactor/clarification task, not a feature expansion task
+
+** notes
+- goal is to restore concern separation and debuggability
+- prefer obvious retailer-specific code over “generic” helpers that guess and obscure control flow
+- browser access can stay shared; retailer auth mapping should be explicit
+- no new heuristics in this task
+
+** evidence
+- commit: `d7a0329` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python scrape_costco.py --help`; verified explicit Costco session bootstrap flow in `scrape_costco.py` and low-level-only browser access in `browser_session.py`
+- date: 2026-03-16
+* [X] t1.9: build pivot-ready normalized purchase log and comparison metrics (2-4 commits)
+
+** acceptance criteria
+- produce a flat `purchases.csv` suitable for excel pivot tables and pivot charts
+- each purchase row preserves:
+  - purchase date
+  - retailer
+  - order id
+  - raw item name
+  - normalized item name
+  - canonical item id when resolved
+  - quantity / unit
+  - line total
+  - store/location info where available
+- derive normalized comparison fields where possible on enriched or observed product rows:
+  - `price_per_lb`
+  - `price_per_oz`
+  - `price_per_each`
+  - `price_per_count`
+- preserve the source basis used to derive each metric, e.g.:
+  - parsed size/unit
+  - receipt weight
+  - explicit count/pack
+- emit nulls when basis is unknown, conflicting, or ambiguous
+- support pivot-friendly analysis of purchase frequency and item cost over time
+- document at least one Giant vs Costco comparison example using the normalized metrics
+
+** notes
+- compute metrics as close to the raw observation as possible
+- canonical layer can aggregate later, but should not invent missing unit economics
+- unit discipline matters more than coverage
+- raw item name must be retained for audit/debugging
+
+** evidence
+- commit: `be1bf63` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python build_purchases.py`; verified `combined_output/purchases.csv` and `combined_output/comparison_examples.csv` on the current Giant + Costco dataset
+- date: 2026-03-16
+
+* [X] t1.11: define review and item-resolution workflow for unresolved products (2-3 commits)
+
+** acceptance criteria
+- define the persistent files used to resolve unknown items, including:
+  - review queue
+  - canonical item catalog
+  - alias / mapping layer if separate
+- specify how unresolved items move from `review_queue.csv` into the final normalized purchase log
+- define the manual resolution workflow, including:
+  - what the human edits
+  - what script is rerun afterward
+  - how resolved mappings are persisted for future runs
+- ensure resolved items are positively identified into stable canonical item ids rather than one-off text substitutions
+- document how raw item name, normalized item name, and canonical item id are all retained
+
+** notes
+- goal is “approve once, reuse forever”
+- keep the workflow simple and auditable
+- manual review is fine; the important part is making it durable and rerunnable
+
+** evidence
+- commit: `c7dad54` on branch `cx`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python build_purchases.py`; `./venv/bin/python review_products.py --refresh-only`; verified `combined_output/review_queue.csv`, `combined_output/review_resolutions.csv` workflow, and `combined_output/canonical_catalog.csv`
+- date: 2026-03-16
+* [X] t1.12: simplify review process display
+Clearly show current state separate from proposed future state.
+** acceptance criteria
+1. Display position in review queue, e.g., (1/22)
+2. Display compact header with observed_product under review, queue position, and canonical decision, e.g.: "Resolve [n] observed product group [name]  and associated items to canonical_name [name]? (\n [n] matched items)"
+3. color-code outputs based on info, input/prompt, warning/error
+   1. color action menu/requests for input differently from display text; do not color individual options separately
+   2. "no canonical_name suggestions found" is informational, not a warning/error.
+4. update action menu `[x]exclude` to `e[x]clude`
+5. on each review item, display a list of all matched items to be linked, sorted by descending date:
+   1. YYYY-mm-dd, price, raw item name, normalized item name, upc, retailer
+   2. image URL, if exists
+   3. Sample:
+6. on each review item, suggest (but do not auto-apply) up to 3 likely existing canonicals using determinstic rules, e.g:
+   1. exact normalized name match
+   2. prefix/contains match on canonical name
+   3. exact UPC
+7. Sample Entry:
+#+begin_comment
+ Review 7/22: Resolve observed_product MIXED PEPPER to canonical_name [__]?
+ 2 matched items:
+  [1] 2026-03-12 | 7.49 | MIXED PEPPER 6-PACK | MIXED PEPPER | [upc] | costco | [img_url]
+  [2] [YYYY-mm-dd] | [price] | [raw_name] | [observed_name] | [upc] | [retailer] | [img_url]
+ 2 canonical suggestions found:
+  [1] BELL PEPPERS, PRODUCE
+  [2] PEPPER, SPICES
+#+end_comment
+8. When link is selected, users should be able to select the number of the item in the list, e.g.:
+#+begin_comment
+  Select the canonical_name to associate [n] items with:
+   [1] GRB GRADU PCH PUF1. | gcan_01b0d623aa02
+   [2] BTB CHICKEN         | gcan_0201f0feb749
+   [3] LIME                | gcan_02074d9e7359
+#+end_comment
+9. Add confirmation to link selection with instructions, "[n] [observed_name] and future observed_name matches will be associated with [canonical_name], is this ok?
+     actions: [Y]es  [n]o  [b]ack  [s]kip  [q]uit
+
+- reinforce project terminology such as raw_name, observed_name, canonical_name   
+
+** evidence
+- commit: `7b8141c`, `d39497c`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python -m unittest tests.test_review_workflow tests.test_purchases`; `./venv/bin/python review_products.py --help`; verified compact review header, numbered matched-item display, informational no-suggestion state, numbered canonical selection, and confirmation flow
+- date: 2026-03-17
+
+** notes
+- The key improvement was shifting the prompt from system metadata to reviewer intent: one observed_product, its matched retailer rows, and one canonical_name decision.
+- Numbered canonical selection plus confirmation worked better than free-text id entry and should reduce accidental links.
+- Deterministic suggestions remain intentionally conservative; they speed up common cases, but unresolved items still depend on human review by design.
+
+* [X] t1.13.1 pipeline accountability and stage visibility (1-2 commits)
+add simple accounting so we can see what survives or drops at each pipeline stage
+
+** AC
+1. emit counts for raw, enriched, combined/observed, review-queued, canonical-linked, and final purchase-log rows
+2. report unresolved and dropped item counts explicitly
+3. make it easy to verify that missing items were intentionally left in review rather than silently lost
+- pm note: simple text/json/csv summary is sufficient; trust and visibility matter more than presentation
+
+** evidence
+- commit: `967e19e`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python report_pipeline_status.py --help`; `./venv/bin/python report_pipeline_status.py`; verified `combined_output/pipeline_status.csv` and `combined_output/pipeline_status.json`
+- date: 2026-03-17
+
+** notes
+- Added a single explicit status script instead of threading counters through every pipeline step; this keeps the pipeline simple while still making row survival visible.
+- The most useful check here is `unresolved_not_in_review_rows`; when it is non-zero, we know we have a real accounting bug rather than normal unresolved work.
+
+* [X] t1.13.2 costco discount matching and net pricing in enrich_costco (2-3 commits)
+refactor costco enrichment so discount lines are matched to purchased items and net pricing is preserved
+
+** AC
+1. detect costco discount/coupon rows like `/<retailer_item_id>` and match them to purchased items within the same order
+2. preserve raw discount rows for auditability while also carrying matched discount values onto the purchased item row
+3. add explicit fields for discount-adjusted pricing, e.g. `matched_discount_amount` and `net_line_total` (or equivalent)
+4. preserve original raw receipt amounts (`line_total`) without overwriting them
+- pm note: keep this retailer-specific and explicit; do not introduce generic discount heuristics
+
+** evidence
+- commit: `56a03bc`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python enrich_costco.py`; verified matched Costco discount rows now populate `matched_discount_amount` and `net_line_total` while preserving raw `line_total`
+- date: 2026-03-17
+
+** notes
+- Kept this retailer-specific and literal: only discount rows with `/<retailer_item_id>` are matched, and only within the same order.
+- Raw discount rows are still preserved for auditability; the purchased row now carries the matched adjustment separately rather than overwriting the original amount.
+* [X] t1.13.3 canonical cleanup and review-first product identity (3-4 commits)
+refactor canonical generation so product identity is cleaner, duplicate canonicals are reduced, and unresolved items stay in review instead of spawning junk canonicals
+
+** AC
+1. stop auto-creating new canonical products from weak normalized names alone; unresolved items remain in `review_queue.csv`
+2. canonical names are based on stable product identity rather than noisy observed titles
+3. packaging/count/size tokens are removed from canonical names when they belong in structured fields (`pack_qty`, `size_value`, `size_unit`)
+4. consolidate obvious duplicate canonicals (e.g. egg/lime cases) and ensure final outputs retain raw item name, normalized item name, and canonical item id
+- pm note: prefer conservative canonical creation and a better manual review loop over aggressive auto-unification
+
+** evidence
+- commit: `08e2a86`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python build_purchases.py`; `./venv/bin/python review_products.py --refresh-only`; verified weaker exact-name cases now remain unresolved in `combined_output/review_queue.csv` and canonical names are cleaned before auto-catalog creation
+- date: 2026-03-17
+
+** notes
+- Removed weak exact-name auto-canonical creation so ambiguous products stay in review instead of generating junk canonicals.
+- Canonical display names are now cleaned of obvious punctuation and packaging noise, but I kept the cleanup conservative rather than adding a broad fuzzy merge layer.
+* [X] t1.14: refactor retailer collection into the new data model (2-4 commits)
+move Giant and Costco collection into the new collect structure and make both retailers emit the same collected schemas
+
+** Acceptance Criteria
+1. create retailer-specific collect scripts in the target naming pattern, e.g.:
+  - collect_giant_web.py
+  - collect_costco_web.py
+2. collected outputs conform to pm/data-model.org:
+  - data/<retailer-method>/raw/...
+  - data/<retailer-method>/collected_orders.csv
+  - data/<retailer-method>/collected_items.csv
+3. current Giant and Costco raw acquisition behavior is preserved during the move
+4. collected schemas preserve retailer truth and provenance:
+  - no interpretation beyond basic flattening
+  - raw_order_path/raw_history_path remain usable
+  - unknown values remain blank rather than guessed
+5. old paths should be removed or deprecated
+6. collect_* scripts do not depend on any normalize/review files or scripts
+- pm note: this is a path/schema refactor, not a parsing rewrite
+
+** evidence
+- commit: `48c6eaf`
+- tests: `./venv/bin/python -m unittest tests.test_scraper tests.test_costco_pipeline tests.test_browser_session`; `./venv/bin/python collect_giant_web.py --help`; `./venv/bin/python collect_costco_web.py --help`; `./venv/bin/python scrape_giant.py --help`; `./venv/bin/python scrape_costco.py --help`
+- datetime: 2026-03-18
+
+** notes
+- Kept this as a path/schema move, not a parsing rewrite: the existing Giant and Costco collection behavior remains in place behind new `collect_*` entry points.
+- Added lightweight deprecation nudges on the legacy `scrape_*` commands rather than removing them immediately, so the move is inspectable and low-risk.
+- The main schema fix was on Giant collection, which was missing retailer/provenance/audit fields that Costco collection already carried.
+
+* [X] t1.14.1: refactor retailer normalization into the new normalized_items schema (3-5 commits)
+make Giant and Costco emit the shared normalized line-item schema without introducing cross-retailer identity logic
+
+** Acceptance Criteria
+1. create retailer-specific normalize scripts in the target naming pattern, e.g.:
+   - normalize_giant_web.py
+   - normalize_costco_web.py
+2. normalized outputs conform to pm/data-model.org:
+   - data/<retailer-method>/normalized_items.csv
+   - one row per collected line item
+   - normalized_row_id is stable and present
+   - normalized_item_id is stable, present, and represents retailer-level identity reused across repeated purchase rows when deterministic retailer evidence is sufficient
+   - normalized_quantity and normalized_quantity_unit
+   - repeated rows for the same retailer product resolve to the same normalized_item_id only when supported by deterministic retailer evidence, e.g. exact upc, exact retailer_item_id, exact cleaned name + same size/pack
+   - normalization_basis is explicit
+3. Giant normalization preserves current useful parsing:
+   - normalized item name
+   - size/unit/pack parsing
+   - fee/store-brand flags
+   - derived price fields
+4. Costco normalization preserves current useful parsing:
+   - normalized item name
+   - size/unit/pack parsing
+   - explicit discount matching using retailer-specific logic
+   - matched_discount_amount and net_line_total
+5. both normalizers preserve raw retailer truth:
+   - line_total is never overwritten
+   - unknown values remain blank rather than guessed
+6. no cross-retailer identity assignment occurs in normalization
+7. normalize never uses fuzzy or semantic matching to assign normalized_item_id
+
+- pm note: prefer explicit retailer-specific code paths over generic normalization helpers unless the duplication is truly mechanical
+- pm note: normalization may resolve retailer-level identity, but not catalog identity
+- pm note: normalized_item_id is the only retailer-level grouping identity; do not introduce observed_products or a second grouping artifact
+** evidence
+- commit: `9064de5`
+- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python -m unittest tests.test_enrich_giant tests.test_costco_pipeline tests.test_purchases`; `./venv/bin/python normalize_giant_web.py --help`; `./venv/bin/python normalize_costco_web.py --help`; `./venv/bin/python enrich_giant.py --help`; `./venv/bin/python enrich_costco.py --help`
+- datetime: 2026-03-18
+
+** notes
+- Kept the existing Giant and Costco parsing logic intact and added the new normalized schema fields in place, rather than rewriting the enrichers from scratch.
+- `normalized_item_id` is always present, but it only collapses repeated rows when the evidence is strong; otherwise it falls back to row-level identity via `normalized_row_id`.
+- Added `normalize_*` entry points for the new data-model layout while leaving the legacy `enrich_*` commands available during the transition.
+
+* [ ] t1.14.2: finalize filesystem and schema alignment for the refactor (2-4 commits)
+bring on-disk outputs fully into the target `data/` structure without changing retailer behavior
+
+** Acceptance Criteria
+1. retailer data directories conform to pm/data-model.org:
+   - `data/giant-web/raw/...`
+   - `data/giant-web/collected_orders.csv`
+   - `data/giant-web/collected_items.csv`
+   - `data/giant-web/normalized_items.csv`
+   - `data/costco-web/raw/...`
+   - `data/costco-web/collected_orders.csv`
+   - `data/costco-web/collected_items.csv`
+   - `data/costco-web/normalized_items.csv`
+2. review/combine outputs are moved or rewritten into the target review paths:
+   - `data/review/review_queue.csv`
+   - `data/review/product_links.csv`
+   - `data/review/review_resolutions.csv`
+   - `data/review/purchases.csv`
+   - `data/review/pipeline_status.csv`
+   - `data/review/pipeline_status.json`
+3. old transitional output paths are either:
+   - removed from active script defaults, or
+   - left as explicit compatibility shims with clear deprecation notes
+4. no recollection is required if existing raw files and collected csvs can be moved/copied losslessly into the new structure
+5. no schema information is lost during the move:
+   - raw paths still resolve
+   - collected/normalized csvs still open with the expected headers
+6. README and task/docs reflect the final active paths
+- pm note: prefer moving/adapting existing files over recollecting from retailers unless a real data loss or schema mismatch forces recollection
+- pm note: this is a structure-alignment task, not a retailer parsing task
 
 ** evidence
 - commit:
 - tests:
-- date:
+- datetime:
-
-* [ ] t1.9: compute normalized comparison metrics (2-3 commits)
-
-** acceptance criteria
-- derive normalized comparison fields where possible:
-  - price per lb
-  - price per oz
-  - price per each
-  - price per count
-- metrics are attached at canonical or linked-observed level as appropriate
-- emit obvious nulls when basis is unknown rather than inventing values
 
 ** notes
-- this is where “gala apples 5 lb bag vs other gala apples” becomes possible
+
-- units discipline matters a lot here
+* [ ] t1.14.3: retailer-specific Costco normalization cleanup (2-4 commits)
+tighten Costco-specific normalization so normalized item names are cleaner and deterministic retailer grouping is less noisy
+
+** Acceptance Criteria
+1. improve Costco item-name cleanup for obvious non-identity noise, such as:
+   - trailing slash fragments
+   - code tokens and receipt-format artifacts
+   - duplicated measurement fragments already captured in structured fields
+2. preserve deterministic normalization rules only:
+   - exact retailer_item_id
+   - exact cleaned name + same size/pack when needed
+   - approved retailer alias
+   - no fuzzy or semantic matching
+3. normalized Costco names improve on known bad examples, e.g.:
+   - `MANDARIN /` -> cleaner normalized item name
+   - `LIFE 6'TABLE ... /` -> cleaner normalized item name
+4. cleanup does not overwrite retailer truth:
+   - raw `item_name` is unchanged
+   - parsed `size_value`, `size_unit`, `pack_qty`, and pricing fields remain intact
+5. discount-row behavior remains correct:
+   - matched discount rows still populate `matched_discount_amount`
+   - `net_line_total` remains correct
+   - discount rows remain auditable
+6. add regression tests for the cleaned Costco examples and any new parsing rules
+- pm note: keep this explicitly Costco-specific; do not introduce a generic cleanup framework
+- pm note: prefer a short allowlist/blocklist of known receipt artifacts over broad heuristics
 
 ** evidence
 - commit:
 - tests:
-- date:
+- datetime:
 
-* [ ] t1.10: add optional llm-assisted suggestion workflow for unresolved products (2-4 commits)
+** notes
+
+* [ ] t1.15: refactor review/combine pipeline around normalized_item_id and catalog links (4-8 commits)
+replace the old observed/canonical workflow with a review-first pipeline that uses normalized_item_id as the retailer-level review unit and links it to catalog items
+
+** Acceptance Criteria
+1. refactor review outputs to conform to pm/data-model.org:
+   - data/review/review_queue.csv
+   - data/review/product_links.csv
+   - data/catalog.csv
+   - data/purchases.csv
+2. review logic uses normalized_item_id as the upstream retailer-level review identity:
+   - no dependency on observed_product_id
+   - no dependency on products_observed.csv
+   - one review/link decision applies to all purchase rows sharing the same normalized_item_id
+3. product_links.csv stores review-approved links from normalized_item_id to catalog_id
+   - one row per approved retailer-level identity to catalog mapping
+4. catalog.csv entries are review-first and conservative:
+   - no auto-creation from weak normalized names alone
+   - names come from reviewed catalog naming, not raw retailer strings
+   - packaging/count is not embedded in catalog_name unless essential to identity
+   - catalog_name/product_type/category/brand/variant may be blank until reviewed; blank is preferred to guessed
+5. purchases.csv remains pivot-ready and retains:
+   - raw item name
+   - normalized item name
+   - normalized_row_id (not for review)
+   - normalized_item_id
+   - catalog_id
+   - catalog fields
+   - raw line_total
+   - matched_discount_amount and net_line_total when present
+   - derived price fields and their bases
+6. terminal review flow remains simple and usable:
+   - reviewer sees one grouped retailer item identity (normalized_item_id) with count and list of matches, not one prompt per purchase row; use existing pattern as a template
+   - link to existing catalog item
+   - create new catalog item
+   - exclude
+   - skip
+7. pipeline accounting remains valid after the refactor:
+   - unresolved items are visible
+   - missing items are not silently dropped
+8. pm note: prefer a better manual review loop over aggressive automatic grouping. initial manual data entry is expected, and should resolve over time
+9. pm note: keep review/combine auditable; each catalog link should be explainable from normalized rows and review state
+
+** evidence
+- commit:
+- tests:
+- datetime:
+
+** notes
+
+* [ ] 1t.10: add optional llm-assisted suggestion workflow for unresolved normalized retailer items (2-4 commits)
 
 ** acceptance criteria
-- llm suggestions are generated only for unresolved observed products
+- llm suggestions are generated only for unresolved normalized retailer items
 - llm outputs are stored as suggestions, not auto-applied truth
 - reviewer can approve/edit/reject suggestions
 - approved decisions are persisted into canonical/link files

report_pipeline_status.py

@@ -0,0 +1,119 @@
+import json
+from pathlib import Path
+
+import click
+
+import build_observed_products
+import build_purchases
+import review_products
+from layer_helpers import read_csv_rows, write_csv_rows
+
+
+SUMMARY_FIELDS = ["stage", "count"]
+
+
+def read_rows_if_exists(path):
+    path = Path(path)
+    if not path.exists():
+        return []
+    return read_csv_rows(path)
+
+
+def build_status_summary(
+    giant_orders,
+    giant_items,
+    giant_enriched,
+    costco_orders,
+    costco_items,
+    costco_enriched,
+    purchases,
+    resolutions,
+):
+    enriched_rows = giant_enriched + costco_enriched
+    observed_rows = build_observed_products.build_observed_products(enriched_rows)
+    queue_rows = review_products.build_review_queue(purchases, resolutions)
+
+    unresolved_purchase_rows = [
+        row
+        for row in purchases
+        if row.get("observed_product_id")
+        and not row.get("canonical_product_id")
+        and row.get("is_fee") != "true"
+        and row.get("is_discount_line") != "true"
+        and row.get("is_coupon_line") != "true"
+    ]
+    excluded_rows = [
+        row
+        for row in purchases
+        if row.get("resolution_action") == "exclude"
+    ]
+    linked_purchase_rows = [row for row in purchases if row.get("canonical_product_id")]
+
+    summary = [
+        {"stage": "raw_orders", "count": len(giant_orders) + len(costco_orders)},
+        {"stage": "raw_items", "count": len(giant_items) + len(costco_items)},
+        {"stage": "enriched_items", "count": len(enriched_rows)},
+        {"stage": "observed_products", "count": len(observed_rows)},
+        {"stage": "review_queue_observed_products", "count": len(queue_rows)},
+        {"stage": "canonical_linked_purchase_rows", "count": len(linked_purchase_rows)},
+        {"stage": "final_purchase_rows", "count": len(purchases)},
+        {"stage": "unresolved_purchase_rows", "count": len(unresolved_purchase_rows)},
+        {"stage": "excluded_purchase_rows", "count": len(excluded_rows)},
+        {
+            "stage": "unresolved_not_in_review_rows",
+            "count": len(
+                [
+                    row
+                    for row in unresolved_purchase_rows
+                    if row.get("observed_product_id")
+                    not in {queue_row["observed_product_id"] for queue_row in queue_rows}
+                ]
+            ),
+        },
+    ]
+    return summary
+
+
+@click.command()
+@click.option("--giant-orders-csv", default="giant_output/orders.csv", show_default=True)
+@click.option("--giant-items-csv", default="giant_output/items.csv", show_default=True)
+@click.option("--giant-enriched-csv", default="giant_output/items_enriched.csv", show_default=True)
+@click.option("--costco-orders-csv", default="costco_output/orders.csv", show_default=True)
+@click.option("--costco-items-csv", default="costco_output/items.csv", show_default=True)
+@click.option("--costco-enriched-csv", default="costco_output/items_enriched.csv", show_default=True)
+@click.option("--purchases-csv", default="combined_output/purchases.csv", show_default=True)
+@click.option("--resolutions-csv", default="combined_output/review_resolutions.csv", show_default=True)
+@click.option("--summary-csv", default="combined_output/pipeline_status.csv", show_default=True)
+@click.option("--summary-json", default="combined_output/pipeline_status.json", show_default=True)
+def main(
+    giant_orders_csv,
+    giant_items_csv,
+    giant_enriched_csv,
+    costco_orders_csv,
+    costco_items_csv,
+    costco_enriched_csv,
+    purchases_csv,
+    resolutions_csv,
+    summary_csv,
+    summary_json,
+):
+    summary_rows = build_status_summary(
+        read_rows_if_exists(giant_orders_csv),
+        read_rows_if_exists(giant_items_csv),
+        read_rows_if_exists(giant_enriched_csv),
+        read_rows_if_exists(costco_orders_csv),
+        read_rows_if_exists(costco_items_csv),
+        read_rows_if_exists(costco_enriched_csv),
+        read_rows_if_exists(purchases_csv),
+        read_rows_if_exists(resolutions_csv),
+    )
+    write_csv_rows(summary_csv, summary_rows, SUMMARY_FIELDS)
+    summary_json_path = Path(summary_json)
+    summary_json_path.parent.mkdir(parents=True, exist_ok=True)
+    summary_json_path.write_text(json.dumps(summary_rows, indent=2), encoding="utf-8")
+    for row in summary_rows:
+        click.echo(f"{row['stage']}: {row['count']}")
+
+
+if __name__ == "__main__":
+    main()

review_products.py

@@ -0,0 +1,426 @@
+from collections import defaultdict
+from datetime import date
+
+import click
+
+import build_purchases
+from layer_helpers import compact_join, stable_id, write_csv_rows
+
+
+QUEUE_FIELDS = [
+    "review_id",
+    "retailer",
+    "observed_product_id",
+    "canonical_product_id",
+    "reason_code",
+    "priority",
+    "raw_item_names",
+    "normalized_names",
+    "upc_values",
+    "example_prices",
+    "seen_count",
+    "status",
+    "resolution_action",
+    "resolution_notes",
+    "created_at",
+    "updated_at",
+]
+
+
+def build_review_queue(purchase_rows, resolution_rows):
+    by_observed = defaultdict(list)
+    resolution_lookup = build_purchases.load_resolution_lookup(resolution_rows)
+
+    for row in purchase_rows:
+        observed_product_id = row.get("observed_product_id", "")
+        if not observed_product_id:
+            continue
+        by_observed[observed_product_id].append(row)
+
+    today_text = str(date.today())
+    queue_rows = []
+    for observed_product_id, rows in sorted(by_observed.items()):
+        current_resolution = resolution_lookup.get(observed_product_id, {})
+        if current_resolution.get("status") == "approved":
+            continue
+        unresolved_rows = [row for row in rows if not row.get("canonical_product_id")]
+        if not unresolved_rows:
+            continue
+
+        retailers = sorted({row["retailer"] for row in rows})
+        review_id = stable_id("rvw", observed_product_id)
+        queue_rows.append(
+            {
+                "review_id": review_id,
+                "retailer": " | ".join(retailers),
+                "observed_product_id": observed_product_id,
+                "canonical_product_id": current_resolution.get("canonical_product_id", ""),
+                "reason_code": "missing_canonical_link",
+                "priority": "high",
+                "raw_item_names": compact_join(
+                    sorted({row["raw_item_name"] for row in rows if row["raw_item_name"]}),
+                    limit=8,
+                ),
+                "normalized_names": compact_join(
+                    sorted(
+                        {
+                            row["normalized_item_name"]
+                            for row in rows
+                            if row["normalized_item_name"]
+                        }
+                    ),
+                    limit=8,
+                ),
+                "upc_values": compact_join(
+                    sorted({row["upc"] for row in rows if row["upc"]}),
+                    limit=8,
+                ),
+                "example_prices": compact_join(
+                    sorted({row["line_total"] for row in rows if row["line_total"]}),
+                    limit=8,
+                ),
+                "seen_count": str(len(rows)),
+                "status": current_resolution.get("status", "pending"),
+                "resolution_action": current_resolution.get("resolution_action", ""),
+                "resolution_notes": current_resolution.get("resolution_notes", ""),
+                "created_at": current_resolution.get("reviewed_at", today_text),
+                "updated_at": today_text,
+            }
+        )
+    return queue_rows
+
+
+def save_resolution_rows(path, rows):
+    write_csv_rows(path, rows, build_purchases.RESOLUTION_FIELDS)
+
+
+def save_catalog_rows(path, rows):
+    write_csv_rows(path, rows, build_purchases.CATALOG_FIELDS)
+
+
+INFO_COLOR = "cyan"
+PROMPT_COLOR = "bright_yellow"
+WARNING_COLOR = "magenta"
+
+
+def sort_related_items(rows):
+    return sorted(
+        rows,
+        key=lambda row: (
+            row.get("purchase_date", ""),
+            row.get("order_id", ""),
+            int(row.get("line_no", "0") or "0"),
+        ),
+        reverse=True,
+    )
+
+
+def build_canonical_suggestions(related_rows, catalog_rows, limit=3):
+    normalized_names = {
+        row.get("normalized_item_name", "").strip().upper()
+        for row in related_rows
+        if row.get("normalized_item_name", "").strip()
+    }
+    upcs = {
+        row.get("upc", "").strip()
+        for row in related_rows
+        if row.get("upc", "").strip()
+    }
+    suggestions = []
+    seen_ids = set()
+
+    def add_matches(rows, reason):
+        for row in rows:
+            canonical_product_id = row.get("canonical_product_id", "")
+            if not canonical_product_id or canonical_product_id in seen_ids:
+                continue
+            seen_ids.add(canonical_product_id)
+            suggestions.append(
+                {
+                    "canonical_product_id": canonical_product_id,
+                    "canonical_name": row.get("canonical_name", ""),
+                    "reason": reason,
+                }
+            )
+            if len(suggestions) >= limit:
+                return True
+        return False
+
+    exact_upc_rows = [
+        row
+        for row in catalog_rows
+        if row.get("upc", "").strip() and row.get("upc", "").strip() in upcs
+    ]
+    if add_matches(exact_upc_rows, "exact upc"):
+        return suggestions
+
+    exact_name_rows = [
+        row
+        for row in catalog_rows
+        if row.get("canonical_name", "").strip().upper() in normalized_names
+    ]
+    if add_matches(exact_name_rows, "exact normalized name"):
+        return suggestions
+
+    contains_rows = []
+    for row in catalog_rows:
+        canonical_name = row.get("canonical_name", "").strip().upper()
+        if not canonical_name:
+            continue
+        for normalized_name in normalized_names:
+            if normalized_name in canonical_name or canonical_name in normalized_name:
+                contains_rows.append(row)
+                break
+    add_matches(contains_rows, "canonical name contains match")
+    return suggestions
+
+
+def build_display_lines(queue_row, related_rows):
+    lines = []
+    for index, row in enumerate(sort_related_items(related_rows), start=1):
+        lines.append(
+            " [{index}] {purchase_date} | {line_total} | {raw_item_name} | {normalized_item_name} | "
+            "{upc} | {retailer}".format(
+                index=index,
+                purchase_date=row.get("purchase_date", ""),
+                line_total=row.get("line_total", ""),
+                raw_item_name=row.get("raw_item_name", ""),
+                normalized_item_name=row.get("normalized_item_name", ""),
+                upc=row.get("upc", ""),
+                retailer=row.get("retailer", ""),
+            )
+        )
+        if row.get("image_url"):
+            lines.append(f"     {row['image_url']}")
+    if not lines:
+        lines.append(" [1] no matched item rows found")
+    return lines
+
+
+def observed_name(queue_row, related_rows):
+    if queue_row.get("normalized_names"):
+        return queue_row["normalized_names"].split(" | ")[0]
+    for row in related_rows:
+        if row.get("normalized_item_name"):
+            return row["normalized_item_name"]
+    return queue_row.get("observed_product_id", "")
+
+
+def choose_existing_canonical(display_rows, observed_label, matched_count):
+    click.secho(
+        f"Select the canonical_name to associate {matched_count} items with:",
+        fg=INFO_COLOR,
+    )
+    for index, row in enumerate(display_rows, start=1):
+        click.echo(f"  [{index}] {row['canonical_name']} | {row['canonical_product_id']}")
+    choice = click.prompt(
+        click.style("selection", fg=PROMPT_COLOR),
+        type=click.IntRange(1, len(display_rows)),
+    )
+    chosen_row = display_rows[choice - 1]
+    click.echo(
+        f'{matched_count} "{observed_label}" items and future matches will be associated '
+        f'with "{chosen_row["canonical_name"]}".'
+    )
+    click.secho(
+        "actions: [y]es  [n]o  [b]ack  [s]kip  [q]uit",
+        fg=PROMPT_COLOR,
+    )
+    confirm = click.prompt(
+        click.style("confirm", fg=PROMPT_COLOR),
+        type=click.Choice(["y", "n", "b", "s", "q"]),
+    )
+    if confirm == "y":
+        return chosen_row["canonical_product_id"], ""
+    if confirm == "s":
+        return "", "skip"
+    if confirm == "q":
+        return "", "quit"
+    return "", "back"
+
+
+def prompt_resolution(queue_row, related_rows, catalog_rows, queue_index, queue_total):
+    suggestions = build_canonical_suggestions(related_rows, catalog_rows)
+    observed_label = observed_name(queue_row, related_rows)
+    matched_count = len(related_rows)
+    click.echo("")
+    click.secho(
+        f"Review {queue_index}/{queue_total}: Resolve observed_product {observed_label} "
+        "to canonical_name [__]?",
+        fg=INFO_COLOR,
+    )
+    click.echo(f"{matched_count} matched items:")
+    for line in build_display_lines(queue_row, related_rows):
+        click.echo(line)
+    if suggestions:
+        click.echo(f"{len(suggestions)} canonical suggestions found:")
+        for index, suggestion in enumerate(suggestions, start=1):
+            click.echo(f" [{index}] {suggestion['canonical_name']}")
+    else:
+        click.echo("no canonical_name suggestions found")
+    click.secho(
+        "[l]ink existing  [n]ew canonical  e[x]clude  [s]kip  [q]uit:",
+        fg=PROMPT_COLOR,
+    )
+    action = click.prompt(
+        "",
+        type=click.Choice(["l", "n", "x", "s", "q"]),
+        prompt_suffix=" ",
+    )
+    if action == "q":
+        return None, None
+    if action == "s":
+        return {
+            "observed_product_id": queue_row["observed_product_id"],
+            "canonical_product_id": "",
+            "resolution_action": "skip",
+            "status": "pending",
+            "resolution_notes": queue_row.get("resolution_notes", ""),
+            "reviewed_at": str(date.today()),
+        }, None
+    if action == "x":
+        notes = click.prompt(
+            click.style("exclude notes", fg=PROMPT_COLOR),
+            default="",
+            show_default=False,
+        )
+        return {
+            "observed_product_id": queue_row["observed_product_id"],
+            "canonical_product_id": "",
+            "resolution_action": "exclude",
+            "status": "approved",
+            "resolution_notes": notes,
+            "reviewed_at": str(date.today()),
+        }, None
+    if action == "l":
+        display_rows = suggestions or [
+            {
+                "canonical_product_id": row["canonical_product_id"],
+                "canonical_name": row["canonical_name"],
+                "reason": "catalog sample",
+            }
+            for row in catalog_rows[:10]
+        ]
+        while True:
+            canonical_product_id, outcome = choose_existing_canonical(
+                display_rows,
+                observed_label,
+                matched_count,
+            )
+            if outcome == "skip":
+                return {
+                    "observed_product_id": queue_row["observed_product_id"],
+                    "canonical_product_id": "",
+                    "resolution_action": "skip",
+                    "status": "pending",
+                    "resolution_notes": queue_row.get("resolution_notes", ""),
+                    "reviewed_at": str(date.today()),
+                }, None
+            if outcome == "quit":
+                return None, None
+            if outcome == "back":
+                continue
+            break
+        notes = click.prompt(click.style("link notes", fg=PROMPT_COLOR), default="", show_default=False)
+        return {
+            "observed_product_id": queue_row["observed_product_id"],
+            "canonical_product_id": canonical_product_id,
+            "resolution_action": "link",
+            "status": "approved",
+            "resolution_notes": notes,
+            "reviewed_at": str(date.today()),
+        }, None
+
+    canonical_name = click.prompt(click.style("canonical name", fg=PROMPT_COLOR), type=str)
+    category = click.prompt(
+        click.style("category", fg=PROMPT_COLOR),
+        default="",
+        show_default=False,
+    )
+    product_type = click.prompt(
+        click.style("product type", fg=PROMPT_COLOR),
+        default="",
+        show_default=False,
+    )
+    notes = click.prompt(
+        click.style("notes", fg=PROMPT_COLOR),
+        default="",
+        show_default=False,
+    )
+    canonical_product_id = stable_id("gcan", f"manual|{canonical_name}|{category}|{product_type}")
+    canonical_row = {
+        "canonical_product_id": canonical_product_id,
+        "canonical_name": canonical_name,
+        "category": category,
+        "product_type": product_type,
+        "brand": "",
+        "variant": "",
+        "size_value": "",
+        "size_unit": "",
+        "pack_qty": "",
+        "measure_type": "",
+        "notes": notes,
+        "created_at": str(date.today()),
+        "updated_at": str(date.today()),
+    }
+    resolution_row = {
+        "observed_product_id": queue_row["observed_product_id"],
+        "canonical_product_id": canonical_product_id,
+        "resolution_action": "create",
+        "status": "approved",
+        "resolution_notes": notes,
+        "reviewed_at": str(date.today()),
+    }
+    return resolution_row, canonical_row
+
+
+@click.command()
+@click.option("--purchases-csv", default="combined_output/purchases.csv", show_default=True)
+@click.option("--queue-csv", default="combined_output/review_queue.csv", show_default=True)
+@click.option("--resolutions-csv", default="combined_output/review_resolutions.csv", show_default=True)
+@click.option("--catalog-csv", default="combined_output/canonical_catalog.csv", show_default=True)
+@click.option("--limit", default=0, show_default=True, type=int)
+@click.option("--refresh-only", is_flag=True, help="Only rebuild review_queue.csv without prompting.")
+def main(purchases_csv, queue_csv, resolutions_csv, catalog_csv, limit, refresh_only):
+    purchase_rows = build_purchases.read_optional_csv_rows(purchases_csv)
+    resolution_rows = build_purchases.read_optional_csv_rows(resolutions_csv)
+    catalog_rows = build_purchases.read_optional_csv_rows(catalog_csv)
+    queue_rows = build_review_queue(purchase_rows, resolution_rows)
+    write_csv_rows(queue_csv, queue_rows, QUEUE_FIELDS)
+    click.echo(f"wrote {len(queue_rows)} rows to {queue_csv}")
+
+    if refresh_only:
+        return
+
+    resolution_lookup = build_purchases.load_resolution_lookup(resolution_rows)
+    catalog_by_id = {row["canonical_product_id"]: row for row in catalog_rows if row.get("canonical_product_id")}
+    rows_by_observed = defaultdict(list)
+    for row in purchase_rows:
+        observed_product_id = row.get("observed_product_id", "")
+        if observed_product_id:
+            rows_by_observed[observed_product_id].append(row)
+    reviewed = 0
+    for index, queue_row in enumerate(queue_rows, start=1):
+        if limit and reviewed >= limit:
+            break
+        related_rows = rows_by_observed.get(queue_row["observed_product_id"], [])
+        result = prompt_resolution(queue_row, related_rows, catalog_rows, index, len(queue_rows))
+        if result == (None, None):
+            break
+        resolution_row, canonical_row = result
+        resolution_lookup[resolution_row["observed_product_id"]] = resolution_row
+        if canonical_row and canonical_row["canonical_product_id"] not in catalog_by_id:
+            catalog_by_id[canonical_row["canonical_product_id"]] = canonical_row
+            catalog_rows.append(canonical_row)
+        reviewed += 1
+
+    save_resolution_rows(resolutions_csv, sorted(resolution_lookup.values(), key=lambda row: row["observed_product_id"]))
+    save_catalog_rows(catalog_csv, sorted(catalog_by_id.values(), key=lambda row: row["canonical_product_id"]))
+    click.echo(
+        f"saved {len(resolution_lookup)} resolution rows to {resolutions_csv} "
+        f"and {len(catalog_by_id)} catalog rows to {catalog_csv}"
+    )
+
+
+if __name__ == "__main__":
+    main()

scrape-click.py

@@ -1,254 +0,0 @@
-import json
-import time
-from pathlib import Path
-
-import browser_cookie3
-import click
-import pandas as pd
-from curl_cffi import requests
-from dotenv import load_dotenv
-import os
-
-
-BASE = "https://giantfood.com"
-ACCOUNT_PAGE = f"{BASE}/account/history/invoice/in-store"
-
-
-def load_config():
-    load_dotenv()
-    return {
-        "user_id": os.getenv("GIANT_USER_ID", "").strip(),
-        "loyalty": os.getenv("GIANT_LOYALTY_NUMBER", "").strip(),
-    }
-
-
-def build_session():
-    s = requests.Session()
-    s.cookies.update(browser_cookie3.firefox(domain_name="giantfood.com"))
-    s.headers.update({
-        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:148.0) Gecko/20100101 Firefox/148.0",
-        "accept": "application/json, text/plain, */*",
-        "accept-language": "en-US,en;q=0.9",
-        "referer": ACCOUNT_PAGE,
-    })
-    return s
-
-
-def safe_get(session, url, **kwargs):
-    last_response = None
-
-    for attempt in range(3):
-        try:
-            r = session.get(
-                url,
-                impersonate="firefox",
-                timeout=30,
-                **kwargs,
-            )
-            last_response = r
-
-            if r.status_code == 200:
-                return r
-
-            click.echo(f"retry {attempt + 1}/3 status={r.status_code}")
-        except Exception as e:
-            click.echo(f"retry {attempt + 1}/3 error={e}")
-
-        time.sleep(3)
-
-    if last_response is not None:
-        last_response.raise_for_status()
-
-    raise RuntimeError(f"failed to fetch {url}")
-
-
-def get_history(session, user_id, loyalty):
-    url = f"{BASE}/api/v6.0/user/{user_id}/order/history"
-    r = safe_get(
-        session,
-        url,
-        params={
-            "filter": "instore",
-            "loyaltyNumber": loyalty,
-        },
-    )
-    return r.json()
-
-
-def get_order_detail(session, user_id, order_id):
-    url = f"{BASE}/api/v6.0/user/{user_id}/order/history/detail/{order_id}"
-    r = safe_get(
-        session,
-        url,
-        params={"isInStore": "true"},
-    )
-    return r.json()
-
-
-def flatten_orders(history, details):
-    orders = []
-    items = []
-
-    history_lookup = {
-        r["orderId"]: r
-        for r in history.get("records", [])
-    }
-
-    for d in details:
-        hist = history_lookup.get(d["orderId"], {})
-        pup = d.get("pup", {})
-
-        orders.append({
-            "order_id": d["orderId"],
-            "order_date": d.get("orderDate"),
-            "delivery_date": d.get("deliveryDate"),
-            "service_type": hist.get("serviceType"),
-            "order_total": d.get("orderTotal"),
-            "payment_method": d.get("paymentMethod"),
-            "total_item_count": d.get("totalItemCount"),
-            "total_savings": d.get("totalSavings"),
-            "your_savings_total": d.get("yourSavingsTotal"),
-            "coupons_discounts_total": d.get("couponsDiscountsTotal"),
-            "store_name": pup.get("storeName"),
-            "store_number": pup.get("aholdStoreNumber"),
-            "store_address1": pup.get("storeAddress1"),
-            "store_city": pup.get("storeCity"),
-            "store_state": pup.get("storeState"),
-            "store_zipcode": pup.get("storeZipcode"),
-            "refund_order": d.get("refundOrder"),
-            "ebt_order": d.get("ebtOrder"),
-        })
-
-        for i, item in enumerate(d.get("items", []), start=1):
-            items.append({
-                "order_id": d["orderId"],
-                "order_date": d.get("orderDate"),
-                "line_no": i,
-                "pod_id": item.get("podId"),
-                "item_name": item.get("itemName"),
-                "upc": item.get("primUpcCd"),
-                "category_id": item.get("categoryId"),
-                "category": item.get("categoryDesc"),
-                "qty": item.get("shipQy"),
-                "unit": item.get("lbEachCd"),
-                "unit_price": item.get("unitPrice"),
-                "line_total": item.get("groceryAmount"),
-                "picked_weight": item.get("totalPickedWeight"),
-                "mvp_savings": item.get("mvpSavings"),
-                "reward_savings": item.get("rewardSavings"),
-                "coupon_savings": item.get("couponSavings"),
-                "coupon_price": item.get("couponPrice"),
-            })
-
-    return pd.DataFrame(orders), pd.DataFrame(items)
-
-
-def read_existing_order_ids(orders_csv: Path) -> set[str]:
-    if not orders_csv.exists():
-        return set()
-
-    try:
-        df = pd.read_csv(orders_csv, dtype={"order_id": str})
-        if "order_id" not in df.columns:
-            return set()
-        return set(df["order_id"].dropna().astype(str))
-    except Exception:
-        return set()
-
-
-def append_dedup(existing_path: Path, new_df: pd.DataFrame, subset: list[str]) -> pd.DataFrame:
-    if existing_path.exists():
-        old_df = pd.read_csv(existing_path, dtype=str)
-        combined = pd.concat([old_df, new_df.astype(str)], ignore_index=True)
-    else:
-        combined = new_df.astype(str).copy()
-
-    combined = combined.drop_duplicates(subset=subset, keep="last")
-    combined.to_csv(existing_path, index=False)
-    return combined
-
-
-@click.command()
-@click.option("--user-id", default=None, help="giant user id")
-@click.option("--loyalty", default=None, help="giant loyalty number")
-@click.option("--outdir", default="giant_output", show_default=True, help="output directory")
-@click.option("--sleep-seconds", default=1.5, show_default=True, type=float, help="delay between detail requests")
-def main(user_id, loyalty, outdir, sleep_seconds):
-    cfg = load_config()
-
-    user_id = user_id or cfg["user_id"] or click.prompt("giant user id", type=str)
-    loyalty = loyalty or cfg["loyalty"] or click.prompt("giant loyalty number", type=str)
-
-    outdir = Path(outdir)
-    rawdir = outdir / "raw"
-    rawdir.mkdir(parents=True, exist_ok=True)
-
-    orders_csv = outdir / "orders.csv"
-    items_csv = outdir / "items.csv"
-
-    click.echo("using cookies from your current firefox profile.")
-    click.echo(f"open giant here, make sure you're logged in, then return: {ACCOUNT_PAGE}")
-    click.pause(info="press any key once giant is open and logged in")
-
-    session = build_session()
-
-    click.echo("fetching order history...")
-    history = get_history(session, user_id, loyalty)
-
-    (rawdir / "history.json").write_text(
-        json.dumps(history, indent=2),
-        encoding="utf-8",
-    )
-
-    records = history.get("records", [])
-    click.echo(f"history returned {len(records)} visits")
-    click.echo("tip: giant appears to expose only the most recent 50 visits, so run this periodically if you want full continuity.")
-
-    history_order_ids = [str(r["orderId"]) for r in records]
-    existing_order_ids = read_existing_order_ids(orders_csv)
-    new_order_ids = [oid for oid in history_order_ids if oid not in existing_order_ids]
-
-    click.echo(f"existing orders in csv: {len(existing_order_ids)}")
-    click.echo(f"new orders to fetch: {len(new_order_ids)}")
-
-    if not new_order_ids:
-        click.echo("no new orders found. done.")
-        return
-
-    details = []
-    for order_id in new_order_ids:
-        click.echo(f"fetching {order_id}")
-        d = get_order_detail(session, user_id, order_id)
-        details.append(d)
-
-        (rawdir / f"{order_id}.json").write_text(
-            json.dumps(d, indent=2),
-            encoding="utf-8",
-        )
-
-        time.sleep(sleep_seconds)
-
-    click.echo("flattening new data...")
-    orders_df, items_df = flatten_orders(history, details)
-
-    orders_all = append_dedup(
-        orders_csv,
-        orders_df,
-        subset=["order_id"],
-    )
-
-    items_all = append_dedup(
-        items_csv,
-        items_df,
-        subset=["order_id", "line_no", "item_name", "upc", "line_total"],
-    )
-
-    click.echo("done")
-    click.echo(f"orders csv: {orders_csv}")
-    click.echo(f"items csv:  {items_csv}")
-    click.echo(f"total orders stored: {len(orders_all)}")
-    click.echo(f"total item rows stored: {len(items_all)}")
-
-
-if __name__ == "__main__":
-    main()

scrape_costco.py

@@ -0,0 +1,738 @@
+import os
+import csv
+import json
+import time
+import re
+from pathlib import Path
+from calendar import monthrange
+from datetime import datetime, timedelta
+from dotenv import load_dotenv
+import click
+from curl_cffi import requests
+
+from browser_session import (
+    find_firefox_profile_dir,
+    load_firefox_cookies,
+    read_firefox_local_storage,
+    read_firefox_webapps_store,
+)
+
+BASE_URL = "https://ecom-api.costco.com/ebusiness/order/v1/orders/graphql"
+RETAILER = "costco"
+
+SUMMARY_QUERY = """
+query receiptsWithCounts($startDate: String!, $endDate: String!, $documentType: String!, $documentSubType: String!) {
+  receiptsWithCounts(startDate: $startDate, endDate: $endDate, documentType: $documentType, documentSubType: $documentSubType) {
+    inWarehouse
+    gasStation
+    carWash
+    gasAndCarWash
+    receipts {
+      warehouseName
+      receiptType 
+      documentType
+      transactionDateTime
+      transactionBarcode
+      warehouseName
+      transactionType
+      total
+      totalItemCount
+      itemArray {
+        itemNumber
+      }
+      tenderArray {
+        tenderTypeCode
+        tenderDescription
+        amountTender
+      }
+      couponArray {
+        upcnumberCoupon
+      }
+    }
+  }
+}
+""".strip()
+
+DETAIL_QUERY = """
+query receiptsWithCounts($barcode: String!, $documentType: String!) {
+  receiptsWithCounts(barcode: $barcode, documentType: $documentType) {
+    receipts {
+      warehouseName
+      receiptType
+      documentType
+      transactionDateTime
+      transactionDate
+      companyNumber
+      warehouseNumber
+      operatorNumber
+      warehouseShortName
+      registerNumber
+      transactionNumber
+      transactionType
+      transactionBarcode
+      total
+      warehouseAddress1
+      warehouseAddress2
+      warehouseCity
+      warehouseState
+      warehouseCountry
+      warehousePostalCode
+      totalItemCount
+      subTotal
+      taxes
+      total
+      invoiceNumber
+      sequenceNumber
+      itemArray {
+        itemNumber
+        itemDescription01
+        frenchItemDescription1
+        itemDescription02
+        frenchItemDescription2
+        itemIdentifier
+        itemDepartmentNumber
+        unit
+        amount
+        taxFlag
+        merchantID
+        entryMethod
+        transDepartmentNumber
+        fuelUnitQuantity
+        fuelGradeCode
+        itemUnitPriceAmount
+        fuelUomCode
+        fuelUomDescription
+        fuelUomDescriptionFr
+        fuelGradeDescription
+        fuelGradeDescriptionFr
+      }
+      tenderArray {
+        tenderTypeCode
+        tenderSubTypeCode
+        tenderDescription
+        amountTender
+        displayAccountNumber
+        sequenceNumber
+        approvalNumber
+        responseCode
+        tenderTypeName
+        transactionID
+        merchantID
+        entryMethod
+        tenderAcctTxnNumber
+        tenderAuthorizationCode
+        tenderTypeNameFr
+        tenderEntryMethodDescription
+        walletType
+        walletId
+        storedValueBucket
+      }
+      subTaxes {
+        tax1
+        tax2
+        tax3
+        tax4
+        aTaxPercent
+        aTaxLegend
+        aTaxAmount
+        aTaxPrintCode
+        aTaxPrintCodeFR
+        aTaxIdentifierCode
+        bTaxPercent
+        bTaxLegend
+        bTaxAmount
+        bTaxPrintCode
+        bTaxPrintCodeFR
+        bTaxIdentifierCode
+        cTaxPercent
+        cTaxLegend
+        cTaxAmount
+        cTaxIdentifierCode
+        dTaxPercent
+        dTaxLegend
+        dTaxAmount
+        dTaxPrintCode
+        dTaxPrintCodeFR
+        dTaxIdentifierCode
+        uTaxLegend
+        uTaxAmount
+        uTaxableAmount
+      }
+      instantSavings
+      membershipNumber
+    }
+  }
+}
+""".strip()
+
+ORDER_FIELDS = [
+    "retailer",
+    "order_id",
+    "order_date",
+    "delivery_date",
+    "service_type",
+    "order_total",
+    "payment_method",
+    "total_item_count",
+    "total_savings",
+    "your_savings_total",
+    "coupons_discounts_total",
+    "store_name",
+    "store_number",
+    "store_address1",
+    "store_city",
+    "store_state",
+    "store_zipcode",
+    "refund_order",
+    "ebt_order",
+    "raw_history_path",
+    "raw_order_path",
+]
+
+ITEM_FIELDS = [
+    "retailer",
+    "order_id",
+    "line_no",
+    "order_date",
+    "retailer_item_id",
+    "pod_id",
+    "item_name",
+    "upc",
+    "category_id",
+    "category",
+    "qty",
+    "unit",
+    "unit_price",
+    "line_total",
+    "picked_weight",
+    "mvp_savings",
+    "reward_savings",
+    "coupon_savings",
+    "coupon_price",
+    "image_url",
+    "raw_order_path",
+    "is_discount_line",
+    "is_coupon_line",
+]
+
+COSTCO_STORAGE_ORIGIN = "costco.com"
+COSTCO_ID_TOKEN_STORAGE_KEY = "idToken"
+COSTCO_CLIENT_ID_STORAGE_KEY = "clientID"
+
+def load_config():
+    load_dotenv()
+    return {
+        "authorization": os.getenv("COSTCO_X_AUTHORIZATION", "").strip(),
+        "client_id": os.getenv("COSTCO_X_WCS_CLIENTID", "").strip(),
+        "client_identifier": os.getenv("COSTCO_CLIENT_IDENTIFIER", "").strip(),
+    }
+
+
+def build_headers(auth_headers):
+    headers = {
+        "accept": "*/*",
+        "content-type": "application/json-patch+json",
+        "costco.service": "restOrders",
+        "costco.env": "ecom",
+        "origin": "https://www.costco.com",
+        "referer": "https://www.costco.com/",
+        "user-agent": (
+            "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:148.0) "
+            "Gecko/20100101 Firefox/148.0"
+        ),
+    }
+    headers.update(auth_headers)
+    return headers
+
+
+def load_costco_browser_headers(profile_dir, authorization, client_id, client_identifier):
+    local_storage = read_firefox_local_storage(profile_dir, COSTCO_STORAGE_ORIGIN)
+    webapps_store = read_firefox_webapps_store(profile_dir, COSTCO_STORAGE_ORIGIN)
+    auth_header = authorization.strip() if authorization else ""
+    if client_id:
+        client_id = client_id.strip()
+    if client_identifier:
+        client_identifier = client_identifier.strip()
+
+    if not auth_header:
+        id_token = (
+            local_storage.get(COSTCO_ID_TOKEN_STORAGE_KEY, "").strip()
+            or webapps_store.get(COSTCO_ID_TOKEN_STORAGE_KEY, "").strip()
+        )
+        if id_token:
+            auth_header = f"Bearer {id_token}"
+
+    client_id = client_id or (
+        local_storage.get(COSTCO_CLIENT_ID_STORAGE_KEY, "").strip()
+        or webapps_store.get(COSTCO_CLIENT_ID_STORAGE_KEY, "").strip()
+    )
+
+    if not auth_header:
+        raise click.ClickException(
+            "could not find Costco auth token; set COSTCO_X_AUTHORIZATION or load Firefox idToken"
+        )
+    if not client_id or not client_identifier:
+        raise click.ClickException(
+            "missing Costco client ids; set COSTCO_X_WCS_CLIENTID and COSTCO_CLIENT_IDENTIFIER"
+        )
+
+    return {
+        "costco-x-authorization": auth_header,
+        "costco-x-wcs-clientId": client_id,
+        "client-identifier": client_identifier,
+    }
+
+
+def build_session(profile_dir, auth_headers):
+    session = requests.Session()
+    session.cookies.update(load_firefox_cookies(".costco.com", profile_dir))
+    session.headers.update(build_headers(auth_headers))
+    session.headers.update(auth_headers)
+    return session
+
+
+def graphql_post(session, query, variables):
+    last_response = None
+
+    for attempt in range(3):
+        try:
+            response = session.post(
+                BASE_URL,
+                json={"query": query, "variables": variables},
+                impersonate="firefox",
+                timeout=30,
+            )
+            last_response = response
+            if response.status_code == 200:
+                return response.json()
+            click.echo(f"retry {attempt + 1}/3 status={response.status_code} body={response.text[:500]}")
+        except Exception as exc:  # pragma: no cover - network error path
+            click.echo(f"retry {attempt + 1}/3 error={exc}")
+        time.sleep(3)
+
+    if last_response is not None:
+        last_response.raise_for_status()
+
+    raise RuntimeError("failed to fetch Costco GraphQL payload")
+
+def safe_filename(value):
+    return re.sub(r'[<>:"/\\|?*]+', "-", str(value))
+
+def summary_receipts(payload):
+    return payload.get("data", {}).get("receiptsWithCounts", {}).get("receipts", [])
+
+
+def detail_receipts(payload):
+    return payload.get("data", {}).get("receiptsWithCounts", {}).get("receipts", [])
+
+
+def summary_counts(payload):
+    counts = payload.get("data", {}).get("receiptsWithCounts", {})
+    return {
+        "inWarehouse": counts.get("inWarehouse", 0) or 0,
+        "gasStation": counts.get("gasStation", 0) or 0,
+        "carWash": counts.get("carWash", 0) or 0,
+        "gasAndCarWash": counts.get("gasAndCarWash", 0) or 0,
+    }
+
+
+def parse_cli_date(value):
+    return datetime.strptime(value, "%m/%d/%Y").date()
+
+
+def format_cli_date(value):
+    return f"{value.month}/{value.day:02d}/{value.year}"
+
+
+def subtract_months(value, months):
+    year = value.year
+    month = value.month - months
+    while month <= 0:
+        month += 12
+        year -= 1
+    day = min(value.day, monthrange(year, month)[1])
+    return value.replace(year=year, month=month, day=day)
+
+
+def resolve_date_range(months_back, today=None):
+    if months_back < 1:
+        raise click.ClickException("months-back must be at least 1")
+
+    end = today or datetime.now().date()
+    start = subtract_months(end, months_back)
+    return format_cli_date(start), format_cli_date(end)
+
+
+def build_date_windows(start_date, end_date, window_days):
+    start = parse_cli_date(start_date)
+    end = parse_cli_date(end_date)
+    if end < start:
+        raise click.ClickException("end-date must be on or after start-date")
+    if window_days < 1:
+        raise click.ClickException("window-days must be at least 1")
+
+    windows = []
+    current = start
+    while current <= end:
+        window_end = min(current + timedelta(days=window_days - 1), end)
+        windows.append(
+            {
+                "startDate": format_cli_date(current),
+                "endDate": format_cli_date(window_end),
+            }
+        )
+        current = window_end + timedelta(days=1)
+    return windows
+
+
+def unique_receipts(receipts):
+    by_barcode = {}
+    for receipt in receipts:
+        key = receipt_key(receipt)
+        if key:
+            by_barcode[key] = receipt
+    return list(by_barcode.values())
+
+
+def receipt_key(receipt):
+    barcode = receipt.get("transactionBarcode", "")
+    transaction_date_time = receipt.get("transactionDateTime", "")
+    if not barcode:
+        return ""
+    return f"{barcode}::{transaction_date_time}"
+
+
+def fetch_summary_windows(
+    session,
+    start_date,
+    end_date,
+    document_type,
+    document_sub_type,
+    window_days,
+):
+    requests_metadata = []
+    combined_receipts = []
+
+    for window in build_date_windows(start_date, end_date, window_days):
+        variables = {
+            "startDate": window["startDate"],
+            "endDate": window["endDate"],
+            "text": "custom",
+            "documentType": document_type,
+            "documentSubType": document_sub_type,
+        }
+        payload = graphql_post(session, SUMMARY_QUERY, variables)
+        receipts = summary_receipts(payload)
+        counts = summary_counts(payload)
+        warehouse_count = sum(
+            1 for receipt in receipts if receipt.get("receiptType") == "In-Warehouse"
+        )
+        mismatch = counts["inWarehouse"] != warehouse_count
+        requests_metadata.append(
+            {
+                **variables,
+                "returnedReceipts": len(receipts),
+                "returnedInWarehouseReceipts": warehouse_count,
+                "inWarehouse": counts["inWarehouse"],
+                "gasStation": counts["gasStation"],
+                "carWash": counts["carWash"],
+                "gasAndCarWash": counts["gasAndCarWash"],
+                "countMismatch": mismatch,
+            }
+        )
+        if mismatch:
+            click.echo(
+                (
+                    "warning: summary count mismatch for "
+                    f"{window['startDate']} to {window['endDate']}: "
+                    f"inWarehouse={counts['inWarehouse']} "
+                    f"returnedInWarehouseReceipts={warehouse_count}"
+                ),
+                err=True,
+            )
+        combined_receipts.extend(receipts)
+
+    unique = unique_receipts(combined_receipts)
+    aggregate_payload = {
+        "data": {
+            "receiptsWithCounts": {
+                "inWarehouse": sum(row["inWarehouse"] for row in requests_metadata),
+                "gasStation": sum(row["gasStation"] for row in requests_metadata),
+                "carWash": sum(row["carWash"] for row in requests_metadata),
+                "gasAndCarWash": sum(row["gasAndCarWash"] for row in requests_metadata),
+                "receipts": unique,
+            }
+        }
+    }
+    return aggregate_payload, requests_metadata
+
+
+def flatten_costco_data(summary_payload, detail_payloads, raw_dir):
+    summary_lookup = {
+        receipt_key(receipt): receipt
+        for receipt in summary_receipts(summary_payload)
+        if receipt_key(receipt)
+    }
+    orders = []
+    items = []
+
+    for detail_payload in detail_payloads:
+        for receipt in detail_receipts(detail_payload):
+            order_id = receipt["transactionBarcode"]
+            receipt_id = receipt_key(receipt)
+            summary_row = summary_lookup.get(receipt_id, {})
+            coupon_numbers = {
+                row.get("upcnumberCoupon", "")
+                for row in summary_row.get("couponArray", []) or []
+                if row.get("upcnumberCoupon")
+            }
+            raw_order_path = raw_dir / f"{safe_filename(receipt_id or order_id)}.json"
+
+            orders.append(
+                {
+                    "retailer": RETAILER,
+                    "order_id": order_id,
+                    "order_date": receipt.get("transactionDate", ""),
+                    "delivery_date": receipt.get("transactionDate", ""),
+                    "service_type": receipt.get("receiptType", ""),
+                    "order_total": stringify(receipt.get("total")),
+                    "payment_method": compact_join(
+                        summary_row.get("tenderArray", []) or [], "tenderDescription"
+                    ),
+                    "total_item_count": stringify(receipt.get("totalItemCount")),
+                    "total_savings": stringify(receipt.get("instantSavings")),
+                    "your_savings_total": stringify(receipt.get("instantSavings")),
+                    "coupons_discounts_total": stringify(receipt.get("instantSavings")),
+                    "store_name": receipt.get("warehouseName", ""),
+                    "store_number": stringify(receipt.get("warehouseNumber")),
+                    "store_address1": receipt.get("warehouseAddress1", ""),
+                    "store_city": receipt.get("warehouseCity", ""),
+                    "store_state": receipt.get("warehouseState", ""),
+                    "store_zipcode": receipt.get("warehousePostalCode", ""),
+                    "refund_order": "false",
+                    "ebt_order": "false",
+                    "raw_history_path": (raw_dir / "summary.json").as_posix(),
+                    "raw_order_path": raw_order_path.as_posix(),
+                }
+            )
+
+            for line_no, item in enumerate(receipt.get("itemArray", []), start=1):
+                item_number = stringify(item.get("itemNumber"))
+                description = join_descriptions(
+                    item.get("itemDescription01"), item.get("itemDescription02")
+                )
+                is_discount = is_discount_line(item)
+                is_coupon = is_discount and (
+                    item_number in coupon_numbers
+                    or description.startswith("/")
+                )
+
+                items.append(
+                    {
+                        "retailer": RETAILER,
+                        "order_id": order_id,
+                        "line_no": str(line_no),
+                        "order_date": receipt.get("transactionDate", ""),
+                        "retailer_item_id": item_number,
+                        "pod_id": "",
+                        "item_name": description,
+                        "upc": "",
+                        "category_id": stringify(item.get("itemDepartmentNumber")),
+                        "category": stringify(item.get("transDepartmentNumber")),
+                        "qty": stringify(item.get("unit")),
+                        "unit": stringify(item.get("itemIdentifier")),
+                        "unit_price": stringify(item.get("itemUnitPriceAmount")),
+                        "line_total": stringify(item.get("amount")),
+                        "picked_weight": "",
+                        "mvp_savings": "",
+                        "reward_savings": "",
+                        "coupon_savings": stringify(item.get("amount") if is_coupon else ""),
+                        "coupon_price": "",
+                        "image_url": "",
+                        "raw_order_path": raw_order_path.as_posix(),
+                        "is_discount_line": "true" if is_discount else "false",
+                        "is_coupon_line": "true" if is_coupon else "false",
+                    }
+                )
+
+    return orders, items
+
+
+def join_descriptions(*parts):
+    return " ".join(str(part).strip() for part in parts if part).strip()
+
+
+def compact_join(rows, field):
+    values = [str(row.get(field, "")).strip() for row in rows if row.get(field)]
+    return " | ".join(values)
+
+
+def is_discount_line(item):
+    amount = item.get("amount")
+    unit = item.get("unit")
+    description = join_descriptions(
+        item.get("itemDescription01"), item.get("itemDescription02")
+    )
+    try:
+        amount_val = float(amount)
+    except (TypeError, ValueError):
+        amount_val = 0.0
+    try:
+        unit_val = float(unit)
+    except (TypeError, ValueError):
+        unit_val = 0.0
+    return amount_val < 0 or unit_val < 0 or description.startswith("/")
+
+
+def stringify(value):
+    if value is None:
+        return ""
+    return str(value)
+
+
+def write_json(path, payload):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+
+
+def write_csv(path, rows, fieldnames):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+@click.command()
+@click.option(
+    "--outdir",
+    default="costco_output",
+    show_default=True,
+    help="Output directory for Costco raw and flattened files.",
+)
+@click.option(
+    "--document-type",
+    default="all",
+    show_default=True,
+    help="Summary document type.",
+)
+@click.option(
+    "--document-sub-type",
+    default="all",
+    show_default=True,
+    help="Summary document sub type.",
+)
+@click.option(
+    "--window-days",
+    default=92,
+    show_default=True,
+    type=int,
+    help="Maximum number of days to request per summary window.",
+)
+@click.option(
+    "--months-back",
+    default=36,
+    show_default=True,
+    type=int,
+    help="How many months of receipts to enumerate back from today.",
+)
+@click.option(
+    "--firefox-profile-dir",
+    default=None,
+    help="Firefox profile directory to use for cookies and session storage.",
+)
+def main(
+    outdir,
+    document_type,
+    document_sub_type,
+    window_days,
+    months_back,
+    firefox_profile_dir,
+):
+    click.echo("legacy entrypoint: prefer collect_costco_web.py for data-model outputs")
+    run_collection(
+        outdir=outdir,
+        document_type=document_type,
+        document_sub_type=document_sub_type,
+        window_days=window_days,
+        months_back=months_back,
+        firefox_profile_dir=firefox_profile_dir,
+    )
+
+
+def run_collection(
+    outdir,
+    document_type,
+    document_sub_type,
+    window_days,
+    months_back,
+    firefox_profile_dir,
+    orders_filename="orders.csv",
+    items_filename="items.csv",
+):
+    outdir = Path(outdir)
+    raw_dir = outdir / "raw"
+    config = load_config()
+
+    profile_dir = Path(firefox_profile_dir) if firefox_profile_dir else None
+    if profile_dir is None:
+        try:
+            profile_dir = find_firefox_profile_dir()
+        except Exception:
+            profile_dir = click.prompt(
+                "Firefox profile dir",
+                type=click.Path(exists=True, file_okay=False, path_type=Path),
+            )
+
+    auth_headers = load_costco_browser_headers(
+        profile_dir,
+        authorization=config["authorization"],
+        client_id=config["client_id"],
+        client_identifier=config["client_identifier"],
+    )
+    session = build_session(profile_dir, auth_headers)
+    click.echo(
+        "session bootstrap: "
+        f"cookies={True} "
+        f"authorization={bool(auth_headers.get('costco-x-authorization'))} "
+        f"client_id={bool(auth_headers.get('costco-x-wcs-clientId'))} "
+        f"client_identifier={bool(auth_headers.get('client-identifier'))}"
+    )
+
+    start_date, end_date = resolve_date_range(months_back)
+
+    summary_payload, request_metadata = fetch_summary_windows(
+        session,
+        start_date,
+        end_date,
+        document_type,
+        document_sub_type,
+        window_days,
+    )
+    write_json(raw_dir / "summary.json", summary_payload)
+    write_json(raw_dir / "summary_requests.json", request_metadata)
+    receipts = summary_receipts(summary_payload)
+
+    detail_payloads = []
+    for receipt in receipts:
+        barcode = receipt["transactionBarcode"]
+        receipt_id = receipt_key(receipt) or barcode
+        click.echo(f"fetching {barcode}")
+        detail_payload = graphql_post(
+            session,
+            DETAIL_QUERY,
+            {"barcode": barcode, "documentType": "warehouse"},
+        )
+        detail_payloads.append(detail_payload)
+        write_json(raw_dir / f"{safe_filename(receipt_id)}.json", detail_payload)
+
+    orders, items = flatten_costco_data(summary_payload, detail_payloads, raw_dir)
+    write_csv(outdir / orders_filename, orders, ORDER_FIELDS)
+    write_csv(outdir / items_filename, items, ITEM_FIELDS)
+    click.echo(f"wrote {len(orders)} orders and {len(items)} item rows to {outdir}")
+
+
+if __name__ == "__main__":
+    main()
+
+ 

scrape_giant.py

@@ -0,0 +1,367 @@
+import csv
+import json
+import os
+import time
+from pathlib import Path
+
+import click
+from dotenv import load_dotenv
+from curl_cffi import requests
+
+from browser_session import find_firefox_profile_dir, load_firefox_cookies
+
+
+BASE = "https://giantfood.com"
+ACCOUNT_PAGE = f"{BASE}/account/history/invoice/in-store"
+RETAILER = "giant"
+
+ORDER_FIELDS = [
+    "retailer",
+    "order_id",
+    "order_date",
+    "delivery_date",
+    "service_type",
+    "order_total",
+    "payment_method",
+    "total_item_count",
+    "total_savings",
+    "your_savings_total",
+    "coupons_discounts_total",
+    "store_name",
+    "store_number",
+    "store_address1",
+    "store_city",
+    "store_state",
+    "store_zipcode",
+    "refund_order",
+    "ebt_order",
+    "raw_history_path",
+    "raw_order_path",
+]
+
+ITEM_FIELDS = [
+    "retailer",
+    "order_id",
+    "order_date",
+    "line_no",
+    "retailer_item_id",
+    "pod_id",
+    "item_name",
+    "upc",
+    "category_id",
+    "category",
+    "qty",
+    "unit",
+    "unit_price",
+    "line_total",
+    "picked_weight",
+    "mvp_savings",
+    "reward_savings",
+    "coupon_savings",
+    "coupon_price",
+    "image_url",
+    "raw_order_path",
+    "is_discount_line",
+    "is_coupon_line",
+]
+
+
+def load_config():
+    if load_dotenv is not None:
+        load_dotenv()
+
+    return {
+        "user_id": os.getenv("GIANT_USER_ID", "").strip(),
+        "loyalty": os.getenv("GIANT_LOYALTY_NUMBER", "").strip(),
+    }
+
+
+def build_session():
+    profile_dir = find_firefox_profile_dir()
+    session = requests.Session()
+    session.cookies.update(load_firefox_cookies("giantfood.com", profile_dir))
+    session.headers.update(
+        {
+            "user-agent": (
+                "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:148.0) "
+                "Gecko/20100101 Firefox/148.0"
+            ),
+            "accept": "application/json, text/plain, */*",
+            "accept-language": "en-US,en;q=0.9",
+            "referer": ACCOUNT_PAGE,
+        }
+    )
+    return session
+
+
+def safe_get(session, url, **kwargs):
+    last_response = None
+
+    for attempt in range(3):
+        try:
+            response = session.get(
+                url,
+                impersonate="firefox",
+                timeout=30,
+                **kwargs,
+            )
+            last_response = response
+
+            if response.status_code == 200:
+                return response
+
+            click.echo(f"retry {attempt + 1}/3 status={response.status_code}")
+        except Exception as exc:  # pragma: no cover - network error path
+            click.echo(f"retry {attempt + 1}/3 error={exc}")
+
+        time.sleep(3)
+
+    if last_response is not None:
+        last_response.raise_for_status()
+
+    raise RuntimeError(f"failed to fetch {url}")
+
+
+def get_history(session, user_id, loyalty):
+    response = safe_get(
+        session,
+        f"{BASE}/api/v6.0/user/{user_id}/order/history",
+        params={"filter": "instore", "loyaltyNumber": loyalty},
+    )
+    return response.json()
+
+
+def get_order_detail(session, user_id, order_id):
+    response = safe_get(
+        session,
+        f"{BASE}/api/v6.0/user/{user_id}/order/history/detail/{order_id}",
+        params={"isInStore": "true"},
+    )
+    return response.json()
+
+
+def flatten_orders(history, details, history_path=None, raw_dir=None):
+    orders = []
+    items = []
+    history_lookup = {record["orderId"]: record for record in history.get("records", [])}
+    history_path_value = history_path.as_posix() if history_path else ""
+
+    for detail in details:
+        order_id = str(detail["orderId"])
+        history_row = history_lookup.get(detail["orderId"], {})
+        pickup = detail.get("pup", {})
+        raw_order_path = (raw_dir / f"{order_id}.json").as_posix() if raw_dir else ""
+
+        orders.append(
+            {
+                "retailer": RETAILER,
+                "order_id": order_id,
+                "order_date": detail.get("orderDate"),
+                "delivery_date": detail.get("deliveryDate"),
+                "service_type": history_row.get("serviceType"),
+                "order_total": detail.get("orderTotal"),
+                "payment_method": detail.get("paymentMethod"),
+                "total_item_count": detail.get("totalItemCount"),
+                "total_savings": detail.get("totalSavings"),
+                "your_savings_total": detail.get("yourSavingsTotal"),
+                "coupons_discounts_total": detail.get("couponsDiscountsTotal"),
+                "store_name": pickup.get("storeName"),
+                "store_number": pickup.get("aholdStoreNumber"),
+                "store_address1": pickup.get("storeAddress1"),
+                "store_city": pickup.get("storeCity"),
+                "store_state": pickup.get("storeState"),
+                "store_zipcode": pickup.get("storeZipcode"),
+                "refund_order": detail.get("refundOrder"),
+                "ebt_order": detail.get("ebtOrder"),
+                "raw_history_path": history_path_value,
+                "raw_order_path": raw_order_path,
+            }
+        )
+
+        for line_no, item in enumerate(detail.get("items", []), start=1):
+            items.append(
+                {
+                    "retailer": RETAILER,
+                    "order_id": order_id,
+                    "order_date": detail.get("orderDate"),
+                    "line_no": str(line_no),
+                    "retailer_item_id": "",
+                    "pod_id": item.get("podId"),
+                    "item_name": item.get("itemName"),
+                    "upc": item.get("primUpcCd"),
+                    "category_id": item.get("categoryId"),
+                    "category": item.get("categoryDesc"),
+                    "qty": item.get("shipQy"),
+                    "unit": item.get("lbEachCd"),
+                    "unit_price": item.get("unitPrice"),
+                    "line_total": item.get("groceryAmount"),
+                    "picked_weight": item.get("totalPickedWeight"),
+                    "mvp_savings": item.get("mvpSavings"),
+                    "reward_savings": item.get("rewardSavings"),
+                    "coupon_savings": item.get("couponSavings"),
+                    "coupon_price": item.get("couponPrice"),
+                    "image_url": "",
+                    "raw_order_path": raw_order_path,
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                }
+            )
+
+    return orders, items
+
+
+def normalize_row(row, fieldnames):
+    return {field: stringify(row.get(field)) for field in fieldnames}
+
+
+def stringify(value):
+    if value is None:
+        return ""
+    return str(value)
+
+
+def read_csv_rows(path):
+    if not path.exists():
+        return [], []
+
+    with path.open(newline="", encoding="utf-8") as handle:
+        reader = csv.DictReader(handle)
+        fieldnames = reader.fieldnames or []
+        return fieldnames, list(reader)
+
+
+def read_existing_order_ids(path):
+    _, rows = read_csv_rows(path)
+    return {row["order_id"] for row in rows if row.get("order_id")}
+
+
+def merge_rows(existing_rows, new_rows, subset):
+    merged = []
+    row_index = {}
+
+    for row in existing_rows + new_rows:
+        key = tuple(stringify(row.get(field)) for field in subset)
+        normalized = dict(row)
+        if key in row_index:
+            merged[row_index[key]] = normalized
+        else:
+            row_index[key] = len(merged)
+            merged.append(normalized)
+
+    return merged
+
+
+def append_dedup(path, new_rows, subset, fieldnames):
+    existing_fieldnames, existing_rows = read_csv_rows(path)
+    all_fieldnames = list(dict.fromkeys(existing_fieldnames + fieldnames))
+
+    merged = merge_rows(
+        [normalize_row(row, all_fieldnames) for row in existing_rows],
+        [normalize_row(row, all_fieldnames) for row in new_rows],
+        subset=subset,
+    )
+
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=all_fieldnames)
+        writer.writeheader()
+        writer.writerows(merged)
+
+    return merged
+
+
+def write_json(path, payload):
+    path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+
+
+@click.command()
+@click.option("--user-id", default=None, help="Giant user id.")
+@click.option("--loyalty", default=None, help="Giant loyalty number.")
+@click.option(
+    "--outdir",
+    default="giant_output",
+    show_default=True,
+    help="Directory for raw json and csv outputs.",
+)
+@click.option(
+    "--sleep-seconds",
+    default=1.5,
+    show_default=True,
+    type=float,
+    help="Delay between order detail requests.",
+)
+def main(user_id, loyalty, outdir, sleep_seconds):
+    click.echo("legacy entrypoint: prefer collect_giant_web.py for data-model outputs")
+    run_collection(user_id, loyalty, outdir, sleep_seconds)
+
+
+def run_collection(
+    user_id,
+    loyalty,
+    outdir,
+    sleep_seconds,
+    orders_filename="orders.csv",
+    items_filename="items.csv",
+):
+    config = load_config()
+    user_id = user_id or config["user_id"] or click.prompt("Giant user id", type=str)
+    loyalty = loyalty or config["loyalty"] or click.prompt(
+        "Giant loyalty number", type=str
+    )
+
+    outdir = Path(outdir)
+    rawdir = outdir / "raw"
+    rawdir.mkdir(parents=True, exist_ok=True)
+
+    orders_csv = outdir / orders_filename
+    items_csv = outdir / items_filename
+    existing_order_ids = read_existing_order_ids(orders_csv)
+
+    session = build_session()
+    history = get_history(session, user_id, loyalty)
+    history_path = rawdir / "history.json"
+    write_json(history_path, history)
+
+    records = history.get("records", [])
+    click.echo(f"history returned {len(records)} visits; Giant exposes only the most recent 50")
+
+    unseen_records = [
+        record
+        for record in records
+        if stringify(record.get("orderId")) not in existing_order_ids
+    ]
+    click.echo(
+        f"found {len(unseen_records)} unseen visits "
+        f"({len(existing_order_ids)} already stored)"
+    )
+
+    details = []
+    for index, record in enumerate(unseen_records, start=1):
+        order_id = stringify(record.get("orderId"))
+        click.echo(f"[{index}/{len(unseen_records)}] fetching {order_id}")
+        detail = get_order_detail(session, user_id, order_id)
+        write_json(rawdir / f"{order_id}.json", detail)
+        details.append(detail)
+        if index < len(unseen_records):
+            time.sleep(sleep_seconds)
+
+    orders, items = flatten_orders(history, details, history_path=history_path, raw_dir=rawdir)
+    merged_orders = append_dedup(
+        orders_csv,
+        orders,
+        subset=["order_id"],
+        fieldnames=ORDER_FIELDS,
+    )
+    merged_items = append_dedup(
+        items_csv,
+        items,
+        subset=["order_id", "line_no"],
+        fieldnames=ITEM_FIELDS,
+    )
+    click.echo(
+        f"wrote {len(orders)} new orders / {len(items)} new items "
+        f"({len(merged_orders)} total orders, {len(merged_items)} total items)"
+    )
+
+
+if __name__ == "__main__":
+    main()

scraper.py

@@ -1,181 +1,5 @@
-import json
+from scrape_giant import *  # noqa: F401,F403
-import time
-from pathlib import Path
-
-import browser_cookie3
-import pandas as pd
-from curl_cffi import requests
-
-
-BASE = "https://giantfood.com"
-ACCOUNT_PAGE = f"{BASE}/account/history/invoice/in-store"
-
-USER_ID = "369513017"
-LOYALTY = "440155630880"
-
-
-def build_session():
-    s = requests.Session()
-    s.cookies.update(browser_cookie3.firefox(domain_name="giantfood.com"))
-    s.headers.update({
-        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:148.0) Gecko/20100101 Firefox/148.0",
-        "accept": "application/json, text/plain, */*",
-        "accept-language": "en-US,en;q=0.9",
-        "referer": ACCOUNT_PAGE,
-    })
-    return s
-
-
-def safe_get(session, url, **kwargs):
-    last_response = None
-
-    for attempt in range(3):
-        try:
-            r = session.get(
-                url,
-                impersonate="firefox",
-                timeout=30,
-                **kwargs,
-            )
-            last_response = r
-
-            if r.status_code == 200:
-                return r
-
-            print(f"retry {attempt + 1}/3 status={r.status_code}")
-        except Exception as e:
-            print(f"retry {attempt + 1}/3 error={e}")
-
-        time.sleep(3)
-
-    if last_response is not None:
-        last_response.raise_for_status()
-
-    raise RuntimeError(f"failed to fetch {url}")
-
-
-def get_history(session):
-    url = f"{BASE}/api/v6.0/user/{USER_ID}/order/history"
-    r = safe_get(
-        session,
-        url,
-        params={
-            "filter": "instore",
-            "loyaltyNumber": LOYALTY,
-        },
-    )
-    return r.json()
-
-
-def get_order_detail(session, order_id):
-    url = f"{BASE}/api/v6.0/user/{USER_ID}/order/history/detail/{order_id}"
-    r = safe_get(
-        session,
-        url,
-        params={"isInStore": "true"},
-    )
-    return r.json()
-
-
-def flatten_orders(history, details):
-    orders = []
-    items = []
-
-    history_lookup = {
-        r["orderId"]: r
-        for r in history.get("records", [])
-    }
-
-    for d in details:
-        hist = history_lookup.get(d["orderId"], {})
-        pup = d.get("pup", {})
-
-        orders.append({
-            "order_id": d["orderId"],
-            "order_date": d.get("orderDate"),
-            "delivery_date": d.get("deliveryDate"),
-            "service_type": hist.get("serviceType"),
-            "order_total": d.get("orderTotal"),
-            "payment_method": d.get("paymentMethod"),
-            "total_item_count": d.get("totalItemCount"),
-            "total_savings": d.get("totalSavings"),
-            "your_savings_total": d.get("yourSavingsTotal"),
-            "coupons_discounts_total": d.get("couponsDiscountsTotal"),
-            "store_name": pup.get("storeName"),
-            "store_number": pup.get("aholdStoreNumber"),
-            "store_address1": pup.get("storeAddress1"),
-            "store_city": pup.get("storeCity"),
-            "store_state": pup.get("storeState"),
-            "store_zipcode": pup.get("storeZipcode"),
-            "refund_order": d.get("refundOrder"),
-            "ebt_order": d.get("ebtOrder"),
-        })
-
-        for i, item in enumerate(d.get("items", []), start=1):
-            items.append({
-                "order_id": d["orderId"],
-                "order_date": d.get("orderDate"),
-                "line_no": i,
-                "pod_id": item.get("podId"),
-                "item_name": item.get("itemName"),
-                "upc": item.get("primUpcCd"),
-                "category_id": item.get("categoryId"),
-                "category": item.get("categoryDesc"),
-                "qty": item.get("shipQy"),
-                "unit": item.get("lbEachCd"),
-                "unit_price": item.get("unitPrice"),
-                "line_total": item.get("groceryAmount"),
-                "picked_weight": item.get("totalPickedWeight"),
-                "mvp_savings": item.get("mvpSavings"),
-                "reward_savings": item.get("rewardSavings"),
-                "coupon_savings": item.get("couponSavings"),
-                "coupon_price": item.get("couponPrice"),
-            })
-
-    return pd.DataFrame(orders), pd.DataFrame(items)
-
-
-def main():
-    outdir = Path("giant_output")
-    rawdir = outdir / "raw"
-    rawdir.mkdir(parents=True, exist_ok=True)
-
-    session = build_session()
-
-    print("fetching order history...")
-    history = get_history(session)
-
-    (rawdir / "history.json").write_text(
-        json.dumps(history, indent=2),
-        encoding="utf-8",
-    )
-
-    order_ids = [r["orderId"] for r in history.get("records", [])]
-    print(f"{len(order_ids)} orders found")
-
-    details = []
-    for order_id in order_ids:
-        print(f"fetching {order_id}")
-        d = get_order_detail(session, order_id)
-        details.append(d)
-
-        (rawdir / f"{order_id}.json").write_text(
-            json.dumps(d, indent=2),
-            encoding="utf-8",
-        )
-
-        time.sleep(1.5)
-
-    print("flattening data...")
-    orders_df, items_df = flatten_orders(history, details)
-
-    orders_df.to_csv(outdir / "orders.csv", index=False)
-    items_df.to_csv(outdir / "items.csv", index=False)
-
-    print("done")
-    print(f"{len(orders_df)} orders written to {outdir / 'orders.csv'}")
-    print(f"{len(items_df)} items written to {outdir / 'items.csv'}")
 
 
 if __name__ == "__main__":
-    main()
+    main()

tests/test_bc.py

@@ -1,28 +1,17 @@
-import requests
+import unittest
-import browser_cookie3
 
-BASE = "https://giantfood.com"
-ACCOUNT_PAGE = f"{BASE}/account/history/invoice/in-store"
 
-USER_ID = "369513017"
+try:
-LOYALTY = "440155630880"
+    import browser_cookie3  # noqa: F401
+    import requests  # noqa: F401
+except ImportError as exc:  # pragma: no cover - dependency-gated smoke test
+    browser_cookie3 = None
+    _IMPORT_ERROR = exc
+else:
+    _IMPORT_ERROR = None
 
-cj = browser_cookie3.firefox(domain_name="giantfood.com")
 
-s = requests.Session()
+@unittest.skipIf(browser_cookie3 is None, f"optional smoke test dependency missing: {_IMPORT_ERROR}")
-s.cookies.update(cj)
+class BrowserCookieSmokeTest(unittest.TestCase):
-s.headers.update({
+    def test_dependencies_available(self):
-    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:148.0) Gecko/20100101 Firefox/148.0",
+        self.assertIsNotNone(browser_cookie3)
-    "accept": "application/json, text/plain, */*",
-    "accept-language": "en-US,en;q=0.9",
-    "referer": ACCOUNT_PAGE,
-})
-
-r = s.get(
-    f"{BASE}/api/v6.0/user/{USER_ID}/order/history",
-    params={"filter": "instore", "loyaltyNumber": LOYALTY},
-    timeout=30,
-)
-
-print(r.status_code)
-print(r.text[:500])

tests/test_bc_cffi.py

@@ -1,27 +1,17 @@
-import browser_cookie3
+import unittest
-from curl_cffi import requests
 
-BASE = "https://giantfood.com"
-ACCOUNT_PAGE = f"{BASE}/account/history/invoice/in-store"
 
-USER_ID = "369513017"
+try:
-LOYALTY = "440155630880"
+    import browser_cookie3  # noqa: F401
+    from curl_cffi import requests  # noqa: F401
+except ImportError as exc:  # pragma: no cover - dependency-gated smoke test
+    browser_cookie3 = None
+    _IMPORT_ERROR = exc
+else:
+    _IMPORT_ERROR = None
 
-s = requests.Session()
-s.cookies.update(browser_cookie3.firefox(domain_name="giantfood.com"))
-s.headers.update({
-    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:148.0) Gecko/20100101 Firefox/148.0",
-    "accept": "application/json, text/plain, */*",
-    "accept-language": "en-US,en;q=0.9",
-    "referer": ACCOUNT_PAGE,
-})
 
-r = s.get(
+@unittest.skipIf(browser_cookie3 is None, f"optional smoke test dependency missing: {_IMPORT_ERROR}")
-    f"{BASE}/api/v6.0/user/{USER_ID}/order/history",
+class CurlCffiSmokeTest(unittest.TestCase):
-    params={"filter": "instore", "loyaltyNumber": LOYALTY},
+    def test_dependencies_available(self):
-    impersonate="firefox",
+        self.assertIsNotNone(browser_cookie3)
-    timeout=30,
-)
-
-print(r.status_code)
-print(r.text[:500])

tests/test_browser_session.py

@@ -0,0 +1,155 @@
+import sqlite3
+import tempfile
+import unittest
+from pathlib import Path
+from unittest import mock
+
+import browser_session
+import scrape_costco
+
+
+class BrowserSessionTests(unittest.TestCase):
+    def test_read_firefox_local_storage_reads_copied_sqlite(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            profile_dir = Path(tmpdir) / "abcd.default-release"
+            ls_dir = profile_dir / "storage" / "default" / "https+++www.costco.com" / "ls"
+            ls_dir.mkdir(parents=True)
+            db_path = ls_dir / "data.sqlite"
+
+            with sqlite3.connect(db_path) as connection:
+                connection.execute("CREATE TABLE data (key TEXT, value TEXT)")
+                connection.execute(
+                    "INSERT INTO data (key, value) VALUES (?, ?)",
+                    ("costco-x-wcs-clientId", "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf"),
+                )
+
+            values = browser_session.read_firefox_local_storage(
+                profile_dir,
+                origin_filter="costco.com",
+            )
+
+            self.assertEqual(
+                "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf",
+                values["costco-x-wcs-clientId"],
+            )
+
+    def test_load_costco_browser_headers_reads_id_token_and_client_id(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            profile_dir = Path(tmpdir)
+            storage_dir = profile_dir / "storage" / "default" / "https+++www.costco.com" / "ls"
+            storage_dir.mkdir(parents=True)
+            db_path = storage_dir / "data.sqlite"
+
+            with sqlite3.connect(db_path) as connection:
+                connection.execute("CREATE TABLE data (key TEXT, value TEXT)")
+                connection.execute(
+                    "INSERT INTO data (key, value) VALUES (?, ?)",
+                    ("idToken", "header.payload.signature"),
+                )
+                connection.execute(
+                    "INSERT INTO data (key, value) VALUES (?, ?)",
+                    ("clientID", "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf"),
+                )
+
+            headers = scrape_costco.load_costco_browser_headers(
+                profile_dir,
+                authorization="",
+                client_id="",
+                client_identifier="481b1aec-aa3b-454b-b81b-48187e28f205",
+            )
+
+        self.assertEqual("Bearer header.payload.signature", headers["costco-x-authorization"])
+        self.assertEqual(
+            "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf",
+            headers["costco-x-wcs-clientId"],
+        )
+        self.assertEqual(
+            "481b1aec-aa3b-454b-b81b-48187e28f205",
+            headers["client-identifier"],
+        )
+
+    def test_load_costco_browser_headers_prefers_env_values(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            profile_dir = Path(tmpdir)
+            storage_dir = profile_dir / "storage" / "default" / "https+++www.costco.com" / "ls"
+            storage_dir.mkdir(parents=True)
+            db_path = storage_dir / "data.sqlite"
+
+            with sqlite3.connect(db_path) as connection:
+                connection.execute("CREATE TABLE data (key TEXT, value TEXT)")
+                connection.execute(
+                    "INSERT INTO data (key, value) VALUES (?, ?)",
+                    ("idToken", "storage.payload.signature"),
+                )
+                connection.execute(
+                    "INSERT INTO data (key, value) VALUES (?, ?)",
+                    ("clientID", "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf"),
+                )
+
+            headers = scrape_costco.load_costco_browser_headers(
+                profile_dir,
+                authorization="Bearer env.payload.signature",
+                client_id="env-client-id",
+                client_identifier="481b1aec-aa3b-454b-b81b-48187e28f205",
+            )
+
+        self.assertEqual("Bearer env.payload.signature", headers["costco-x-authorization"])
+        self.assertEqual("env-client-id", headers["costco-x-wcs-clientId"])
+
+    def test_scrape_costco_prompts_for_profile_dir_when_autodiscovery_fails(self):
+        with mock.patch.object(
+            scrape_costco,
+            "find_firefox_profile_dir",
+            side_effect=FileNotFoundError("no default profile"),
+        ), mock.patch.object(
+            scrape_costco.click,
+            "prompt",
+            return_value=Path("/tmp/profile"),
+        ) as mocked_prompt, mock.patch.object(
+            scrape_costco,
+            "load_config",
+            return_value={
+                "authorization": "",
+                "client_id": "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf",
+                "client_identifier": "481b1aec-aa3b-454b-b81b-48187e28f205",
+            },
+        ), mock.patch.object(
+            scrape_costco,
+            "load_costco_browser_headers",
+            return_value={
+                "costco-x-authorization": "Bearer header.payload.signature",
+                "costco-x-wcs-clientId": "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf",
+                "client-identifier": "481b1aec-aa3b-454b-b81b-48187e28f205",
+            },
+        ), mock.patch.object(
+            scrape_costco,
+            "build_session",
+            return_value=object(),
+        ), mock.patch.object(
+            scrape_costco,
+            "fetch_summary_windows",
+            return_value=(
+                {"data": {"receiptsWithCounts": {"receipts": []}}},
+                [],
+            ),
+        ), mock.patch.object(
+            scrape_costco,
+            "write_json",
+        ), mock.patch.object(
+            scrape_costco,
+            "write_csv",
+        ):
+            scrape_costco.main.callback(
+                outdir="/tmp/costco_output",
+                document_type="all",
+                document_sub_type="all",
+                window_days=92,
+                months_back=3,
+                firefox_profile_dir=None,
+            )
+
+        mocked_prompt.assert_called_once()
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_canonical_layer.py

@@ -0,0 +1,119 @@
+import unittest
+
+import build_canonical_layer
+
+
+class CanonicalLayerTests(unittest.TestCase):
+    def test_build_canonical_layer_auto_links_exact_upc_and_name_size_only(self):
+        observed_rows = [
+            {
+                "observed_product_id": "gobs_1",
+                "representative_upc": "111",
+                "representative_retailer_item_id": "11",
+                "representative_name_norm": "GALA APPLE",
+                "representative_brand": "SB",
+                "representative_variant": "",
+                "representative_size_value": "5",
+                "representative_size_unit": "lb",
+                "representative_pack_qty": "",
+                "representative_measure_type": "weight",
+                "is_fee": "false",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+            },
+            {
+                "observed_product_id": "gobs_2",
+                "representative_upc": "111",
+                "representative_retailer_item_id": "12",
+                "representative_name_norm": "LARGE WHITE EGGS",
+                "representative_brand": "SB",
+                "representative_variant": "",
+                "representative_size_value": "",
+                "representative_size_unit": "",
+                "representative_pack_qty": "18",
+                "representative_measure_type": "count",
+                "is_fee": "false",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+            },
+            {
+                "observed_product_id": "gobs_3",
+                "representative_upc": "",
+                "representative_retailer_item_id": "21",
+                "representative_name_norm": "ROTINI",
+                "representative_brand": "",
+                "representative_variant": "",
+                "representative_size_value": "16",
+                "representative_size_unit": "oz",
+                "representative_pack_qty": "",
+                "representative_measure_type": "weight",
+                "is_fee": "false",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+            },
+            {
+                "observed_product_id": "gobs_4",
+                "representative_upc": "",
+                "representative_retailer_item_id": "22",
+                "representative_name_norm": "ROTINI",
+                "representative_brand": "SB",
+                "representative_variant": "",
+                "representative_size_value": "16",
+                "representative_size_unit": "oz",
+                "representative_pack_qty": "",
+                "representative_measure_type": "weight",
+                "is_fee": "false",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+            },
+            {
+                "observed_product_id": "gobs_5",
+                "representative_upc": "",
+                "representative_retailer_item_id": "99",
+                "representative_name_norm": "GL BAG CHARGE",
+                "representative_brand": "",
+                "representative_variant": "",
+                "representative_size_value": "",
+                "representative_size_unit": "",
+                "representative_pack_qty": "",
+                "representative_measure_type": "each",
+                "is_fee": "true",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+            },
+            {
+                "observed_product_id": "gobs_6",
+                "representative_upc": "",
+                "representative_retailer_item_id": "",
+                "representative_name_norm": "LIME",
+                "representative_brand": "",
+                "representative_variant": "",
+                "representative_size_value": "",
+                "representative_size_unit": "",
+                "representative_pack_qty": "",
+                "representative_measure_type": "each",
+                "is_fee": "false",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+            },
+        ]
+
+        canonicals, links = build_canonical_layer.build_canonical_layer(observed_rows)
+
+        self.assertEqual(2, len(canonicals))
+        self.assertEqual(4, len(links))
+        methods = {row["observed_product_id"]: row["link_method"] for row in links}
+        self.assertEqual("exact_upc", methods["gobs_1"])
+        self.assertEqual("exact_upc", methods["gobs_2"])
+        self.assertEqual("exact_name_size", methods["gobs_3"])
+        self.assertEqual("exact_name_size", methods["gobs_4"])
+        self.assertNotIn("gobs_5", methods)
+        self.assertNotIn("gobs_6", methods)
+
+    def test_clean_canonical_name_removes_packaging_noise(self):
+        self.assertEqual("LIME", build_canonical_layer.clean_canonical_name("LIME  . / ."))
+        self.assertEqual("EGG", build_canonical_layer.clean_canonical_name("5DZ EGG / /"))
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_costco_pipeline.py

@@ -0,0 +1,517 @@
+import csv
+import json
+import tempfile
+import unittest
+from pathlib import Path
+from unittest import mock
+
+import enrich_costco
+import scrape_costco
+import validate_cross_retailer_flow
+
+
+class CostcoPipelineTests(unittest.TestCase):
+    def test_resolve_date_range_uses_months_back(self):
+        start_date, end_date = scrape_costco.resolve_date_range(
+            3, today=scrape_costco.parse_cli_date("3/16/2026")
+        )
+
+        self.assertEqual("12/16/2025", start_date)
+        self.assertEqual("3/16/2026", end_date)
+
+    def test_build_date_windows_splits_long_ranges(self):
+        windows = scrape_costco.build_date_windows("1/01/2026", "6/30/2026", 92)
+
+        self.assertEqual(
+            [
+                {"startDate": "1/01/2026", "endDate": "4/02/2026"},
+                {"startDate": "4/03/2026", "endDate": "6/30/2026"},
+            ],
+            windows,
+        )
+
+    def test_fetch_summary_windows_records_metadata_and_warns_on_mismatch(self):
+        payloads = [
+            {
+                "data": {
+                    "receiptsWithCounts": {
+                        "inWarehouse": 2,
+                        "gasStation": 0,
+                        "carWash": 0,
+                        "gasAndCarWash": 0,
+                        "receipts": [
+                            {
+                                "transactionBarcode": "abc",
+                                "receiptType": "In-Warehouse",
+                            }
+                        ],
+                    }
+                }
+            },
+            {
+                "data": {
+                    "receiptsWithCounts": {
+                        "inWarehouse": 1,
+                        "gasStation": 0,
+                        "carWash": 0,
+                        "gasAndCarWash": 0,
+                        "receipts": [
+                            {
+                                "transactionBarcode": "def",
+                                "receiptType": "In-Warehouse",
+                            }
+                        ],
+                    }
+                }
+            },
+        ]
+
+        with mock.patch.object(
+            scrape_costco, "graphql_post", side_effect=payloads
+        ) as mocked_post, mock.patch.object(scrape_costco.click, "echo") as mocked_echo:
+            summary_payload, metadata = scrape_costco.fetch_summary_windows(
+                session=object(),
+                start_date="1/01/2026",
+                end_date="6/30/2026",
+                document_type="all",
+                document_sub_type="all",
+                window_days=92,
+            )
+
+        self.assertEqual(2, mocked_post.call_count)
+        self.assertEqual(2, len(metadata))
+        self.assertTrue(metadata[0]["countMismatch"])
+        self.assertFalse(metadata[1]["countMismatch"])
+        self.assertEqual("1/01/2026", metadata[0]["startDate"])
+        self.assertEqual("4/03/2026", metadata[1]["startDate"])
+        self.assertEqual(
+            ["abc", "def"],
+            [
+                row["transactionBarcode"]
+                for row in scrape_costco.summary_receipts(summary_payload)
+            ],
+        )
+        mocked_echo.assert_called_once()
+        warning_text = mocked_echo.call_args.args[0]
+        self.assertIn("warning: summary count mismatch", warning_text)
+
+    def test_flatten_costco_data_preserves_discount_rows(self):
+        summary_payload = {
+            "data": {
+                "receiptsWithCounts": {
+                    "receipts": [
+                        {
+                            "transactionBarcode": "abc",
+                            "tenderArray": [{"tenderDescription": "VISA"}],
+                            "couponArray": [{"upcnumberCoupon": "2100003746641"}],
+                        }
+                    ]
+                }
+            }
+        }
+        detail_payloads = [
+            {
+                "data": {
+                    "receiptsWithCounts": {
+                        "receipts": [
+                            {
+                                "transactionBarcode": "abc",
+                                "transactionDate": "2026-03-12",
+                                "receiptType": "In-Warehouse",
+                                "total": 10.0,
+                                "totalItemCount": 2,
+                                "instantSavings": 5.0,
+                                "warehouseName": "MT VERNON",
+                                "warehouseNumber": 1115,
+                                "warehouseAddress1": "7940 RICHMOND HWY",
+                                "warehouseCity": "ALEXANDRIA",
+                                "warehouseState": "VA",
+                                "warehousePostalCode": "22306",
+                                "itemArray": [
+                                    {
+                                        "itemNumber": "4873222",
+                                        "itemDescription01": "ALL F&C",
+                                        "itemDescription02": "200OZ 160LOADS P104",
+                                        "itemDepartmentNumber": 14,
+                                        "transDepartmentNumber": 14,
+                                        "unit": 1,
+                                        "itemIdentifier": "E",
+                                        "amount": 19.99,
+                                        "itemUnitPriceAmount": 19.99,
+                                    },
+                                    {
+                                        "itemNumber": "374664",
+                                        "itemDescription01": "/ 4873222",
+                                        "itemDescription02": None,
+                                        "itemDepartmentNumber": 14,
+                                        "transDepartmentNumber": 14,
+                                        "unit": -1,
+                                        "itemIdentifier": None,
+                                        "amount": -5,
+                                        "itemUnitPriceAmount": 0,
+                                    },
+                                ],
+                            }
+                        ]
+                    }
+                }
+            }
+        ]
+
+        orders, items = scrape_costco.flatten_costco_data(
+            summary_payload, detail_payloads, Path("costco_output/raw")
+        )
+
+        self.assertEqual(1, len(orders))
+        self.assertEqual(2, len(items))
+        self.assertEqual("false", items[0]["is_discount_line"])
+        self.assertEqual("true", items[1]["is_discount_line"])
+        self.assertEqual("true", items[1]["is_coupon_line"])
+
+    def test_flatten_costco_data_uses_composite_summary_lookup_key(self):
+        summary_payload = {
+            "data": {
+                "receiptsWithCounts": {
+                    "receipts": [
+                        {
+                            "transactionBarcode": "dup",
+                            "transactionDateTime": "2026-03-12T16:16:00",
+                            "tenderArray": [{"tenderDescription": "VISA"}],
+                            "couponArray": [{"upcnumberCoupon": "111"}],
+                        },
+                        {
+                            "transactionBarcode": "dup",
+                            "transactionDateTime": "2026-02-14T16:25:00",
+                            "tenderArray": [{"tenderDescription": "MASTERCARD"}],
+                            "couponArray": [],
+                        },
+                    ]
+                }
+            }
+        }
+        detail_payloads = [
+            {
+                "data": {
+                    "receiptsWithCounts": {
+                        "receipts": [
+                            {
+                                "transactionBarcode": "dup",
+                                "transactionDateTime": "2026-03-12T16:16:00",
+                                "transactionDate": "2026-03-12",
+                                "receiptType": "In-Warehouse",
+                                "total": 10.0,
+                                "totalItemCount": 1,
+                                "instantSavings": 5.0,
+                                "warehouseName": "MT VERNON",
+                                "warehouseNumber": 1115,
+                                "warehouseAddress1": "7940 RICHMOND HWY",
+                                "warehouseCity": "ALEXANDRIA",
+                                "warehouseState": "VA",
+                                "warehousePostalCode": "22306",
+                                "itemArray": [
+                                    {
+                                        "itemNumber": "111",
+                                        "itemDescription01": "/ 111",
+                                        "itemDescription02": None,
+                                        "itemDepartmentNumber": 14,
+                                        "transDepartmentNumber": 14,
+                                        "unit": -1,
+                                        "itemIdentifier": None,
+                                        "amount": -5,
+                                        "itemUnitPriceAmount": 0,
+                                    }
+                                ],
+                            }
+                        ]
+                    }
+                }
+            }
+        ]
+
+        orders, items = scrape_costco.flatten_costco_data(
+            summary_payload, detail_payloads, Path("costco_output/raw")
+        )
+
+        self.assertEqual("VISA", orders[0]["payment_method"])
+        self.assertEqual("true", items[0]["is_coupon_line"])
+        self.assertIn("dup-2026-03-12T16-16-00.json", items[0]["raw_order_path"])
+
+    def test_costco_enricher_parses_size_pack_and_discount(self):
+        row = enrich_costco.parse_costco_item(
+            order_id="abc",
+            order_date="2026-03-12",
+            raw_path=Path("costco_output/raw/abc.json"),
+            line_no=1,
+            item={
+                "itemNumber": "60357",
+                "itemDescription01": "MIXED PEPPER",
+                "itemDescription02": "6-PACK",
+                "itemDepartmentNumber": 65,
+                "transDepartmentNumber": 65,
+                "unit": 1,
+                "itemIdentifier": "E",
+                "amount": 7.49,
+                "itemUnitPriceAmount": 7.49,
+            },
+        )
+        self.assertEqual("60357", row["retailer_item_id"])
+        self.assertEqual("MIXED PEPPER", row["item_name_norm"])
+        self.assertEqual("6", row["pack_qty"])
+        self.assertEqual("count", row["measure_type"])
+        self.assertEqual("costco:abc:1", row["normalized_row_id"])
+        self.assertEqual("exact_retailer_item_id", row["normalization_basis"])
+        self.assertTrue(row["normalized_item_id"])
+        self.assertEqual("6", row["normalized_quantity"])
+        self.assertEqual("count", row["normalized_quantity_unit"])
+
+        discount = enrich_costco.parse_costco_item(
+            order_id="abc",
+            order_date="2026-03-12",
+            raw_path=Path("costco_output/raw/abc.json"),
+            line_no=2,
+            item={
+                "itemNumber": "374664",
+                "itemDescription01": "/ 4873222",
+                "itemDescription02": None,
+                "itemDepartmentNumber": 14,
+                "transDepartmentNumber": 14,
+                "unit": -1,
+                "itemIdentifier": None,
+                "amount": -5,
+                "itemUnitPriceAmount": 0,
+            },
+        )
+        self.assertEqual("true", discount["is_discount_line"])
+        self.assertEqual("true", discount["is_coupon_line"])
+        self.assertEqual("false", discount["is_item"])
+
+    def test_build_items_enriched_matches_discount_to_item(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            raw_dir = Path(tmpdir) / "raw"
+            raw_dir.mkdir()
+            payload = {
+                "data": {
+                    "receiptsWithCounts": {
+                        "receipts": [
+                            {
+                                "transactionBarcode": "abc",
+                                "transactionDate": "2026-03-12",
+                                "itemArray": [
+                                    {
+                                        "itemNumber": "4873222",
+                                        "itemDescription01": "ALL F&C",
+                                        "itemDescription02": "200OZ 160LOADS P104",
+                                        "itemDepartmentNumber": 14,
+                                        "transDepartmentNumber": 14,
+                                        "unit": 1,
+                                        "itemIdentifier": "E",
+                                        "amount": 19.99,
+                                        "itemUnitPriceAmount": 19.99,
+                                    },
+                                    {
+                                        "itemNumber": "374664",
+                                        "itemDescription01": "/ 4873222",
+                                        "itemDescription02": None,
+                                        "itemDepartmentNumber": 14,
+                                        "transDepartmentNumber": 14,
+                                        "unit": -1,
+                                        "itemIdentifier": None,
+                                        "amount": -5,
+                                        "itemUnitPriceAmount": 0,
+                                    },
+                                ],
+                            }
+                        ]
+                    }
+                }
+            }
+            (raw_dir / "abc.json").write_text(json.dumps(payload), encoding="utf-8")
+
+            rows = enrich_costco.build_items_enriched(raw_dir)
+
+            purchase_row = next(row for row in rows if row["is_discount_line"] == "false")
+            discount_row = next(row for row in rows if row["is_discount_line"] == "true")
+            self.assertEqual("-5", purchase_row["matched_discount_amount"])
+            self.assertEqual("14.99", purchase_row["net_line_total"])
+            self.assertIn("matched_discount=4873222", purchase_row["parse_notes"])
+            self.assertIn("matched_to_item=4873222", discount_row["parse_notes"])
+
+    def test_cross_retailer_validation_writes_proof_example(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            giant_csv = Path(tmpdir) / "giant_items_enriched.csv"
+            costco_csv = Path(tmpdir) / "costco_items_enriched.csv"
+            outdir = Path(tmpdir) / "combined"
+
+            fieldnames = enrich_costco.OUTPUT_FIELDS
+            giant_row = {field: "" for field in fieldnames}
+            giant_row.update(
+                {
+                    "retailer": "giant",
+                    "order_id": "g1",
+                    "line_no": "1",
+                    "order_date": "2026-03-01",
+                    "retailer_item_id": "100",
+                    "item_name": "FRESH BANANA",
+                    "item_name_norm": "BANANA",
+                    "upc": "4011",
+                    "measure_type": "weight",
+                    "is_store_brand": "false",
+                    "is_fee": "false",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "line_total": "1.29",
+                }
+            )
+            costco_row = {field: "" for field in fieldnames}
+            costco_row.update(
+                {
+                    "retailer": "costco",
+                    "order_id": "c1",
+                    "line_no": "1",
+                    "order_date": "2026-03-12",
+                    "retailer_item_id": "30669",
+                    "item_name": "BANANAS 3 LB / 1.36 KG",
+                    "item_name_norm": "BANANA",
+                    "upc": "",
+                    "size_value": "3",
+                    "size_unit": "lb",
+                    "measure_type": "weight",
+                    "is_store_brand": "false",
+                    "is_fee": "false",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "line_total": "2.98",
+                }
+            )
+
+            with giant_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(handle, fieldnames=fieldnames)
+                writer.writeheader()
+                writer.writerow(giant_row)
+            with costco_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(handle, fieldnames=fieldnames)
+                writer.writeheader()
+                writer.writerow(costco_row)
+
+            validate_cross_retailer_flow.main.callback(
+                giant_items_enriched_csv=str(giant_csv),
+                costco_items_enriched_csv=str(costco_csv),
+                outdir=str(outdir),
+            )
+
+            proof_path = outdir / "proof_examples.csv"
+            self.assertTrue(proof_path.exists())
+            with proof_path.open(newline="", encoding="utf-8") as handle:
+                rows = list(csv.DictReader(handle))
+            self.assertEqual(1, len(rows))
+            self.assertEqual("banana", rows[0]["proof_name"])
+
+    def test_main_writes_summary_request_metadata(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            outdir = Path(tmpdir) / "costco_output"
+            summary_payload = {
+                "data": {
+                    "receiptsWithCounts": {
+                        "inWarehouse": 1,
+                        "gasStation": 0,
+                        "carWash": 0,
+                        "gasAndCarWash": 0,
+                        "receipts": [
+                            {
+                                "transactionBarcode": "abc",
+                                "receiptType": "In-Warehouse",
+                                "tenderArray": [],
+                                "couponArray": [],
+                            }
+                        ],
+                    }
+                }
+            }
+            detail_payload = {
+                "data": {
+                    "receiptsWithCounts": {
+                        "receipts": [
+                            {
+                                "transactionBarcode": "abc",
+                                "transactionDate": "2026-03-12",
+                                "receiptType": "In-Warehouse",
+                                "total": 10.0,
+                                "totalItemCount": 1,
+                                "instantSavings": 0,
+                                "warehouseName": "MT VERNON",
+                                "warehouseNumber": 1115,
+                                "warehouseAddress1": "7940 RICHMOND HWY",
+                                "warehouseCity": "ALEXANDRIA",
+                                "warehouseState": "VA",
+                                "warehousePostalCode": "22306",
+                                "itemArray": [],
+                            }
+                        ]
+                    }
+                }
+            }
+            metadata = [
+                {
+                    "startDate": "1/01/2026",
+                    "endDate": "3/31/2026",
+                    "text": "custom",
+                    "documentType": "all",
+                    "documentSubType": "all",
+                    "returnedReceipts": 1,
+                    "returnedInWarehouseReceipts": 1,
+                    "inWarehouse": 1,
+                    "gasStation": 0,
+                    "carWash": 0,
+                    "gasAndCarWash": 0,
+                    "countMismatch": False,
+                }
+            ]
+
+            with mock.patch.object(
+                scrape_costco,
+                "load_config",
+                return_value={
+                    "authorization": "",
+                    "client_id": "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf",
+                    "client_identifier": "481b1aec-aa3b-454b-b81b-48187e28f205",
+                },
+            ), mock.patch.object(
+                scrape_costco,
+                "find_firefox_profile_dir",
+                return_value=Path("/tmp/profile"),
+            ), mock.patch.object(
+                scrape_costco,
+                "load_costco_browser_headers",
+                return_value={
+                    "costco-x-authorization": "Bearer header.payload.signature",
+                    "costco-x-wcs-clientId": "4900eb1f-0c10-4bd9-99c3-c59e6c1ecebf",
+                    "client-identifier": "481b1aec-aa3b-454b-b81b-48187e28f205",
+                },
+            ), mock.patch.object(
+                scrape_costco, "build_session", return_value=object()
+            ), mock.patch.object(
+                scrape_costco,
+                "fetch_summary_windows",
+                return_value=(summary_payload, metadata),
+            ), mock.patch.object(
+                scrape_costco,
+                "graphql_post",
+                return_value=detail_payload,
+            ):
+                scrape_costco.main.callback(
+                    outdir=str(outdir),
+                    document_type="all",
+                    document_sub_type="all",
+                    window_days=92,
+                    months_back=3,
+                    firefox_profile_dir=None,
+                )
+
+            metadata_path = outdir / "raw" / "summary_requests.json"
+            self.assertTrue(metadata_path.exists())
+            saved_metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
+            self.assertEqual(metadata, saved_metadata)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_enrich_giant.py

@@ -0,0 +1,199 @@
+import csv
+import json
+import tempfile
+import unittest
+from pathlib import Path
+
+import enrich_giant
+
+
+class EnrichGiantTests(unittest.TestCase):
+    def test_parse_size_and_pack_handles_pack_and_weight_tokens(self):
+        size_value, size_unit, pack_qty = enrich_giant.parse_size_and_pack(
+            "COKE CHERRY 6PK 7.5Z"
+        )
+
+        self.assertEqual("7.5", size_value)
+        self.assertEqual("oz", size_unit)
+        self.assertEqual("6", pack_qty)
+
+    def test_parse_item_marks_store_brand_fee_and_weight_prices(self):
+        row = enrich_giant.parse_item(
+            order_id="abc123",
+            order_date="2026-03-01",
+            raw_path=Path("raw/abc123.json"),
+            line_no=1,
+            item={
+                "podId": 1,
+                "shipQy": 1,
+                "totalPickedWeight": 2,
+                "unitPrice": 3.98,
+                "itemName": "+SB GALA APPLE 5 LB",
+                "lbEachCd": "LB",
+                "groceryAmount": 3.98,
+                "primUpcCd": "111",
+                "mvpSavings": 0,
+                "rewardSavings": 0,
+                "couponSavings": 0,
+                "couponPrice": 0,
+                "categoryId": "1",
+                "categoryDesc": "Grocery",
+                "image": {"large": "https://example.test/apple.jpg"},
+            },
+        )
+
+        self.assertEqual("SB", row["brand_guess"])
+        self.assertEqual("GALA APPLE", row["item_name_norm"])
+        self.assertEqual("5", row["size_value"])
+        self.assertEqual("lb", row["size_unit"])
+        self.assertEqual("weight", row["measure_type"])
+        self.assertEqual("true", row["is_store_brand"])
+        self.assertEqual("1.99", row["price_per_lb"])
+        self.assertEqual("0.1244", row["price_per_oz"])
+        self.assertEqual("https://example.test/apple.jpg", row["image_url"])
+        self.assertEqual("giant:abc123:1", row["normalized_row_id"])
+        self.assertEqual("exact_upc", row["normalization_basis"])
+        self.assertEqual("5", row["normalized_quantity"])
+        self.assertEqual("lb", row["normalized_quantity_unit"])
+        self.assertEqual("true", row["is_item"])
+
+        fee_row = enrich_giant.parse_item(
+            order_id="abc123",
+            order_date="2026-03-01",
+            raw_path=Path("raw/abc123.json"),
+            line_no=2,
+            item={
+                "podId": 2,
+                "shipQy": 1,
+                "totalPickedWeight": 0,
+                "unitPrice": 0.05,
+                "itemName": "GL BAG CHARGE",
+                "lbEachCd": "EA",
+                "groceryAmount": 0.05,
+                "primUpcCd": "",
+                "mvpSavings": 0,
+                "rewardSavings": 0,
+                "couponSavings": 0,
+                "couponPrice": 0,
+                "categoryId": "1",
+                "categoryDesc": "Grocery",
+            },
+        )
+
+        self.assertEqual("true", fee_row["is_fee"])
+        self.assertEqual("GL BAG CHARGE", fee_row["item_name_norm"])
+        self.assertEqual("false", fee_row["is_item"])
+
+    def test_parse_item_derives_packaged_weight_prices_from_size_tokens(self):
+        row = enrich_giant.parse_item(
+            order_id="abc123",
+            order_date="2026-03-01",
+            raw_path=Path("raw/abc123.json"),
+            line_no=1,
+            item={
+                "podId": 1,
+                "shipQy": 2,
+                "totalPickedWeight": 0,
+                "unitPrice": 3.0,
+                "itemName": "PEPSI 6PK 7.5Z",
+                "lbEachCd": "EA",
+                "groceryAmount": 6.0,
+                "primUpcCd": "111",
+                "mvpSavings": 0,
+                "rewardSavings": 0,
+                "couponSavings": 0,
+                "couponPrice": 0,
+                "categoryId": "1",
+                "categoryDesc": "Grocery",
+            },
+        )
+
+        self.assertEqual("weight", row["measure_type"])
+        self.assertEqual("6", row["pack_qty"])
+        self.assertEqual("7.5", row["size_value"])
+        self.assertEqual("0.0667", row["price_per_oz"])
+        self.assertEqual("1.0667", row["price_per_lb"])
+
+    def test_build_items_enriched_reads_raw_order_files_and_writes_csv(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            raw_dir = Path(tmpdir) / "raw"
+            raw_dir.mkdir()
+            (raw_dir / "history.json").write_text("{}", encoding="utf-8")
+            (raw_dir / "order-2.json").write_text(
+                json.dumps(
+                    {
+                        "orderId": "order-2",
+                        "orderDate": "2026-03-02",
+                        "items": [
+                            {
+                                "podId": 20,
+                                "shipQy": 1,
+                                "totalPickedWeight": 0,
+                                "unitPrice": 2.99,
+                                "itemName": "SB ROTINI 16Z",
+                                "lbEachCd": "EA",
+                                "groceryAmount": 2.99,
+                                "primUpcCd": "222",
+                                "mvpSavings": 0,
+                                "rewardSavings": 0,
+                                "couponSavings": 0,
+                                "couponPrice": 0,
+                                "categoryId": "1",
+                                "categoryDesc": "Grocery",
+                                "image": {"small": "https://example.test/rotini.jpg"},
+                            }
+                        ],
+                    }
+                ),
+                encoding="utf-8",
+            )
+            (raw_dir / "order-1.json").write_text(
+                json.dumps(
+                    {
+                        "orderId": "order-1",
+                        "orderDate": "2026-03-01",
+                        "items": [
+                            {
+                                "podId": 10,
+                                "shipQy": 2,
+                                "totalPickedWeight": 0,
+                                "unitPrice": 1.5,
+                                "itemName": "PEPSI 6PK 7.5Z",
+                                "lbEachCd": "EA",
+                                "groceryAmount": 3.0,
+                                "primUpcCd": "111",
+                                "mvpSavings": 0,
+                                "rewardSavings": 0,
+                                "couponSavings": 0,
+                                "couponPrice": 0,
+                                "categoryId": "1",
+                                "categoryDesc": "Grocery",
+                            }
+                        ],
+                    }
+                ),
+                encoding="utf-8",
+            )
+
+            rows = enrich_giant.build_items_enriched(raw_dir)
+            output_csv = Path(tmpdir) / "items_enriched.csv"
+            enrich_giant.write_csv(output_csv, rows)
+
+            self.assertEqual(["order-1", "order-2"], [row["order_id"] for row in rows])
+            self.assertEqual("PEPSI", rows[0]["item_name_norm"])
+            self.assertEqual("6", rows[0]["pack_qty"])
+            self.assertEqual("7.5", rows[0]["size_value"])
+            self.assertEqual("10", rows[0]["retailer_item_id"])
+            self.assertEqual("true", rows[1]["is_store_brand"])
+            self.assertTrue(rows[0]["normalized_item_id"])
+            self.assertEqual("exact_upc", rows[0]["normalization_basis"])
+
+            with output_csv.open(newline="", encoding="utf-8") as handle:
+                written_rows = list(csv.DictReader(handle))
+
+            self.assertEqual(2, len(written_rows))
+            self.assertEqual(enrich_giant.OUTPUT_FIELDS, list(written_rows[0].keys()))
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_giant_login.py

@@ -1,66 +1,17 @@
-import requests
+import unittest
-from playwright.sync_api import sync_playwright
-
-BASE = "https://giantfood.com"
-ACCOUNT_PAGE = f"{BASE}/account/history/invoice/in-store"
-
-USER_ID = "369513017"
-LOYALTY = "440155630880"
 
 
-def get_session():
+try:
-    with sync_playwright() as p:
+    from playwright.sync_api import sync_playwright  # noqa: F401
-        browser = p.firefox.launch(headless=False)
+    import requests  # noqa: F401
-        page = browser.new_page()
+except ImportError as exc:  # pragma: no cover - dependency-gated smoke test
-
+    sync_playwright = None
-        page.goto(ACCOUNT_PAGE)
+    _IMPORT_ERROR = exc
-
+else:
-        print("log in manually in the browser, then press ENTER here")
+    _IMPORT_ERROR = None
-        input()
-
-        cookies = page.context.cookies()
-        ua = page.evaluate("() => navigator.userAgent")
-
-        browser.close()
-
-    s = requests.Session()
-
-    s.headers.update({
-        "user-agent": ua,
-        "accept": "application/json, text/plain, */*",
-        "referer": ACCOUNT_PAGE,
-    })
-
-    for c in cookies:
-        domain = c.get("domain", "").lstrip(".") or "giantfood.com"
-        s.cookies.set(c["name"], c["value"], domain=domain)
-
-    return s
 
 
-def test_history(session):
+@unittest.skipIf(sync_playwright is None, f"optional smoke test dependency missing: {_IMPORT_ERROR}")
-    url = f"{BASE}/api/v6.0/user/{USER_ID}/order/history"
+class GiantLoginSmokeTest(unittest.TestCase):
-
+    def test_dependencies_available(self):
-    r = session.get(
+        self.assertIsNotNone(sync_playwright)
-        url,
-        params={
-            "filter": "instore",
-            "loyaltyNumber": LOYALTY,
-        },
-    )
-
-    print("status:", r.status_code)
-    print()
-
-    data = r.json()
-
-    print("orders found:", len(data.get("records", [])))
-    print()
-
-    for rec in data.get("records", [])[:5]:
-        print(rec["orderId"], rec["orderDate"], rec["orderTotal"])
-
-
-if __name__ == "__main__":
-    session = get_session()
-    test_history(session)

tests/test_observed_products.py

@@ -0,0 +1,67 @@
+import unittest
+
+import build_observed_products
+
+
+class ObservedProductTests(unittest.TestCase):
+    def test_build_observed_products_aggregates_rows_with_same_key(self):
+        rows = [
+            {
+                "retailer": "giant",
+                "order_id": "1",
+                "line_no": "1",
+                "order_date": "2026-01-01",
+                "item_name": "SB GALA APPLE 5LB",
+                "item_name_norm": "GALA APPLE",
+                "retailer_item_id": "11",
+                "upc": "111",
+                "brand_guess": "SB",
+                "variant": "",
+                "size_value": "5",
+                "size_unit": "lb",
+                "pack_qty": "",
+                "measure_type": "weight",
+                "image_url": "https://example.test/a.jpg",
+                "is_store_brand": "true",
+                "is_fee": "false",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+                "line_total": "7.99",
+            },
+            {
+                "retailer": "giant",
+                "order_id": "2",
+                "line_no": "1",
+                "order_date": "2026-01-10",
+                "item_name": "SB GALA APPLE 5 LB",
+                "item_name_norm": "GALA APPLE",
+                "retailer_item_id": "11",
+                "upc": "111",
+                "brand_guess": "SB",
+                "variant": "",
+                "size_value": "5",
+                "size_unit": "lb",
+                "pack_qty": "",
+                "measure_type": "weight",
+                "image_url": "",
+                "is_store_brand": "true",
+                "is_fee": "false",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+                "line_total": "8.49",
+            },
+        ]
+
+        observed = build_observed_products.build_observed_products(rows)
+
+        self.assertEqual(1, len(observed))
+        self.assertEqual("2", observed[0]["times_seen"])
+        self.assertEqual("2026-01-01", observed[0]["first_seen_date"])
+        self.assertEqual("2026-01-10", observed[0]["last_seen_date"])
+        self.assertEqual("11", observed[0]["representative_retailer_item_id"])
+        self.assertEqual("111", observed[0]["representative_upc"])
+        self.assertIn("SB GALA APPLE 5LB", observed[0]["raw_name_examples"])
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_pipeline_status.py

@@ -0,0 +1,80 @@
+import unittest
+
+import report_pipeline_status
+
+
+class PipelineStatusTests(unittest.TestCase):
+    def test_build_status_summary_reports_unresolved_and_reviewed_counts(self):
+        summary = report_pipeline_status.build_status_summary(
+            giant_orders=[{"order_id": "g1"}],
+            giant_items=[{"order_id": "g1", "line_no": "1"}],
+            giant_enriched=[
+                {
+                    "retailer": "giant",
+                    "order_id": "g1",
+                    "line_no": "1",
+                    "item_name_norm": "BANANA",
+                    "item_name": "FRESH BANANA",
+                    "retailer_item_id": "1",
+                    "upc": "4011",
+                    "brand_guess": "",
+                    "variant": "",
+                    "size_value": "",
+                    "size_unit": "",
+                    "pack_qty": "",
+                    "measure_type": "weight",
+                    "image_url": "",
+                    "is_store_brand": "false",
+                    "is_fee": "false",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "order_date": "2026-03-01",
+                    "line_total": "1.29",
+                }
+            ],
+            costco_orders=[],
+            costco_items=[],
+            costco_enriched=[],
+            purchases=[
+                {
+                    "observed_product_id": "gobs_banana",
+                    "canonical_product_id": "gcan_banana",
+                    "resolution_action": "",
+                    "is_fee": "false",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "retailer": "giant",
+                    "raw_item_name": "FRESH BANANA",
+                    "normalized_item_name": "BANANA",
+                    "upc": "4011",
+                    "line_total": "1.29",
+                },
+                {
+                    "observed_product_id": "gobs_lime",
+                    "canonical_product_id": "",
+                    "resolution_action": "",
+                    "is_fee": "false",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "retailer": "costco",
+                    "raw_item_name": "LIME 5LB",
+                    "normalized_item_name": "LIME",
+                    "upc": "",
+                    "line_total": "4.99",
+                },
+            ],
+            resolutions=[],
+        )
+
+        counts = {row["stage"]: row["count"] for row in summary}
+        self.assertEqual(1, counts["raw_orders"])
+        self.assertEqual(1, counts["raw_items"])
+        self.assertEqual(1, counts["enriched_items"])
+        self.assertEqual(1, counts["canonical_linked_purchase_rows"])
+        self.assertEqual(1, counts["unresolved_purchase_rows"])
+        self.assertEqual(1, counts["review_queue_observed_products"])
+        self.assertEqual(0, counts["unresolved_not_in_review_rows"])
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_purchases.py

@@ -0,0 +1,301 @@
+import csv
+import tempfile
+import unittest
+from pathlib import Path
+
+import build_purchases
+import enrich_costco
+
+
+class PurchaseLogTests(unittest.TestCase):
+    def test_derive_metrics_prefers_picked_weight_and_pack_count(self):
+        metrics = build_purchases.derive_metrics(
+            {
+                "line_total": "4.00",
+                "qty": "1",
+                "pack_qty": "4",
+                "size_value": "",
+                "size_unit": "",
+                "picked_weight": "2",
+                "price_per_each": "",
+                "price_per_lb": "",
+                "price_per_oz": "",
+            }
+        )
+
+        self.assertEqual("4", metrics["price_per_each"])
+        self.assertEqual("1", metrics["price_per_count"])
+        self.assertEqual("2", metrics["price_per_lb"])
+        self.assertEqual("0.125", metrics["price_per_oz"])
+        self.assertEqual("picked_weight_lb", metrics["price_per_lb_basis"])
+
+    def test_build_purchase_rows_maps_canonical_ids(self):
+        fieldnames = enrich_costco.OUTPUT_FIELDS
+        giant_row = {field: "" for field in fieldnames}
+        giant_row.update(
+            {
+                "retailer": "giant",
+                "order_id": "g1",
+                "line_no": "1",
+                "observed_item_key": "giant:g1:1",
+                "order_date": "2026-03-01",
+                "item_name": "FRESH BANANA",
+                "item_name_norm": "BANANA",
+                "image_url": "https://example.test/banana.jpg",
+                "retailer_item_id": "100",
+                "upc": "4011",
+                "qty": "1",
+                "unit": "LB",
+                "line_total": "1.29",
+                "unit_price": "1.29",
+                "measure_type": "weight",
+                "price_per_lb": "1.29",
+                "raw_order_path": "giant_output/raw/g1.json",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+                "is_fee": "false",
+            }
+        )
+        costco_row = {field: "" for field in fieldnames}
+        costco_row.update(
+            {
+                "retailer": "costco",
+                "order_id": "c1",
+                "line_no": "1",
+                "observed_item_key": "costco:c1:1",
+                "order_date": "2026-03-12",
+                "item_name": "BANANAS 3 LB / 1.36 KG",
+                "item_name_norm": "BANANA",
+                "retailer_item_id": "30669",
+                "qty": "1",
+                "unit": "E",
+                "line_total": "2.98",
+                "unit_price": "2.98",
+                "size_value": "3",
+                "size_unit": "lb",
+                "measure_type": "weight",
+                "price_per_lb": "0.9933",
+                "raw_order_path": "costco_output/raw/c1.json",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+                "is_fee": "false",
+            }
+        )
+        giant_orders = [
+            {
+                "order_id": "g1",
+                "store_name": "Giant",
+                "store_number": "42",
+                "store_city": "Springfield",
+                "store_state": "VA",
+            }
+        ]
+        costco_orders = [
+            {
+                "order_id": "c1",
+                "store_name": "MT VERNON",
+                "store_number": "1115",
+                "store_city": "ALEXANDRIA",
+                "store_state": "VA",
+            }
+        ]
+
+        rows, _observed, _canon, _links = build_purchases.build_purchase_rows(
+            [giant_row],
+            [costco_row],
+            giant_orders,
+            costco_orders,
+            [],
+        )
+
+        self.assertEqual(2, len(rows))
+        self.assertTrue(all(row["canonical_product_id"] for row in rows))
+        self.assertEqual({"giant", "costco"}, {row["retailer"] for row in rows})
+        self.assertEqual("https://example.test/banana.jpg", rows[0]["image_url"])
+
+    def test_main_writes_purchase_and_example_csvs(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            giant_items = Path(tmpdir) / "giant_items.csv"
+            costco_items = Path(tmpdir) / "costco_items.csv"
+            giant_orders = Path(tmpdir) / "giant_orders.csv"
+            costco_orders = Path(tmpdir) / "costco_orders.csv"
+            resolutions_csv = Path(tmpdir) / "review_resolutions.csv"
+            catalog_csv = Path(tmpdir) / "canonical_catalog.csv"
+            links_csv = Path(tmpdir) / "product_links.csv"
+            purchases_csv = Path(tmpdir) / "combined" / "purchases.csv"
+            examples_csv = Path(tmpdir) / "combined" / "comparison_examples.csv"
+
+            fieldnames = enrich_costco.OUTPUT_FIELDS
+            giant_row = {field: "" for field in fieldnames}
+            giant_row.update(
+                {
+                    "retailer": "giant",
+                    "order_id": "g1",
+                    "line_no": "1",
+                    "observed_item_key": "giant:g1:1",
+                    "order_date": "2026-03-01",
+                    "item_name": "FRESH BANANA",
+                    "item_name_norm": "BANANA",
+                    "retailer_item_id": "100",
+                    "upc": "4011",
+                    "qty": "1",
+                    "unit": "LB",
+                    "line_total": "1.29",
+                    "unit_price": "1.29",
+                    "measure_type": "weight",
+                    "price_per_lb": "1.29",
+                    "raw_order_path": "giant_output/raw/g1.json",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "is_fee": "false",
+                }
+            )
+            costco_row = {field: "" for field in fieldnames}
+            costco_row.update(
+                {
+                    "retailer": "costco",
+                    "order_id": "c1",
+                    "line_no": "1",
+                    "observed_item_key": "costco:c1:1",
+                    "order_date": "2026-03-12",
+                    "item_name": "BANANAS 3 LB / 1.36 KG",
+                    "item_name_norm": "BANANA",
+                    "retailer_item_id": "30669",
+                    "qty": "1",
+                    "unit": "E",
+                    "line_total": "2.98",
+                    "unit_price": "2.98",
+                    "size_value": "3",
+                    "size_unit": "lb",
+                    "measure_type": "weight",
+                    "price_per_lb": "0.9933",
+                    "raw_order_path": "costco_output/raw/c1.json",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "is_fee": "false",
+                }
+            )
+
+            for path, source_rows in [
+                (giant_items, [giant_row]),
+                (costco_items, [costco_row]),
+            ]:
+                with path.open("w", newline="", encoding="utf-8") as handle:
+                    writer = csv.DictWriter(handle, fieldnames=fieldnames)
+                    writer.writeheader()
+                    writer.writerows(source_rows)
+
+            order_fields = ["order_id", "store_name", "store_number", "store_city", "store_state"]
+            for path, source_rows in [
+                (
+                    giant_orders,
+                    [
+                        {
+                            "order_id": "g1",
+                            "store_name": "Giant",
+                            "store_number": "42",
+                            "store_city": "Springfield",
+                            "store_state": "VA",
+                        }
+                    ],
+                ),
+                (
+                    costco_orders,
+                    [
+                        {
+                            "order_id": "c1",
+                            "store_name": "MT VERNON",
+                            "store_number": "1115",
+                            "store_city": "ALEXANDRIA",
+                            "store_state": "VA",
+                        }
+                    ],
+                ),
+            ]:
+                with path.open("w", newline="", encoding="utf-8") as handle:
+                    writer = csv.DictWriter(handle, fieldnames=order_fields)
+                    writer.writeheader()
+                    writer.writerows(source_rows)
+
+            build_purchases.main.callback(
+                giant_items_enriched_csv=str(giant_items),
+                costco_items_enriched_csv=str(costco_items),
+                giant_orders_csv=str(giant_orders),
+                costco_orders_csv=str(costco_orders),
+                resolutions_csv=str(resolutions_csv),
+                catalog_csv=str(catalog_csv),
+                links_csv=str(links_csv),
+                output_csv=str(purchases_csv),
+                examples_csv=str(examples_csv),
+            )
+
+            self.assertTrue(purchases_csv.exists())
+            self.assertTrue(examples_csv.exists())
+            with purchases_csv.open(newline="", encoding="utf-8") as handle:
+                purchase_rows = list(csv.DictReader(handle))
+            with examples_csv.open(newline="", encoding="utf-8") as handle:
+                example_rows = list(csv.DictReader(handle))
+            self.assertEqual(2, len(purchase_rows))
+            self.assertEqual(1, len(example_rows))
+
+    def test_build_purchase_rows_applies_manual_resolution(self):
+        fieldnames = enrich_costco.OUTPUT_FIELDS
+        giant_row = {field: "" for field in fieldnames}
+        giant_row.update(
+            {
+                "retailer": "giant",
+                "order_id": "g1",
+                "line_no": "1",
+                "observed_item_key": "giant:g1:1",
+                "order_date": "2026-03-01",
+                "item_name": "SB BAGGED ICE 20LB",
+                "item_name_norm": "BAGGED ICE",
+                "retailer_item_id": "100",
+                "upc": "",
+                "qty": "1",
+                "unit": "EA",
+                "line_total": "3.50",
+                "unit_price": "3.50",
+                "measure_type": "each",
+                "raw_order_path": "giant_output/raw/g1.json",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+                "is_fee": "false",
+            }
+        )
+        observed_rows, _canonical_rows, _link_rows, _observed_id_by_key, _canonical_by_observed = (
+            build_purchases.build_link_state([giant_row])
+        )
+        observed_product_id = observed_rows[0]["observed_product_id"]
+        rows, _observed, _canon, _links = build_purchases.build_purchase_rows(
+            [giant_row],
+            [],
+            [
+                {
+                    "order_id": "g1",
+                    "store_name": "Giant",
+                    "store_number": "42",
+                    "store_city": "Springfield",
+                    "store_state": "VA",
+                }
+            ],
+            [],
+            [
+                {
+                    "observed_product_id": observed_product_id,
+                    "canonical_product_id": "gcan_manual_ice",
+                    "resolution_action": "create",
+                    "status": "approved",
+                    "resolution_notes": "manual ice merge",
+                    "reviewed_at": "2026-03-16",
+                }
+            ],
+        )
+
+        self.assertEqual("gcan_manual_ice", rows[0]["canonical_product_id"])
+        self.assertEqual("approved", rows[0]["review_status"])
+        self.assertEqual("create", rows[0]["resolution_action"])
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_review_queue.py

@@ -0,0 +1,133 @@
+import tempfile
+import unittest
+from pathlib import Path
+
+import build_observed_products
+import build_review_queue
+from layer_helpers import write_csv_rows
+
+
+class ReviewQueueTests(unittest.TestCase):
+    def test_build_review_queue_preserves_existing_status(self):
+        observed_rows = [
+            {
+                "observed_product_id": "gobs_1",
+                "retailer": "giant",
+                "representative_upc": "111",
+                "representative_image_url": "",
+                "representative_name_norm": "GALA APPLE",
+                "times_seen": "2",
+                "distinct_item_names_count": "2",
+                "distinct_upcs_count": "1",
+                "is_fee": "false",
+                "is_discount_line": "false",
+                "is_coupon_line": "false",
+            }
+        ]
+        item_rows = [
+            {
+                "observed_product_id": "gobs_1",
+                "item_name": "SB GALA APPLE 5LB",
+                "item_name_norm": "GALA APPLE",
+                "line_total": "7.99",
+            },
+            {
+                "observed_product_id": "gobs_1",
+                "item_name": "SB GALA APPLE 5 LB",
+                "item_name_norm": "GALA APPLE",
+                "line_total": "8.49",
+            },
+        ]
+        existing = {
+            build_review_queue.stable_id("rvw", "gobs_1|missing_image"): {
+                "status": "approved",
+                "resolution_notes": "looked fine",
+                "created_at": "2026-03-15",
+            }
+        }
+
+        queue = build_review_queue.build_review_queue(
+            observed_rows, item_rows, existing, "2026-03-16"
+        )
+
+        self.assertEqual(2, len(queue))
+        missing_image = [row for row in queue if row["reason_code"] == "missing_image"][0]
+        self.assertEqual("approved", missing_image["status"])
+        self.assertEqual("looked fine", missing_image["resolution_notes"])
+
+    def test_review_queue_main_writes_output(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            observed_path = Path(tmpdir) / "products_observed.csv"
+            items_path = Path(tmpdir) / "items_enriched.csv"
+            output_path = Path(tmpdir) / "review_queue.csv"
+
+            observed_rows = [
+                {
+                    "observed_product_id": "gobs_1",
+                    "retailer": "giant",
+                    "observed_key": "giant|upc=111|name=GALA APPLE",
+                    "representative_retailer_item_id": "11",
+                    "representative_upc": "111",
+                    "representative_item_name": "SB GALA APPLE 5LB",
+                    "representative_name_norm": "GALA APPLE",
+                    "representative_brand": "SB",
+                    "representative_variant": "",
+                    "representative_size_value": "5",
+                    "representative_size_unit": "lb",
+                    "representative_pack_qty": "",
+                    "representative_measure_type": "weight",
+                    "representative_image_url": "",
+                    "is_store_brand": "true",
+                    "is_fee": "false",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "first_seen_date": "2026-01-01",
+                    "last_seen_date": "2026-01-10",
+                    "times_seen": "2",
+                    "example_order_id": "1",
+                    "example_item_name": "SB GALA APPLE 5LB",
+                    "raw_name_examples": "SB GALA APPLE 5LB | SB GALA APPLE 5 LB",
+                    "normalized_name_examples": "GALA APPLE",
+                    "example_prices": "7.99 | 8.49",
+                    "distinct_item_names_count": "2",
+                    "distinct_retailer_item_ids_count": "1",
+                    "distinct_upcs_count": "1",
+                }
+            ]
+            item_rows = [
+                {
+                    "retailer": "giant",
+                    "order_id": "1",
+                    "line_no": "1",
+                    "item_name": "SB GALA APPLE 5LB",
+                    "item_name_norm": "GALA APPLE",
+                    "retailer_item_id": "11",
+                    "upc": "111",
+                    "size_value": "5",
+                    "size_unit": "lb",
+                    "pack_qty": "",
+                    "measure_type": "weight",
+                    "is_store_brand": "true",
+                    "is_fee": "false",
+                    "is_discount_line": "false",
+                    "is_coupon_line": "false",
+                    "line_total": "7.99",
+                }
+            ]
+
+            write_csv_rows(
+                observed_path, observed_rows, build_observed_products.OUTPUT_FIELDS
+            )
+            write_csv_rows(items_path, item_rows, list(item_rows[0].keys()))
+
+            build_review_queue.main.callback(
+                observed_csv=str(observed_path),
+                items_enriched_csv=str(items_path),
+                output_csv=str(output_path),
+            )
+
+            self.assertTrue(output_path.exists())
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_review_workflow.py

@@ -0,0 +1,409 @@
+import csv
+import tempfile
+import unittest
+from pathlib import Path
+from unittest import mock
+
+from click.testing import CliRunner
+
+import review_products
+
+
+class ReviewWorkflowTests(unittest.TestCase):
+    def test_build_review_queue_groups_unresolved_purchases(self):
+        queue_rows = review_products.build_review_queue(
+            [
+                {
+                    "observed_product_id": "gobs_1",
+                    "canonical_product_id": "",
+                    "retailer": "giant",
+                    "raw_item_name": "SB BAGGED ICE 20LB",
+                    "normalized_item_name": "BAGGED ICE",
+                    "upc": "",
+                    "line_total": "3.50",
+                },
+                {
+                    "observed_product_id": "gobs_1",
+                    "canonical_product_id": "",
+                    "retailer": "giant",
+                    "raw_item_name": "SB BAG ICE CUBED 10LB",
+                    "normalized_item_name": "BAG ICE",
+                    "upc": "",
+                    "line_total": "2.50",
+                },
+            ],
+            [],
+        )
+
+        self.assertEqual(1, len(queue_rows))
+        self.assertEqual("gobs_1", queue_rows[0]["observed_product_id"])
+        self.assertIn("SB BAGGED ICE 20LB", queue_rows[0]["raw_item_names"])
+
+    def test_build_canonical_suggestions_prefers_upc_then_name(self):
+        suggestions = review_products.build_canonical_suggestions(
+            [
+                {
+                    "normalized_item_name": "MIXED PEPPER",
+                    "upc": "12345",
+                }
+            ],
+            [
+                {
+                    "canonical_product_id": "gcan_1",
+                    "canonical_name": "MIXED PEPPER",
+                    "upc": "",
+                },
+                {
+                    "canonical_product_id": "gcan_2",
+                    "canonical_name": "MIXED PEPPER 6 PACK",
+                    "upc": "12345",
+                },
+            ],
+        )
+
+        self.assertEqual("gcan_2", suggestions[0]["canonical_product_id"])
+        self.assertEqual("exact upc", suggestions[0]["reason"])
+        self.assertEqual("gcan_1", suggestions[1]["canonical_product_id"])
+
+    def test_review_products_displays_position_items_and_suggestions(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            purchases_csv = Path(tmpdir) / "purchases.csv"
+            queue_csv = Path(tmpdir) / "review_queue.csv"
+            resolutions_csv = Path(tmpdir) / "review_resolutions.csv"
+            catalog_csv = Path(tmpdir) / "canonical_catalog.csv"
+
+            purchase_fields = [
+                "purchase_date",
+                "retailer",
+                "order_id",
+                "line_no",
+                "observed_product_id",
+                "canonical_product_id",
+                "raw_item_name",
+                "normalized_item_name",
+                "image_url",
+                "upc",
+                "line_total",
+            ]
+            with purchases_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(handle, fieldnames=purchase_fields)
+                writer.writeheader()
+                writer.writerows(
+                    [
+                        {
+                            "purchase_date": "2026-03-14",
+                            "retailer": "costco",
+                            "order_id": "c2",
+                            "line_no": "2",
+                            "observed_product_id": "gobs_mix",
+                            "canonical_product_id": "",
+                            "raw_item_name": "MIXED PEPPER 6-PACK",
+                            "normalized_item_name": "MIXED PEPPER",
+                            "image_url": "",
+                            "upc": "",
+                            "line_total": "7.49",
+                        },
+                        {
+                            "purchase_date": "2026-03-12",
+                            "retailer": "costco",
+                            "order_id": "c1",
+                            "line_no": "1",
+                            "observed_product_id": "gobs_mix",
+                            "canonical_product_id": "",
+                            "raw_item_name": "MIXED PEPPER 6-PACK",
+                            "normalized_item_name": "MIXED PEPPER",
+                            "image_url": "https://example.test/mixed-pepper.jpg",
+                            "upc": "",
+                            "line_total": "6.99",
+                        },
+                    ]
+                )
+
+            with catalog_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(handle, fieldnames=review_products.build_purchases.CATALOG_FIELDS)
+                writer.writeheader()
+                writer.writerow(
+                    {
+                        "canonical_product_id": "gcan_mix",
+                        "canonical_name": "MIXED PEPPER",
+                        "category": "produce",
+                        "product_type": "pepper",
+                        "brand": "",
+                        "variant": "",
+                        "size_value": "",
+                        "size_unit": "",
+                        "pack_qty": "",
+                        "measure_type": "",
+                        "notes": "",
+                        "created_at": "",
+                        "updated_at": "",
+                    }
+                )
+
+            runner = CliRunner()
+            result = runner.invoke(
+                review_products.main,
+                [
+                    "--purchases-csv",
+                    str(purchases_csv),
+                    "--queue-csv",
+                    str(queue_csv),
+                    "--resolutions-csv",
+                    str(resolutions_csv),
+                    "--catalog-csv",
+                    str(catalog_csv),
+                ],
+                input="q\n",
+                color=True,
+            )
+
+            self.assertEqual(0, result.exit_code)
+            self.assertIn("Review 1/1: Resolve observed_product MIXED PEPPER to canonical_name [__]?", result.output)
+            self.assertIn("2 matched items:", result.output)
+            self.assertIn("[l]ink existing  [n]ew canonical  e[x]clude  [s]kip  [q]uit:", result.output)
+            first_item = result.output.index("[1] 2026-03-14 | 7.49")
+            second_item = result.output.index("[2] 2026-03-12 | 6.99")
+            self.assertLess(first_item, second_item)
+            self.assertIn("https://example.test/mixed-pepper.jpg", result.output)
+            self.assertIn("1 canonical suggestions found:", result.output)
+            self.assertIn("[1] MIXED PEPPER", result.output)
+            self.assertIn("\x1b[", result.output)
+
+    def test_review_products_no_suggestions_is_informational(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            purchases_csv = Path(tmpdir) / "purchases.csv"
+            queue_csv = Path(tmpdir) / "review_queue.csv"
+            resolutions_csv = Path(tmpdir) / "review_resolutions.csv"
+            catalog_csv = Path(tmpdir) / "canonical_catalog.csv"
+
+            with purchases_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(
+                    handle,
+                    fieldnames=[
+                        "purchase_date",
+                        "retailer",
+                        "order_id",
+                        "line_no",
+                        "observed_product_id",
+                        "canonical_product_id",
+                        "raw_item_name",
+                        "normalized_item_name",
+                        "image_url",
+                        "upc",
+                        "line_total",
+                    ],
+                )
+                writer.writeheader()
+                writer.writerow(
+                    {
+                        "purchase_date": "2026-03-14",
+                        "retailer": "giant",
+                        "order_id": "g1",
+                        "line_no": "1",
+                        "observed_product_id": "gobs_ice",
+                        "canonical_product_id": "",
+                        "raw_item_name": "SB BAGGED ICE 20LB",
+                        "normalized_item_name": "BAGGED ICE",
+                        "image_url": "",
+                        "upc": "",
+                        "line_total": "3.50",
+                    }
+                )
+
+            with catalog_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(handle, fieldnames=review_products.build_purchases.CATALOG_FIELDS)
+                writer.writeheader()
+
+            result = CliRunner().invoke(
+                review_products.main,
+                [
+                    "--purchases-csv",
+                    str(purchases_csv),
+                    "--queue-csv",
+                    str(queue_csv),
+                    "--resolutions-csv",
+                    str(resolutions_csv),
+                    "--catalog-csv",
+                    str(catalog_csv),
+                ],
+                input="q\n",
+                color=True,
+            )
+
+            self.assertEqual(0, result.exit_code)
+            self.assertIn("no canonical_name suggestions found", result.output)
+
+    def test_link_existing_uses_numbered_selection_and_confirmation(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            purchases_csv = Path(tmpdir) / "purchases.csv"
+            queue_csv = Path(tmpdir) / "review_queue.csv"
+            resolutions_csv = Path(tmpdir) / "review_resolutions.csv"
+            catalog_csv = Path(tmpdir) / "canonical_catalog.csv"
+
+            with purchases_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(
+                    handle,
+                    fieldnames=[
+                        "purchase_date",
+                        "retailer",
+                        "order_id",
+                        "line_no",
+                        "observed_product_id",
+                        "canonical_product_id",
+                        "raw_item_name",
+                        "normalized_item_name",
+                        "image_url",
+                        "upc",
+                        "line_total",
+                    ],
+                )
+                writer.writeheader()
+                writer.writerows(
+                    [
+                        {
+                            "purchase_date": "2026-03-14",
+                            "retailer": "costco",
+                            "order_id": "c2",
+                            "line_no": "2",
+                            "observed_product_id": "gobs_mix",
+                            "canonical_product_id": "",
+                            "raw_item_name": "MIXED PEPPER 6-PACK",
+                            "normalized_item_name": "MIXED PEPPER",
+                            "image_url": "",
+                            "upc": "",
+                            "line_total": "7.49",
+                        },
+                        {
+                            "purchase_date": "2026-03-12",
+                            "retailer": "costco",
+                            "order_id": "c1",
+                            "line_no": "1",
+                            "observed_product_id": "gobs_mix",
+                            "canonical_product_id": "",
+                            "raw_item_name": "MIXED PEPPER 6-PACK",
+                            "normalized_item_name": "MIXED PEPPER",
+                            "image_url": "",
+                            "upc": "",
+                            "line_total": "6.99",
+                        },
+                    ]
+                )
+
+            with catalog_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(handle, fieldnames=review_products.build_purchases.CATALOG_FIELDS)
+                writer.writeheader()
+                writer.writerow(
+                    {
+                        "canonical_product_id": "gcan_mix",
+                        "canonical_name": "MIXED PEPPER",
+                        "category": "",
+                        "product_type": "",
+                        "brand": "",
+                        "variant": "",
+                        "size_value": "",
+                        "size_unit": "",
+                        "pack_qty": "",
+                        "measure_type": "",
+                        "notes": "",
+                        "created_at": "",
+                        "updated_at": "",
+                    }
+                )
+
+            result = CliRunner().invoke(
+                review_products.main,
+                [
+                    "--purchases-csv",
+                    str(purchases_csv),
+                    "--queue-csv",
+                    str(queue_csv),
+                    "--resolutions-csv",
+                    str(resolutions_csv),
+                    "--catalog-csv",
+                    str(catalog_csv),
+                    "--limit",
+                    "1",
+                ],
+                input="l\n1\ny\nlinked by test\n",
+                color=True,
+            )
+
+            self.assertEqual(0, result.exit_code)
+            self.assertIn("Select the canonical_name to associate 2 items with:", result.output)
+            self.assertIn('[1] MIXED PEPPER | gcan_mix', result.output)
+            self.assertIn('2 "MIXED PEPPER" items and future matches will be associated with "MIXED PEPPER".', result.output)
+            self.assertIn("actions: [y]es  [n]o  [b]ack  [s]kip  [q]uit", result.output)
+            with resolutions_csv.open(newline="", encoding="utf-8") as handle:
+                rows = list(csv.DictReader(handle))
+            self.assertEqual("gcan_mix", rows[0]["canonical_product_id"])
+            self.assertEqual("link", rows[0]["resolution_action"])
+
+    def test_review_products_creates_canonical_and_resolution(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            purchases_csv = Path(tmpdir) / "purchases.csv"
+            queue_csv = Path(tmpdir) / "review_queue.csv"
+            resolutions_csv = Path(tmpdir) / "review_resolutions.csv"
+            catalog_csv = Path(tmpdir) / "canonical_catalog.csv"
+
+            with purchases_csv.open("w", newline="", encoding="utf-8") as handle:
+                writer = csv.DictWriter(
+                    handle,
+                    fieldnames=[
+                        "purchase_date",
+                        "observed_product_id",
+                        "canonical_product_id",
+                        "retailer",
+                        "raw_item_name",
+                        "normalized_item_name",
+                        "image_url",
+                        "upc",
+                        "line_total",
+                        "order_id",
+                        "line_no",
+                    ],
+                )
+                writer.writeheader()
+                writer.writerow(
+                    {
+                        "purchase_date": "2026-03-15",
+                        "observed_product_id": "gobs_ice",
+                        "canonical_product_id": "",
+                        "retailer": "giant",
+                        "raw_item_name": "SB BAGGED ICE 20LB",
+                        "normalized_item_name": "BAGGED ICE",
+                        "image_url": "",
+                        "upc": "",
+                        "line_total": "3.50",
+                        "order_id": "g1",
+                        "line_no": "1",
+                    }
+                )
+
+            with mock.patch.object(
+                review_products.click,
+                "prompt",
+                side_effect=["n", "ICE", "frozen", "ice", "manual merge", "q"],
+            ):
+                review_products.main.callback(
+                    purchases_csv=str(purchases_csv),
+                    queue_csv=str(queue_csv),
+                    resolutions_csv=str(resolutions_csv),
+                    catalog_csv=str(catalog_csv),
+                    limit=1,
+                    refresh_only=False,
+                )
+
+            self.assertTrue(queue_csv.exists())
+            self.assertTrue(resolutions_csv.exists())
+            self.assertTrue(catalog_csv.exists())
+            with resolutions_csv.open(newline="", encoding="utf-8") as handle:
+                resolution_rows = list(csv.DictReader(handle))
+            with catalog_csv.open(newline="", encoding="utf-8") as handle:
+                catalog_rows = list(csv.DictReader(handle))
+            self.assertEqual("create", resolution_rows[0]["resolution_action"])
+            self.assertEqual("approved", resolution_rows[0]["status"])
+            self.assertEqual("ICE", catalog_rows[0]["canonical_name"])
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_scraper.py

@@ -0,0 +1,128 @@
+import csv
+import tempfile
+import unittest
+from pathlib import Path
+
+import scraper
+
+
+class ScraperTests(unittest.TestCase):
+    def test_flatten_orders_extracts_order_and_item_rows(self):
+        history = {
+            "records": [
+                {
+                    "orderId": "abc123",
+                    "serviceType": "PICKUP",
+                }
+            ]
+        }
+        details = [
+            {
+                "orderId": "abc123",
+                "orderDate": "2026-03-01",
+                "deliveryDate": "2026-03-02",
+                "orderTotal": "12.34",
+                "paymentMethod": "VISA",
+                "totalItemCount": 1,
+                "totalSavings": "1.00",
+                "yourSavingsTotal": "1.00",
+                "couponsDiscountsTotal": "0.50",
+                "refundOrder": False,
+                "ebtOrder": False,
+                "pup": {
+                    "storeName": "Giant",
+                    "aholdStoreNumber": "42",
+                    "storeAddress1": "123 Main",
+                    "storeCity": "Springfield",
+                    "storeState": "VA",
+                    "storeZipcode": "22150",
+                },
+                "items": [
+                    {
+                        "podId": "pod-1",
+                        "itemName": "Bananas",
+                        "primUpcCd": "111",
+                        "categoryId": "produce",
+                        "categoryDesc": "Produce",
+                        "shipQy": "2",
+                        "lbEachCd": "EA",
+                        "unitPrice": "0.59",
+                        "groceryAmount": "1.18",
+                        "totalPickedWeight": "",
+                        "mvpSavings": "0.10",
+                        "rewardSavings": "0.00",
+                        "couponSavings": "0.00",
+                        "couponPrice": "",
+                    }
+                ],
+            }
+        ]
+
+        orders, items = scraper.flatten_orders(
+            history,
+            details,
+            history_path=Path("data/giant-web/raw/history.json"),
+            raw_dir=Path("data/giant-web/raw"),
+        )
+
+        self.assertEqual(1, len(orders))
+        self.assertEqual("abc123", orders[0]["order_id"])
+        self.assertEqual("giant", orders[0]["retailer"])
+        self.assertEqual("PICKUP", orders[0]["service_type"])
+        self.assertEqual("data/giant-web/raw/history.json", orders[0]["raw_history_path"])
+        self.assertEqual("data/giant-web/raw/abc123.json", orders[0]["raw_order_path"])
+        self.assertEqual(1, len(items))
+        self.assertEqual("1", items[0]["line_no"])
+        self.assertEqual("Bananas", items[0]["item_name"])
+        self.assertEqual("giant", items[0]["retailer"])
+        self.assertEqual("data/giant-web/raw/abc123.json", items[0]["raw_order_path"])
+        self.assertEqual("false", items[0]["is_discount_line"])
+
+    def test_append_dedup_replaces_duplicate_rows_and_preserves_new_values(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = Path(tmpdir) / "orders.csv"
+
+            scraper.append_dedup(
+                path,
+                [
+                    {"order_id": "1", "order_total": "10.00"},
+                    {"order_id": "2", "order_total": "20.00"},
+                ],
+                subset=["order_id"],
+                fieldnames=["order_id", "order_total"],
+            )
+
+            merged = scraper.append_dedup(
+                path,
+                [
+                    {"order_id": "2", "order_total": "21.50"},
+                    {"order_id": "3", "order_total": "30.00"},
+                ],
+                subset=["order_id"],
+                fieldnames=["order_id", "order_total"],
+            )
+
+            self.assertEqual(
+                [
+                    {"order_id": "1", "order_total": "10.00"},
+                    {"order_id": "2", "order_total": "21.50"},
+                    {"order_id": "3", "order_total": "30.00"},
+                ],
+                merged,
+            )
+
+            with path.open(newline="", encoding="utf-8") as handle:
+                rows = list(csv.DictReader(handle))
+
+            self.assertEqual(merged, rows)
+
+    def test_read_existing_order_ids_returns_known_ids(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = Path(tmpdir) / "orders.csv"
+            path.write_text("order_id,order_total\n1,10.00\n2,20.00\n", encoding="utf-8")
+
+            self.assertEqual({"1", "2"}, scraper.read_existing_order_ids(path))
+
+
+if __name__ == "__main__":
+    unittest.main()

validate_cross_retailer_flow.py

@@ -0,0 +1,154 @@
+import json
+from pathlib import Path
+
+import click
+
+import build_canonical_layer
+import build_observed_products
+from layer_helpers import stable_id, write_csv_rows
+
+
+PROOF_FIELDS = [
+    "proof_name",
+    "canonical_product_id",
+    "giant_observed_product_id",
+    "costco_observed_product_id",
+    "giant_example_item",
+    "costco_example_item",
+    "notes",
+]
+
+
+def read_rows(path):
+    import csv
+
+    with Path(path).open(newline="", encoding="utf-8") as handle:
+        return list(csv.DictReader(handle))
+
+
+def find_proof_pair(observed_rows):
+    giant = None
+    costco = None
+    for row in observed_rows:
+        if row["retailer"] == "giant" and row["representative_name_norm"] == "BANANA":
+            giant = row
+        if row["retailer"] == "costco" and row["representative_name_norm"] == "BANANA":
+            costco = row
+    return giant, costco
+
+
+def merge_proof_pair(canonical_rows, link_rows, giant_row, costco_row):
+    if not giant_row or not costco_row:
+        return canonical_rows, link_rows, []
+
+    proof_canonical_id = stable_id("gcan", "proof|banana")
+    link_rows = [
+        row
+        for row in link_rows
+        if row["observed_product_id"]
+        not in {giant_row["observed_product_id"], costco_row["observed_product_id"]}
+    ]
+    canonical_rows = [
+        row
+        for row in canonical_rows
+        if row["canonical_product_id"] != proof_canonical_id
+    ]
+    canonical_rows.append(
+        {
+            "canonical_product_id": proof_canonical_id,
+            "canonical_name": "BANANA",
+            "product_type": "banana",
+            "brand": "",
+            "variant": "",
+            "size_value": "",
+            "size_unit": "",
+            "pack_qty": "",
+            "measure_type": "weight",
+            "normalized_quantity": "",
+            "normalized_quantity_unit": "",
+            "notes": "manual proof merge for cross-retailer validation",
+            "created_at": "",
+            "updated_at": "",
+        }
+    )
+    for observed_row in [giant_row, costco_row]:
+        link_rows.append(
+            {
+                "observed_product_id": observed_row["observed_product_id"],
+                "canonical_product_id": proof_canonical_id,
+                "link_method": "manual_proof_merge",
+                "link_confidence": "medium",
+                "review_status": "",
+                "reviewed_by": "",
+                "reviewed_at": "",
+                "link_notes": "cross-retailer validation proof",
+            }
+        )
+
+    proof_rows = [
+        {
+            "proof_name": "banana",
+            "canonical_product_id": proof_canonical_id,
+            "giant_observed_product_id": giant_row["observed_product_id"],
+            "costco_observed_product_id": costco_row["observed_product_id"],
+            "giant_example_item": giant_row["example_item_name"],
+            "costco_example_item": costco_row["example_item_name"],
+            "notes": "BANANA proof pair built from Giant and Costco enriched rows",
+        }
+    ]
+    return canonical_rows, link_rows, proof_rows
+
+
+@click.command()
+@click.option(
+    "--giant-items-enriched-csv",
+    default="giant_output/items_enriched.csv",
+    show_default=True,
+)
+@click.option(
+    "--costco-items-enriched-csv",
+    default="costco_output/items_enriched.csv",
+    show_default=True,
+)
+@click.option(
+    "--outdir",
+    default="combined_output",
+    show_default=True,
+)
+def main(giant_items_enriched_csv, costco_items_enriched_csv, outdir):
+    outdir = Path(outdir)
+    rows = read_rows(giant_items_enriched_csv) + read_rows(costco_items_enriched_csv)
+    observed_rows = build_observed_products.build_observed_products(rows)
+    canonical_rows, link_rows = build_canonical_layer.build_canonical_layer(observed_rows)
+    giant_row, costco_row = find_proof_pair(observed_rows)
+    if not giant_row or not costco_row:
+        raise click.ClickException(
+            "could not find BANANA proof pair across Giant and Costco observed products"
+        )
+    canonical_rows, link_rows, proof_rows = merge_proof_pair(
+        canonical_rows, link_rows, giant_row, costco_row
+    )
+
+    write_csv_rows(
+        outdir / "products_observed.csv",
+        observed_rows,
+        build_observed_products.OUTPUT_FIELDS,
+    )
+    write_csv_rows(
+        outdir / "products_canonical.csv",
+        canonical_rows,
+        build_canonical_layer.CANONICAL_FIELDS,
+    )
+    write_csv_rows(
+        outdir / "product_links.csv",
+        link_rows,
+        build_canonical_layer.LINK_FIELDS,
+    )
+    write_csv_rows(outdir / "proof_examples.csv", proof_rows, PROOF_FIELDS)
+    click.echo(
+        f"wrote combined outputs to {outdir} using {len(observed_rows)} observed rows"
+    )
+
+
+if __name__ == "__main__":
+    main()