updated readme with Review steps

README.md

@@ -1,227 +1,118 @@
 # scrape-giant
 
-Small grocery-history pipeline for Giant and Costco receipt data.
+Small CLI pipeline for pulling purchase history from Giant and Costco, enriching line items, and building a reviewable cross-retailer purchase dataset.
 
-This repo is still a manual, stepwise pipeline. There is no single orchestrator
-script yet. Each stage is run directly, and later stages depend on files
-produced by earlier stages.
+There is no one-shot runner yet. Today, you run the scripts step by step from the terminal.
 
-## What The Project Does
+## What It Does
 
-The current flow is:
+- `scrape_giant.py`: download Giant orders and items
+- `enrich_giant.py`: normalize Giant line items
+- `scrape_costco.py`: download Costco orders and items
+- `enrich_costco.py`: normalize Costco line items
+- `build_purchases.py`: combine retailer outputs into one purchase table
+- `review_products.py`: review unresolved product matches in the terminal
 
-1. acquire raw Giant receipt/history data
-2. enrich Giant line items into a shared enriched-item schema
-3. acquire raw Costco receipt data
-4. enrich Costco line items into the same shared enriched-item schema
-5. build observed-product, review, and canonical-product layers
-6. validate that Giant and Costco can flow through the same downstream model
+## Requirements
 
-Raw retailer JSON remains the source of truth.
+- Python 3.10+
+- Firefox installed with active Giant and Costco sessions
 
-## Current Scripts
-
-- `scrape_giant.py`
-  Fetch Giant in-store history and order detail payloads from an active Firefox
-  session.
-- `scrape_costco.py`
-  Fetch Costco receipt summary/detail payloads from an active Firefox session.
-  Costco currently prefers `.env` header values first, then falls back to exact
-  Firefox local-storage values for session auth.
-- `enrich_giant.py`
-  Parse Giant raw order JSON into `giant_output/items_enriched.csv`.
-- `enrich_costco.py`
-  Parse Costco raw receipt JSON into `costco_output/items_enriched.csv`.
-- `build_observed_products.py`
-  Build retailer-facing observed products from enriched rows.
-- `build_review_queue.py`
-  Build a manual review queue for low-confidence or unresolved observed
-  products.
-- `build_canonical_layer.py`
-  Build shared canonical products and observed-to-canonical links.
-- `validate_cross_retailer_flow.py`
-  Write a proof/check output showing that Giant and Costco can meet in the same
-  downstream model.
-
-## Manual Pipeline
-
-Run these from the repo root with the venv active, or call them through
-`./venv/bin/python`.
-
-### 1. Acquire Giant raw data
+## Install
 
 ```bash
-./venv/bin/python scrape_giant.py
+python -m venv venv
+./venv/scripts/activate
+pip install -r requirements.txt
 ```
 
-Inputs:
-- active Firefox session for `giantfood.com`
-- `GIANT_USER_ID` and `GIANT_LOYALTY_NUMBER` from `.env`, shell env, or prompt
+## Optional `.env`
 
-Outputs:
-- `giant_output/raw/history.json`
-- `giant_output/raw/<order_id>.json`
+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.
+
+```env
+GIANT_USER_ID=...
+GIANT_LOYALTY_NUMBER=...
+
+# Costco can use these if present, but it can also pull session values from Firefox.
+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
+```
+
+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
+```
+
+## Key Outputs
+
+Giant:
 - `giant_output/orders.csv`
 - `giant_output/items.csv`
-
-### 2. Enrich Giant data
-
-```bash
-./venv/bin/python enrich_giant.py
-```
-
-Input:
-- `giant_output/raw/*.json`
-
-Output:
 - `giant_output/items_enriched.csv`
 
-### 3. Acquire Costco raw data
-
-```bash
-./venv/bin/python scrape_costco.py
-```
-
-Optional useful flags:
-
-```bash
-./venv/bin/python scrape_costco.py --months-back 36
-./venv/bin/python scrape_costco.py --firefox-profile-dir "C:\\Users\\you\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\xxxx.default-release"
-```
-
-Inputs:
-- active Firefox session for `costco.com`
-- optional `.env` values:
-  - `COSTCO_X_AUTHORIZATION`
-  - `COSTCO_X_WCS_CLIENTID`
-  - `COSTCO_CLIENT_IDENTIFIER`
-- if `COSTCO_X_AUTHORIZATION` is absent, the script falls back to exact Firefox
-  local-storage values:
-  - `idToken` -> sent as `Bearer <idToken>`
-  - `clientID` -> used as `costco-x-wcs-clientId` when env is blank
-
-Outputs:
-- `costco_output/raw/summary.json`
-- `costco_output/raw/summary_requests.json`
-- `costco_output/raw/<receipt_id>-<timestamp>.json`
+Costco:
 - `costco_output/orders.csv`
 - `costco_output/items.csv`
-
-### 4. Enrich Costco data
-
-```bash
-./venv/bin/python enrich_costco.py
-```
-
-Input:
-- `costco_output/raw/*.json`
-
-Output:
 - `costco_output/items_enriched.csv`
 
-### 5. Build shared downstream layers
+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`
 
-```bash
-./venv/bin/python build_observed_products.py
-./venv/bin/python build_review_queue.py
-./venv/bin/python build_canonical_layer.py
-```
+## Review Workflow
 
-These scripts consume the enriched item files and generate the downstream
-product-model outputs.
+`review_products.py` is the manual cleanup step for unresolved or weakly unified items.
 
-Current outputs on disk:
+In the terminal, you can:
+- link an item to an existing canonical product
+- create a new canonical product
+- exclude an item
+- skip it for later
 
-- retailer-facing:
-  - `giant_output/products_observed.csv`
-  - `giant_output/review_queue.csv`
-  - `giant_output/products_canonical.csv`
-  - `giant_output/product_links.csv`
-- cross-retailer proof/check output:
-  - `combined_output/products_observed.csv`
-  - `combined_output/products_canonical.csv`
-  - `combined_output/product_links.csv`
-  - `combined_output/proof_examples.csv`
-
-### 6. Validate cross-retailer flow
-
-```bash
-./venv/bin/python validate_cross_retailer_flow.py
-```
-
-This is a proof/check step, not the main acquisition path.
-
-## Inputs And Outputs By Directory
-
-### `giant_output/`
-
-Inputs to this layer:
-- Firefox session data for Giant
-- Giant raw JSON payloads
-
-Generated files:
-- `raw/history.json`
-- `raw/<order_id>.json`
-- `orders.csv`
-- `items.csv`
-- `items_enriched.csv`
-- `products_observed.csv`
-- `review_queue.csv`
-- `products_canonical.csv`
-- `product_links.csv`
-
-### `costco_output/`
-
-Inputs to this layer:
-- Firefox session data for Costco
-- Costco raw GraphQL receipt payloads
-
-Generated files:
-- `raw/summary.json`
-- `raw/summary_requests.json`
-- `raw/<receipt_id>-<timestamp>.json`
-- `orders.csv`
-- `items.csv`
-- `items_enriched.csv`
-
-### `combined_output/`
-
-Generated by cross-retailer proof/build scripts:
-- `products_observed.csv`
-- `products_canonical.csv`
-- `product_links.csv`
-- `proof_examples.csv`
+Those decisions are saved and reused on later runs.
 
 ## Notes
 
-- The pipeline is intentionally simple and currently manual.
-- Scraping is retailer-specific and fragile; downstream modeling is shared only
-  after enrichment.
-- `summary_requests.json` is diagnostic metadata from Costco summary enumeration
-  and is not a receipt payload.
-- `enrich_costco.py` skips that file and only parses receipt payloads.
-- The repo may contain archived or sample output files under `archive/`; they
-  are not part of the active scrape path.
+- 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.
+- `validate_cross_retailer_flow.py` is a proof/check script, not a required production step.
 
-## Verification
-
-Run the full test suite with:
+## Test
 
 ```bash
 ./venv/bin/python -m unittest discover -s tests
 ```
 
-Useful one-off checks:
-
-```bash
-./venv/bin/python scrape_giant.py --help
-./venv/bin/python scrape_costco.py --help
-./venv/bin/python enrich_giant.py
-./venv/bin/python enrich_costco.py
-```
-
 ## Project Docs
 
-- `pm/tasks.org`
-- `pm/data-model.org`
-- `pm/scrape-giant.org`
+- `pm/tasks.org`: task tracking
+- `pm/data-model.org`: current data model notes
+- `pm/review-workflow.org`: review and resolution workflow

build_purchases.py

@@ -0,0 +1,464 @@
+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
+<<<<<<< HEAD
+from layer_helpers import read_csv_rows, stable_id, write_csv_rows
+=======
+from layer_helpers import read_csv_rows, write_csv_rows
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+
+
+PURCHASE_FIELDS = [
+    "purchase_date",
+    "retailer",
+    "order_id",
+    "line_no",
+    "observed_item_key",
+    "observed_product_id",
+    "canonical_product_id",
+<<<<<<< HEAD
+    "review_status",
+    "resolution_action",
+=======
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+    "raw_item_name",
+    "normalized_item_name",
+    "retailer_item_id",
+    "upc",
+    "qty",
+    "unit",
+    "pack_qty",
+    "size_value",
+    "size_unit",
+    "measure_type",
+    "line_total",
+    "unit_price",
+    "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",
+]
+
+<<<<<<< HEAD
+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",
+]
+
+=======
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+
+def decimal_or_zero(value):
+    return to_decimal(value) or Decimal("0")
+
+
+def derive_metrics(row):
+    line_total = to_decimal(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
+    }
+
+
+<<<<<<< HEAD
+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):
+=======
+def build_link_lookup(enriched_rows):
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+    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
+    }
+<<<<<<< HEAD
+    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] = ""
+=======
+    return observed_id_by_key, canonical_id_by_observed
+
+
+def build_purchase_rows(giant_enriched_rows, costco_enriched_rows, giant_orders, costco_orders):
+    all_enriched_rows = giant_enriched_rows + costco_enriched_rows
+    observed_id_by_key, canonical_id_by_observed = build_link_lookup(all_enriched_rows)
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+    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)
+<<<<<<< HEAD
+        resolution = resolution_lookup.get(observed_product_id, {})
+=======
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+        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, ""),
+<<<<<<< HEAD
+                "review_status": resolution.get("status", ""),
+                "resolution_action": resolution.get("resolution_action", ""),
+=======
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+                "raw_item_name": row["item_name"],
+                "normalized_item_name": row["item_name_norm"],
+                "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"],
+                "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,
+            }
+        )
+<<<<<<< HEAD
+    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"])
+=======
+    return purchase_rows
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+
+
+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)
+<<<<<<< HEAD
+@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)
+=======
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+@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,
+<<<<<<< HEAD
+    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(
+=======
+    output_csv,
+    examples_csv,
+):
+    purchase_rows = build_purchase_rows(
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+        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),
+<<<<<<< HEAD
+        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}, "
+=======
+    )
+    example_rows = build_comparison_examples(purchase_rows)
+    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} "
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+        f"and {len(example_rows)} comparison examples to {examples_csv}"
+    )
+
+
+if __name__ == "__main__":
+    main()

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/tasks.org

@@ -276,7 +276,7 @@
 - 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
-* [ ] t1.8.7: simplify costco session bootstrap and remove over-abstraction (2-4 commits)
+* [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
@@ -302,12 +302,23 @@
 - no new heuristics in this task
 
 ** evidence
-- commit:
-- tests:
-- date:  
-* [ ] t1.9: compute normalized comparison metrics (2-4 commits)
+- 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`
@@ -318,18 +329,44 @@
   - 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:
-- tests:
-- date:
+- 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
 * [ ] t1.10: add optional llm-assisted suggestion workflow for unresolved products (2-4 commits)
 
 ** acceptance criteria

review_products.py

@@ -0,0 +1,223 @@
+from collections import defaultdict
+from datetime import date
+from pathlib import Path
+
+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)
+
+
+def prompt_resolution(queue_row, catalog_rows):
+    click.echo("")
+    click.echo(f"observed_product_id: {queue_row['observed_product_id']}")
+    click.echo(f"retailer: {queue_row['retailer']}")
+    click.echo(f"raw names: {queue_row['raw_item_names']}")
+    click.echo(f"normalized names: {queue_row['normalized_names']}")
+    click.echo(f"upcs: {queue_row['upc_values']}")
+    click.echo(f"example prices: {queue_row['example_prices']}")
+    click.echo(f"seen count: {queue_row['seen_count']}")
+    click.echo("actions: [l]ink existing  [n]ew canonical  [x]exclude  [s]kip  [q]uit")
+    action = click.prompt("action", type=click.Choice(["l", "n", "x", "s", "q"]))
+    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("exclude notes", 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":
+        click.echo("existing canonicals:")
+        for row in catalog_rows[:10]:
+            click.echo(f"  {row['canonical_product_id']}  {row['canonical_name']}")
+        canonical_product_id = click.prompt("canonical product id", type=str)
+        notes = click.prompt("link notes", 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("canonical name", type=str)
+    category = click.prompt("category", default="", show_default=False)
+    product_type = click.prompt("product type", default="", show_default=False)
+    notes = click.prompt("notes", 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")}
+    reviewed = 0
+    for queue_row in queue_rows:
+        if limit and reviewed >= limit:
+            break
+        result = prompt_resolution(queue_row, catalog_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_costco.py

@@ -670,6 +670,13 @@ def main(
         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)
 

tests/test_purchases.py

@@ -0,0 +1,280 @@
+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",
+                "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",
+            }
+        ]
+
+<<<<<<< HEAD
+        rows, _observed, _canon, _links = build_purchases.build_purchase_rows(
+=======
+        rows = build_purchases.build_purchase_rows(
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+            [giant_row],
+            [costco_row],
+            giant_orders,
+            costco_orders,
+<<<<<<< HEAD
+            [],
+=======
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+        )
+
+        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})
+
+    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"
+            purchases_csv = Path(tmpdir) / "combined" / "purchases.csv"
+            examples_csv = Path(tmpdir) / "combined" / "comparison_examples.csv"
+
+            fieldnames = enrich_costco.OUTPUT_FIELDS
+            rows = []
+            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",
+                }
+            )
+            rows.extend([giant_row, costco_row])
+
+            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)
+
+            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_id", "store_name", "store_number", "store_city", "store_state"])
+                    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),
+<<<<<<< HEAD
+                resolutions_csv=str(Path(tmpdir) / "review_resolutions.csv"),
+                catalog_csv=str(Path(tmpdir) / "canonical_catalog.csv"),
+                links_csv=str(Path(tmpdir) / "product_links.csv"),
+=======
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+                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))
+
+<<<<<<< HEAD
+    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"])
+
+=======
+>>>>>>> be1bf63 (Build pivot-ready purchase log)
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_review_workflow.py

@@ -0,0 +1,100 @@
+import csv
+import tempfile
+import unittest
+from pathlib import Path
+from unittest import mock
+
+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_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=[
+                        "observed_product_id",
+                        "canonical_product_id",
+                        "retailer",
+                        "raw_item_name",
+                        "normalized_item_name",
+                        "upc",
+                        "line_total",
+                    ],
+                )
+                writer.writeheader()
+                writer.writerow(
+                    {
+                        "observed_product_id": "gobs_ice",
+                        "canonical_product_id": "",
+                        "retailer": "giant",
+                        "raw_item_name": "SB BAGGED ICE 20LB",
+                        "normalized_item_name": "BAGGED ICE",
+                        "upc": "",
+                        "line_total": "3.50",
+                    }
+                )
+
+            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()