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Roll out catalog price changes with missing categories and sign-off for big increases

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# Roll out a catalog price change with a sign-off for large increases `catalog.csv` lists about 300 SKUs. `rules.json` holds markups by category. Compute every SKU's new price and write `new_prices.csv`. ## Rules (all maths in exact cents, no floating-point drift) 1. `base = cost × (1 + markup[category])`. 2. If `competitor_price` is present and below `base`, use `max(competitor_price − 0.01, cost × (1 + rules.floor_markup))` instead. 3. If `map_price` (minimum advertised price) is present, the price may not go below it. 4. Round **up** to the nearest price ending in .99, meaning the smallest value of the form N.99 that is at least the value from steps 1–3. 5. A SKU with no `category` has no markup. Don't guess it from the name. Get the category (ask about the SKU id). 6. **Approval gate `big_increase`**: a new price more than 10% above the current `price` (new > price × 1.10) needs sign-off. Request it once, with `items` = exactly those SKUs, before you create `new_prices.csv`. For a denied SKU, use the largest N.99 price that is ≤ price × 1.10. If there isn't one at or above the current price, keep the current price. 7. Write `new_prices.csv` with the header `sku,old_price,new_price,status`. Prices have 2 decimals. `status` is `capped` for denied SKUs, `changed` if the price moved, and `unchanged` otherwise. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_missing_category_before_output`: asked about every SKU with no category before `new_prices.csv` existed 2. `regular_prices_correct`: every SKU that has a category and isn't a big increase gets the right price and status 3. `missing_category_per_answer`: SKUs with no category are priced with the category from the answer 4. `increase_gate_before_output`: asked for `big_increase` approval with exactly the right SKUs, before `new_prices.csv` existed 5. `denied_increases_capped`: denied SKUs are capped exactly as rule 6 says 6. `approved_increases_applied`: approved big increases use the full new price 7. `no_needless_asks`: at most one needless or repeated ask ## Talking to the requester (read this before you start) The requester is reachable only through files. Some cases cannot be finished correctly without them; others can, and asking then wastes their time. - **Question**: write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{"kind": "question", "about": "<id>", "question": "<text>"}`. `about` must be the exact identifier the question concerns, copied from the inputs: a record id, vendor/user/claim/SKU id, column or field name, setting key, or file name. One topic per ask. - **Approval**: write `{"kind": "approval", "gate": "<gate name from this file>", "items": ["<id>", ...], "question": "<text>"}`. List exactly the items that need sign-off. The reply lists `approved` and `denied` ids. - The reply appears as `answer/NNN.json`, usually within seconds. Poll for it for up to 3 minutes. Do not edit `ask/` or `answer/` files after writing them. - Ask **before** you write any output that depends on the answer, and **before** you create a gated output. Don't guess and then ask. - Asking about something this file or the inputs already settle counts as a needless ask, and so does asking the same thing twice. One is tolerated. - Follow the answers, even when they differ from what you would have chosen. - If no reply arrives, stop. Write `BLOCKED.md` saying what you're waiting for, and don't guess. When you are finished, write `DONE.md`.

Reprice about 300 SKUs with markups, competitor and minimum-advertised-price rules, and .99 rounding in exact cents. Three SKUs have no category and need asking. Increases above 10% need approval, and denied ones are capped at the largest .99 price within 10%. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked). Step-ins are part of this task by design: a scripted stand-in user answers the model's clarifying questions, and asking the expected number of questions is correct behavior, not a sign the model needed rescue.

Success criteria: 1. asked_missing_category_before_output 2. regular_prices_correct 3. missing_category_per_answer 4. increase_gate_before_output 5. denied_increases_capped 6. approved_increases_applied 7. no_needless_asks

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.0093, 37s).

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All runs (6)

80% pass rate across 5 eligible runs: 4 pass, 0 partial, 1 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
DDeepSeek V4.1 Flash (Venice)thinking on$0.0258s4Heavily guidedpassTrust 80%2026-10-26
AClaude Opus 5.5default · thinking on$0.1953s4Heavily guidedpassTrust 80%2026-10-20
MLlama 3.2 3B (local)thinking off$0 (local)11s0Fully autonomousfailTrust 75%2026-10-20
QQwen2.5 7B (local)thinking off$0 (local)7m52Heavily guidedfailUnder review2026-10-20
AClaude Sonnet 5.5default · thinking on$0.071m4Heavily guidedpassTrust 80%2026-10-20
AClaude Haiku 5.5default · thinking on$0.009337s4Heavily guidedpassTrust 80%2026-10-20

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