Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Monthly FIFO inventory report (COGS, write-offs, oversell exceptions) from a messy, shuffled stock-movement export

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-19 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.15
Wall time: 24s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: monthly FIFO inventory report `transactions.csv` is an export of a small shop's stock movements. Write `fifo.py` (Python stdlib only) that reads `transactions.csv` from the current directory and writes `report.csv` and `exceptions.json` there, following the rules below exactly. Run it. The grader compares your two output files with the correct ones, then re-runs `fifo.py` on a freshly generated file of the same kind (different values, same rules), so do not hard-code anything. No network. ## Input Columns: `seq,date,sku,type,qty,unit_amount`. 1. `seq` is a unique integer per transaction. The export contains some exact duplicate rows (same `seq`); a row whose `seq` was already seen must be ignored. 2. Rows are NOT in order. Process transactions sorted by `date` (YYYY-MM-DD), then by `seq` ascending. 3. `sku`: trim spaces and upper-case it (` ab-1` and `AB-1` are the same SKU). `type` is case-insensitive and is one of `BUY`, `SELL`, `WRITE_OFF`. `qty` is a positive integer, possibly with surrounding spaces. 4. `unit_amount` is a decimal number that may have a leading `$`, thousands separators (`,`) and up to 4 decimal places, e.g. `$1,234.5678`. For BUY it is the unit cost; for SELL the unit sale price; for WRITE_OFF it is empty. ## Costing 5. Each BUY creates a lot (qty, unit cost). SELL and WRITE_OFF consume units from the oldest remaining lots first (FIFO), across as many lots as needed. 6. COGS of a SELL = sum over consumed units of their lot's unit cost; revenue = units actually sold x unit price. A WRITE_OFF's consumed cost is write-off cost (no revenue, not COGS, not units sold). 7. If a SELL or WRITE_OFF asks for more units than are on hand, consume everything on hand and record the missing units as an exception; revenue only counts the units actually sold. 8. Use exact decimal arithmetic. Round to 2 decimal places (half-up) only when writing the report; every reported number must equal the exact value rounded that way. ## Output 9. `report.csv` with header `month,sku,units_sold,revenue,cogs,write_off_cost,gross_margin,ending_units,ending_value`: one row per (month, sku) for every SKU that has at least one transaction in that month (month = `YYYY-MM`), sorted by month then sku. `gross_margin = revenue - cogs`, computed from the exact unrounded revenue and COGS and then rounded (so it can differ by 0.01 from subtracting the two rounded columns). `ending_units` and `ending_value` are that SKU's units on hand and their total cost at the end of that month (after all its transactions up to and including that month). Money columns are written with exactly 2 decimals (e.g. `12.50`, `0.00`, `-3.10`), no `$` or separators; unit columns as integers. 10. `exceptions.json`: a JSON list with one object `{"seq": <int>, "sku": "<normalised sku>", "short": <int missing units>}` per SELL/WRITE_OFF that was short, in processing order (`[]` if none).

Needs dedupe by seq, date+seq ordering, SKU/type normalisation, $/thousands parsing, multi-lot FIFO consumption, shortfall handling, exact Decimal maths with rounding only at output (gross margin from unrounded values), and a fresh-seed rerun blocks hand-editing. 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).

Success criteria: 1. report_correct: report.csv header, rows, order and every value exact to the cent, money columns with 2 decimals. 2. exceptions_correct: exceptions.json equals the expected list in processing order. 3. fresh_data_ok: fifo.py re-run on fresh data (seed 4747) produces correct report.csv and exceptions.json.

Setup notes: Use Case Scout batch 7 case g04-fifo-inventory-cogs (offline fixture). Prepared with batch7/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Claude Code's own reported cost for claude-opus-5.5: $0.146326. Score: 3/3 checks. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.15
Wall time: 24s
Human step-ins: 0
Turns: 4

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	81,062	2,587	$0.15

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: 1. report_correct: report.csv header, rows, order and every value exact to the cent, money columns with 2 decimals. 2. exceptions_correct: exceptions.json equals the expected list in processing order. 3. fresh_data_ok: fifo.py re-run on fresh data (seed 4747) produces correct report.csv and exceptions.json.
Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Automated checks

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

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  "result": "I wrote `fifo.py` and ran it on `transactions.csv`. It produced `report.csv` (32 rows, one per month and SKU, covering March to June 2026) and `exceptions.json`. There's no reference output here, so I haven't checked the numbers against correct ones. I only looked at the first rows and confirmed they look sensible.\n\nWhat the script does:\n- **Input clean-up:** it skips rows whose `seq` was already seen, then processes everything sorted by date and then `seq`. It trims and upper-cases SKUs, matches `type` regardless of case, and strips spaces from `qty`. In `unit_amount` it removes `$` and thousands commas.\n- **Costing:** each BUY becomes a lot, and SELL and WRITE_OFF use up the oldest lots first. All arithmetic uses exact decimals, and nothing is rounded until the report is written.\n- **Short sales and write-offs:** if a SELL or WRITE_OFF asks for more than is on hand, it takes everything available and logs the missing units in `exceptions.json`. Revenue counts only the units actually sold. This happens often in this file: the first entries are `seq` 1003, 1009, 1012 and so on.\n- **Report columns:** gross margin is calculated from the unrounded revenue and COGS, then rounded half-up. Ending units and value are the stock left after that SKU's last transaction of the month.\n\nNothing is hard-coded, so it should work on a newly generated file of the same format. If it meets a `type` other than BUY, SELL or WRITE_OFF, it stops with an error rather than skipping the row.",
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