Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Monthly regional report from messy orders CSV + shipments JSONL + weekday-only FX table

AClaude Sonnet 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-17 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.07
Wall time: 19s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: monthly regional order report Inputs: `orders.csv`, `shipments.jsonl`, `fx.csv`. Write `report.py` (Python stdlib only) that reads those three files from the current directory and writes `report.json`, then run it. `report.json` shape: `{"<Region>": {"YYYY-MM": {"orders": int, "revenue_eur": float, "unshipped": int, "late": int}}}` Rules: - Regions are North/South/East/West; the raw data uses many spellings/abbreviations/cases/whitespace (n, NORTH, " North ", E, ...). Normalise to those four names. - Order IDs are case-insensitive (`o-00012` = `O-00012`). Rows that are exact duplicates of an earlier row are dropped. Cancelled orders (any case) are excluded entirely. - `order_date` is ISO `YYYY-MM-DD`, `DD/MM/YYYY`, or `Mon DD YYYY`. The month bucket is the order date's month. - Amounts: USD amounts may have thousands separators (`1,234.50`); EUR amounts may use a decimal comma (`12,50`); GBP is plain. Convert to EUR with `fx.csv` (EUR per 1 unit; EUR itself is 1.0) using the rate on the order date, or if there is none that day (weekends), the most recent earlier date. Round `revenue_eur` to 2 decimals only at the end (sum unrounded values). - `unshipped` = kept orders with no shipment record. `late` = kept orders whose **first** shipment date (UTC date of `shipped_at`) is more than 5 days after the order date. Shipments for unknown orders and blank lines are ignored. `report.py` will also be re-run by the grader on freshly generated data with the same format, so don't hard-code anything. No network.

Many small traps: region aliases, case-insensitive IDs, exact-duplicate rows, three date formats, locale-specific amounts, weekend FX fallback, first-shipment lateness across Z/+00:00, orphan shipments; re-run on fresh data blocks hard-coding. 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_exact: every region/month cell in report.json matches the key (counts exact, revenue_eur within 0.01) and no extra cells. 2. fresh_data_exact: the agent's report.py, re-run on a freshly generated dataset (seed 9191), produces an exactly matching report.

Setup notes: Use Case Scout batch 5 case e07-shipments-reconcile (offline fixture). Prepared with batch5/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-sonnet-5.5: $0.072444. Score: 2/2 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: $0.07
Wall time: 19s
Human step-ins: 0
Turns: 4

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on	81,414	2,595	$0.07

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-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_exact: every region/month cell in report.json matches the key (counts exact, revenue_eur within 0.01) and no extra cells. 2. fresh_data_exact: the agent's report.py, re-run on a freshly generated dataset (seed 9191), produces an exactly matching report.
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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    "canonicalModel": "claude-sonnet-5-5",
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  "result": "I wrote `report.py` and ran it. It produced `report.json` with no errors, but I haven't checked the totals against an independent calculation. It reads the three files from the current directory, uses only the standard library and hard-codes nothing from this data.\n\nI checked the data for the points where your rules could be read two ways:\n- **Duplicates:** `orders.csv` has 847 rows, 47 of them exact repeats. That leaves 800 rows and 800 distinct order IDs once case is ignored, so the exact-row rule and the case-insensitive ID rule agree on this data.\n- **Shipment timestamps:** they use either a `Z` suffix or a `+00:00` offset, and I convert both to UTC before taking the date.\n- **First shipment:** where an order has several shipment records, I use the earliest by timestamp, not the first line in the file.\n- **FX:** `fx.csv` has USD and GBP rates only. Weekend dates use the most recent earlier rate, and EUR is 1.0.\n\nRounding happens once at the end, on the summed unrounded values. Regions and months are sorted in the output.",
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]