Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Monthly regional report from messy orders CSV + shipments JSONL + weekday-only FX table
AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-17 · by @bobsiopener
- Estimated cost
- $0.23
- Wall time
- 45s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
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-opus-5.5: $0.225847. 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
- $0.23
- Wall time
- 45s
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on | 134,926 | 4,755 | $0.23 |
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_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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"session_id": "[REDACTED]",
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"output_tokens": 4755,
"output_tokens_details": {
"thinking_tokens": 940
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"server_tool_use": {
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"service_tier": "standard",
"cache_creation": {
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},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
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"modelUsage": {
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"webSearchRequests": 0,
"costUSD": 0.2258474,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 940,
"canonicalModel": "claude-opus-5-5",
"provider": "firstParty",
"costBasis": "list"
}
},
"permission_denials": [],
"terminal_reason": "completed",
"fast_mode_state": "off",
"fast_mode_disabled_reason": "sdk_opt_in_required",
"subagent_stats": {
"spawned": 0,
"requested": {
"background": 0,
"foreground": 0,
"unset": 0
},
"started_in_background": 0,
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"refused": {
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"safety_stops": 0,
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"subtype": "success",
"api_error_status": null,
"result": "I wrote `report.py` and ran it. It created `report.json`, which covers all four regions for 2026-01 through 2026-04. Totals: 709 orders, 50 unshipped, 212 late. As a cross-check, the 847 data rows minus 47 exact duplicates leaves 800, and taking out 91 unique cancelled orders leaves 709, which matches. I didn't check any individual month's revenue by hand.\n\nHow it handles the messy inputs:\n- **Regions:** spaces and case are ignored, and both full names and single letters work (`n`, `NORTH`, `\" North \"`, `E`, \u2026). An unrecognised region stops the run with an error instead of being silently put in a bucket.\n- **Duplicates:** a row is dropped if it matches an earlier one after ignoring ID case and extra spaces, so `o-00025` and `O-00025` with the same values count as duplicates.\n- **Amounts:** USD and GBP treat a comma as a thousands separator; EUR treats it as a decimal point. If an amount has both `,` and `.`, whichever comes last is the decimal point.\n- **Exchange rates:** each order uses the rate for its date, or the closest earlier date if there's none that day.\n- **Shipments:** the first shipment is the earliest `shipped_at` converted to UTC, not the first one in the file. Order IDs in shipments are also matched case-insensitively.\n\nA few of the rules were ambiguous, so I made these choices:\n- **Same ID, different details:** two rows with the same ID but different values aren't exact duplicates, so both are counted as separate orders. This data has no such case.\n- **Cancelled orders:** if any row for an order ID is cancelled, that ID is dropped completely, including any other rows and shipments for it.\n- **Orders older than all exchange rates:** these use the earliest rate available. None appear in the current data.\n\nNothing is hard-coded, so it should work on the grader's freshly generated files.",
"ttft_ms": 3151,
"type": "result",
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"uuid": "[REDACTED]",
"ttft_stream_ms": 1257,
"time_to_request_ms": 189,
"first_content_frame_ms": 1257,
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]