Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Catch log checked against a made-up fishing regulations table

AClaude Sonnet 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.06
Wall time: 17s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Catch log checked against a made-up regulations table `input.json` names `regulations.json` and `catch_log.csv`. All rules, species codes and data are **fictional** (not real fishing regulations). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. Fresh files are used too. ## Data - `regulations.json`: `species` maps a code to `aliases`, `season` ["MM-DD", "MM-DD"] inclusive (if start > end the season wraps over New Year), `min_in`, `max_in` (null = no maximum), `bag` (legal kept fish per angler per day for that species); `aggregate_daily` = legal kept fish per angler per day, all species. - `catch_log.csv`: `date` (YYYY-MM-DD), `angler`, `species`, `length`, `unit` (`in` or `cm`, any case), `action` (`kept` or `released`, any case). Process rows in file order; row numbers start at 1 for the first data row. ## Rules (first match wins) 1. Species: trim, case-insensitive match to a code or alias; no match -> `unknown_species` (species null). 2. `released` -> status `released` (never a violation, never counted). 3. Kept fish: length in inches (cm / 2.54 rounded to 2 decimals). Then: not in season -> `out_of_season`; below min -> `undersize`; above max -> `oversize`; angler already has `bag` legal fish of that species that date -> `over_bag`; angler already has `aggregate_daily` legal fish that date -> `over_aggregate`; else `legal` (and it counts toward the limits). Boundaries are legal (equal to min or max, first/last season day). 4. Every status except `legal` and `released` is a violation for that angler. ## answer.json `{"rows": [{"row", "species", "status"}, ...], "violations_by_angler": {angler: n} (only anglers with >0, sorted by name), "legal_kept_by_species": {code: n} (only >0, sorted)}` ## Scoring Hidden checks (partial credit; all must pass): `species_and_release` (species codes and which rows are released), `row_statuses` (every row exact), `summaries`, `fresh_run_1`, `fresh_run_2`. 60-second limit per run.

Aliases with odd spacing and case, cm conversion near limits, a season that wraps New Year with boundary dates, slot maximum, and bag/aggregate limits that only count legal fish in log order. 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).

Benchmark code for comparing AI models, not navigation or safety software.

Success criteria: 1. species_and_release 2. row_statuses 3. summaries 4. fresh_run_1 5. fresh_run_2

Setup notes: Use Case Scout batch 10 case k04-catch-log-regulations (offline fixture). Prepared with batch10/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: $0.055685. Score: 5/5. 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: $0.06
Wall time: 17s
Human step-ins: 0
Turns: 3

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on	58,752	1,746	$0.06

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. species_and_release 2. row_statuses 3. summaries 4. fresh_run_1 5. fresh_run_2
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 `solve.py` and ran it on the supplied files. It produced `answer.json` with 55 rows, with `ana` at 15 violations and `ben` at 12. I haven't checked the output against the hidden checks. I also haven't run it on other input files, so the fresh-run checks are untested.\n\nThe legal kept fish came to 5 BSB, 3 FLK, 5 STB and 6 TOG.\n\nThree choices I made where the spec is silent:\n- **Rounding:** centimetre lengths are converted with decimal arithmetic, rounding halves up to 2 decimals. A different tie-breaking rule could change a status only on an exact tie.\n- **Unknown species:** a row with an unknown species is `unknown_species` even if it was released, since rule 1 comes first.\n- **Angler names:** names are trimmed but not case-folded, so `Ana` and `ana` would count as different anglers.",
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