Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Check airline crew rosters against duty, rest, rolling-window and consecutive-day limits
AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener
- Estimated cost
- $0.15
- Wall time
- 26s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Check crew rosters against duty-time limits Inputs: `crew.csv` (crew id and home-base time zone), `duties.csv` (duty periods in UTC, merged from the roster system and a second import). Rules: - The second import repeats some duties under a differently formatted id ("D-00123" vs "D00123"). Duties with the same crew, report time and release time are one duty. Report duty ids as "D" followed by the digits only, e.g. "D00123". - long_duty: duties longer than 13 hours (exactly 13:00 is allowed). - short_rest: duties whose rest since the same crew member's previous release is less than 10 hours (exactly 10:00 is allowed). List the later duty. - over_60h_168h: crew members who have more than 60 hours of duty inside any rolling 168-hour window (any start time, not calendar weeks). Duty time is counted only where it overlaps the window. - seven_day_runs: crew members who report for duty on 7 or more consecutive calendar dates, using the local date of each report time in the crew member's home-base time zone. - Every list is sorted. Write `out/limits.json`: `{"long_duty": [...], "short_rest": [...], "over_60h_168h": [...], "seven_day_runs": [...]}`. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.
Check about 1,400 duty periods for 40-55 crew members against four limits: maximum duty length, minimum rest, 60 hours in any rolling 168 hours, and seven consecutive local duty dates. A second import repeats some duties under reformatted ids. Rest and duty lengths sit exactly on the limits on purpose, and 'consecutive days' is judged on home-base local dates rather than UTC. 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: long_duty short_rest rolling_168h seven_day_runs
Setup notes: Batch 17 case s06-crew-duty-limits (offline, synthetic inputs, seed 9722279). Autonomous case: no stand-in user and no step-ins expected; any file the solver wrote to ask/ is counted as a measured intervention (0) and blocks a pass. Prompt = TASK.md verbatim. Hidden key kept outside the solver's work dir. Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths); pass = every check true and no ask/ files. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.25 (8 cores). Time-zone results depend on the system time-zone database: tzdata 2026c-0+deb13u1 (Python zoneinfo). Claude Code's own reported cost for claude-opus-5.5: $0.148847. Score: 4/4. 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
- 26s
- Human step-ins
- 0
- Turns
- 4
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on | 79,539 | 2,263 | $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: long_duty short_rest rolling_168h seven_day_runs
- 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
[
{
"duration_api_ms": 24944,
"stop_reason": "end_turn",
"session_id": "[REDACTED]",
"total_cost_usd": 0.1488468,
"usage": {
"input_tokens": 8,
"cache_creation_input_tokens": 11237,
"cache_read_input_tokens": 68294,
"output_tokens": 2263,
"output_tokens_details": {
"thinking_tokens": 277
},
"server_tool_use": {
"web_search_requests": 0,
"web_fetch_requests": 0
},
"service_tier": "standard",
"cache_creation": {
"ephemeral_1h_input_tokens": 11237,
"ephemeral_5m_input_tokens": 0
},
"inference_geo": "global",
"iterations": [
{
"input_tokens": 2,
"output_tokens": 529,
"cache_read_input_tokens": 19371,
"cache_creation_input_tokens": 4012,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 4012
},
"type": "message"
}
],
"speed": "standard",
"fallback_credit": null
},
"modelUsage": {
"claude-opus-5-5": {
"inputTokens": 8,
"outputTokens": 2263,
"cacheReadInputTokens": 68294,
"cacheCreationInputTokens": 11237,
"webSearchRequests": 0,
"costUSD": 0.1488468,
"contextWindow": 1000000,
"maxOutputTokens": 128000,
"thinkingTokens": 277,
"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,
"max_depth": 0,
"spawned_by_subagents": 0,
"completed": 0,
"failed": 0,
"killed": {
"parent": 0,
"user": 0,
"system": 0
},
"refused": {
"depth_limit": 0,
"concurrency_limit": 0,
"budget": 0
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"by_type": {}
},
"safety_stops": 0,
"is_error": false,
"num_turns": 4,
"subtype": "success",
"api_error_status": null,
"result": "I've written `out/limits.json` with all four lists, each sorted:\n\n| Check | Count |\n|---|---|\n| `long_duty` (longer than 13:00) | 110 duties |\n| `short_rest` (under 10:00 since the previous release; the later duty is listed) | 131 duties |\n| `over_60h_168h` | 45 of 51 crew |\n| `seven_day_runs` (7+ consecutive local report dates) | 26 crew |\n\nI checked the raw data first. The only problem was the repeated duties from the second import (\"D-12345\" style ids). I counted those once, grouping by crew, report time and release time, and reported every id as \"D\" plus its digits. There were no id clashes, no duties ending before they started, and no overlapping duties for the same person, so nothing else needed fixing.\n\nHow each check was worked out:\n- **60 hours in 168:** for each crew member, I tested windows starting at every report time and windows ending at every release time. The busiest possible window always lines up with one of these. Only the part of a duty inside the window counts.\n- **Seven-day runs:** each report time was converted to the crew member's home-base time zone before taking its date.\n- **Short rest:** each duty is compared with the same crew member's duty just before it, by report time.\n\nMost crew (45 of 51) go over 60 hours in some 168-hour window. That's a lot, but it's what this synthetic data produces under the rules as written.\n\nThe script is at `/tmp/s/a.py`, kept outside the work folder.",
"ttft_ms": 2653,
"type": "result",
"duration_ms": 25381,
"uuid": "[REDACTED]",
"ttft_stream_ms": 1910,
"time_to_request_ms": 226,
"first_content_frame_ms": 1911,
"queued_turn_count": 0,
"result_index": 0
}
]