Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Earliest-arrival journey planner over a timetable (minimum change times, directed footpaths never chained, trip limit, after-midnight times)

AClaude Opus 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: $0.25
Wall time: 1m
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Earliest-arrival journey planner with change times and footpaths `transit.py` plans trips over our city's offline timetable (`stops.csv`, `timetable.csv`, `footpaths.csv` in a directory). The draft only finds direct trips. Rewrite it (Python 3 stdlib only) so that `Network(directory).query(src, dst, start, max_trips)` follows these rules exactly. ## Data 1. `stops.csv`: `stop,min_change` (seconds). `timetable.csv`: `trip_id,seq,stop,arr,dep`; a trip's stops are visited in increasing `seq` (rows may be in any order). Times are `HH:MM:SS` and hours may be 24 or more (after midnight of the same service day). `footpaths.csv`: `from,to,seconds`, **directed**; if a pair appears several times the smallest time counts; rows with `from == to` are ignored. ## Journeys 2. You are at `src` at time `start` (`HH:MM:SS`). You may **board** a trip at a stop at that row's `dep` if you are ready there at or before `dep`, ride it, and **alight** at any later stop of that trip at its `arr`. 3. **Ready times:** at `src` you are ready at `start`. After alighting at stop s at time a you are ready to board **another** trip at s at `a + min_change[s]`. After walking a footpath you are ready at the arrival time with no change time. 4. **Footpaths** may be walked from `src` before the first trip, after alighting from any trip, and to reach `dst` at the end, but **never two footpaths in a row** (no walking from a stop you reached on foot). 5. `max_trips` (integer >= 0) is the maximum number of trips boarded; with 0 only `src == dst` or a single footpath `src -> dst` works. 6. Reaching `dst` means alighting there or walking a footpath into it (no change time is added at the end). If `src == dst` the answer is `(start, 0)`. 7. **Answer:** `(arrival, trips)` with the earliest possible arrival at `dst` formatted `HH:MM:SS` (hours zero-padded to at least 2 digits, may be >= 24), and among journeys with that arrival the smallest number of trips; `None` if `dst` cannot be reached. An unknown stop name (not in `stops.csv`) raises `KeyError`. 8. **Speed:** on the network in this directory, building the `Network` and answering 400 random queries with `max_trips` up to 4 must take under 20 seconds in total on our shared test box. ## Scoring Hidden check groups (partial credit = fraction passed; all must pass), all on exact answers: `direct_and_parsing` (1-2, 7 on a small hand-made network), `change_times` (3), `footpath_rules` (3-4), `max_trips_and_ties` (5, 7), `after_midnight` (1, 7), `unreachable_and_errors` (6-7), `city_queries` (150 queries on this directory's network), `fresh_city_queries` (150 queries on a freshly generated network of the same kind), `speed` (8).

Change time applies only between trips at a stop, not after walking; no two footpaths in a row; trip-count limit with fewest-trips tie-break; hours >= 24. A hand-made network pins each rule, then 300 city queries on two networks. 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. direct_and_parsing 2. change_times 3. footpath_rules 4. max_trips_and_ties 5. after_midnight 6. unreachable_and_errors 7. city_queries 8. fresh_city_queries 9. speed (400 queries < 20 s)

Setup notes: Use Case Scout batch 8 case i10-transit-earliest-arrival (offline fixture). Prepared with batch8/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. 1-min load average at scoring: 2.30. Claude Code's own reported cost for claude-opus-5.5: $0.249743. Score: 9/9. 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: $0.25
Wall time: 1m
Human step-ins: 0
Turns: 5

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	111,094	6,300	$0.25

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. direct_and_parsing 2. change_times 3. footpath_rules 4. max_trips_and_ties 5. after_midnight 6. unreachable_and_errors 7. city_queries 8. fresh_city_queries 9. speed (400 queries < 20 s)
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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 rewrote `transit.py`. On the network in this directory it gives the same answers as a separate slower solver on 300 random queries, and building the network plus answering 400 queries with `max_trips` = 4 took about 0.06 s, against the 20 s limit. The hidden check groups haven't been run, and I didn't try it on a freshly generated network.\n\n**How it works:** the search goes in rounds, and round k allows one more trip than round k\u22121 (k is at most `max_trips`).\n- **Round 0:** you start at `src` at `start` and may walk one footpath.\n- **Each later round:** it checks the trips you could catch at stops whose ready time got better in the last round, and records the earliest time you could get off at each later stop. It also notes any arrival at `dst`, either by getting off there or by one footpath from where you got off.\n- **Next ready times:** getting off at a stop makes you ready there after that stop's change time. Walking a footpath makes you ready at the far end straight away. Walks only start from `src` or from a stop where you got off, so you can never walk twice in a row.\n- **Answer:** the earliest arrival at `dst` wins. If two journeys arrive at the same time, the one found in the earlier round (fewer trips) is kept.\n\n**Other rules covered:**\n- Timetable rows are sorted by `seq`, and times can run past 24:00.\n- Where a footpath is listed more than once, the shortest time is used, and footpaths from a stop to itself are dropped.\n- An unknown stop name raises `KeyError`, and if `src == dst` the answer is `(start, 0)`.\n- Results come back as `HH:MM:SS` with the hours padded to two digits, and `None` if `dst` can't be reached.\n\n**Spot checks:**\n- A 0-trip footpath query gave `('08:03:00', 0)`.\n- `src == dst` at 25:00:00 gave `('25:00:00', 0)`.\n- A direct trip gave `('06:26:00', 1)`.\n- An unknown stop raised `KeyError`.\n\nThe comparison solver used the same \"earlier ready time is always better\" shortcut as the main code, so it doesn't independently test that part of the logic.",
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