Claude Haiku 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 Haiku 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: $0.02
Wall time: 2m
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: 2.31. Claude Code's own reported cost for claude-haiku-5.5: $0.019184. 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: $0.02
Wall time: 2m
Human step-ins: 0
Turns: 11

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	365,263	19,920	$0.02

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Haiku 5.5 (anthropic/claude-haiku-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` to follow the spec, and it agrees with an independent brute-force reference on every query I checked.\n\n**How it works:** round-based search over at most `max_trips` trips. Each round tracks two kinds of arrival per stop:\n- **Trip labels** (alighted here): these can be walked from, and need `min_change` before boarding again.\n- **Foot labels** (walked here): these can only board, never walk again.\n\nBoarding uses the previous round's ready times, so trip counts stay correct. The answer is the earliest arrival at `dst` over all rounds, with the smallest round that reaches it. `src == dst` returns `(start, 0)`, and unknown `src`/`dst` raise `KeyError`.\n\n**Checks I ran** (scripts in `/tmp/transit_check/`, not in the work directory):\n- **Random small networks:** 6,000 queries against a brute-force DFS written directly from the spec, with 300 random networks and max_trips 0\u20133. Zero mismatches. The first version of this reference had a bug (it didn't let you board after a footpath); I fixed it, and the planner was right in the case that exposed it.\n- **Real network:** 36 queries against the reference, zero mismatches.\n- **Speed:** building the network plus 400 random queries with max_trips up to 4 takes about 0.05 s, against the 20 s limit.\n- **Data:** every stop in the timetable and footpaths is in `stops.csv`, so no hidden `KeyError` inside the planner.\n\n**Choices I made:**\n- Walking from `src` is treated as a zero-trip state, so it's allowed at any round, and a single footpath from `src` is valid for `max_trips = 0`.\n- Boarding a trip uses the earliest stop where you're ready, since riding from there dominates riding from any later stop.\n\nI haven't run the hidden check groups. The examples in the spec are the only fixed expectations I could test against directly.",
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