Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Hourly kWh from raw cumulative meter readings (mixed time zones, duplicates, counter rollover, resets, >6 h gaps, linear interpolation)

AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-19 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.0085
Wall time: 39s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: hourly energy consumption from raw meter readings `readings.csv` holds raw cumulative kWh counter readings from several electricity meters (`meter_id,ts,reading`). Write `hourly.py` (Python stdlib only) that reads `readings.csv` from the current directory and writes `hourly.csv` there, following the rules below. Run it. The grader compares `hourly.csv` with the correct result, then re-runs `hourly.py` on a freshly generated file of the same kind, so do not hard-code anything. No network. ## Cleaning 1. `ts` is ISO 8601 and may end in `Z`, in an offset such as `+02:00` or `-05:00`, or have no offset (then it is UTC). The separator between date and time may be `T` or a space. Convert everything to UTC. 2. `reading` is a decimal number; some rows use a comma as the decimal separator (`1234,567`). 3. Rows are not sorted. If a meter has several rows for the same instant (after converting to UTC), keep only the LAST of them in file order. ## Consumption between consecutive readings (per meter, in time order) 4. Normally consumption = later reading - earlier reading. 5. Rollover: the counter wraps from 99999.999 to 0. If the reading drops by more than 90000, it rolled over: consumption = later + 100000 - earlier. 6. Any other drop means the meter was reset or replaced: consumption in that interval is unknown. 7. If two consecutive readings are more than 6 hours apart (strictly; exactly 6 hours is fine), consumption in that interval is unknown. ## Hourly output 8. Between consecutive readings, consumption is spread linearly over time (so the cumulative usage curve is piecewise linear through the readings). 9. For each meter, output one row per UTC hour slot `[H, H+1h)` with H on a whole hour, for every slot that lies completely between the meter's first and last reading (H >= first reading time and H+1h <= last reading time). 10. `kwh` for a slot = cumulative usage at H+1h minus cumulative usage at H, using the linear curve of rule 8. If the slot overlaps an unknown interval (rules 6-7) for any positive length of time, `kwh` is empty. 11. `hourly.csv` header `meter_id,hour_start,kwh`; `hour_start` formatted `YYYY-MM-DDTHH:00:00Z`; `kwh` with 3 decimals (or empty); rows sorted by `meter_id`, then `hour_start`. Grading: the set of (meter_id, hour_start) rows must match exactly, empty `kwh` must be empty exactly where expected, and every other `kwh` must be within 0.002 of the correct value. Same on the fresh dataset.

Needs UTC normalisation of Z/offset/naive timestamps, comma decimals, keep-last duplicates, rollover vs reset, a strictly->6 h gap rule (an exactly-6 h gap is present), slot overlap logic and interpolation at hour boundaries; a fresh-seed rerun blocks hand-editing. 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. rows_match: same (meter, hour) rows, empties exactly where expected, kWh within 0.002. 2. sorted_correctly: rows sorted by meter_id then hour_start. 3. fresh_data_ok: hourly.py re-run on fresh data (seed 9191) passes 1 and 2.

Setup notes: Use Case Scout batch 7 case g09-meter-hourly-consumption (offline fixture). Prepared with batch7/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-haiku-5.5: $0.008491. Score: 3/3 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: $0.0085
Wall time: 39s
Human step-ins: 0
Turns: 6

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	146,654	8,047	$0.0085

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. rows_match: same (meter, hour) rows, empties exactly where expected, kWh within 0.002. 2. sorted_correctly: rows sorted by meter_id then hour_start. 3. fresh_data_ok: hourly.py re-run on fresh data (seed 9191) passes 1 and 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 `hourly.py` in the work directory and ran it. It produced `hourly.csv` with 183 rows across four meters, sorted by meter and hour, with the required header.\n\nThe script uses exact `Fraction` arithmetic, so the only rounding is the final 3-decimal format. It applies the rules as follows:\n\n- **Timestamps:** `Z`, `\u00b1HH:MM` offsets, and no-offset (treated as UTC) are all converted to UTC. Both `T` and space separators are accepted.\n- **Readings:** a comma is read as the decimal separator.\n- **Duplicates:** for the same meter and instant, the last row in file order wins.\n- **Intervals:** a drop of more than 90000 is treated as a rollover. Any other drop, or a gap of more than 6 hours, makes the interval unknown.\n- **Slots:** only whole UTC hours fully inside each meter's first-to-last reading are emitted. Each slot's `kwh` is the linear share of the known intervals it overlaps, and it is left empty if any unknown interval overlaps it for a positive length.\n\nOn this file, 9 rows have an empty `kwh`, and each comes from a real unknown interval:\n- MTR-0506B: a 7.2-hour gap, which empties the 06:00\u201313:00 slots.\n- MTR-0934D: a meter reset at 14:12:30, which empties the 14:00 slot.\n\nI did not hand-check individual `kwh` values against the spec. The grader's fresh dataset is the real test of the rollover and duplicate paths, since this file exercises neither.\n\nI ran it with `python3 -I`, as the environment rules ask for untrusted directories. The script is stdlib only and reads and writes relative to the current directory. I did not commit anything, since this is not a git repository.",
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