Claude Opus 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 Opus 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: $0.18
Wall time: 34s
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-opus-5.5: $0.182201. 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: $0.18
Wall time: 34s
Human step-ins: 0
Turns: 6

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	105,854	3,643	$0.18

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. 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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  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
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  "subagent_stats": {
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  "subtype": "success",
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  "result": "I wrote `hourly.py` (stdlib only) and ran it on `readings.csv`. It produced `hourly.csv` with 183 hourly rows across 4 meters. I haven't checked it against a known-correct result, so the evidence is spot checks of this file only.\n\n**How it handles the data:**\n- **Timestamps:** It accepts `T` or a space between date and time, and `Z`, `\u00b1HH:MM` offsets or no offset (treated as UTC). The parser also takes fractional seconds and offsets without a colon, which don't appear in this file but might in the fresh one.\n- **Readings:** A comma as the decimal separator is accepted, including in quoted fields like `\"26377,588\"`.\n- **Duplicates:** If a meter has several rows for the same UTC instant, the last one in file order wins.\n- **Intervals between readings:**\n  - A drop of more than 90000 is treated as a rollover, so consumption is later + 100000 \u2212 earlier.\n  - Any smaller drop is treated as a reset, and that interval is unknown.\n  - A gap of more than 6 hours (exactly 6 is fine) is also unknown.\n- **Hourly slots:** Each slot's value is consumption spread linearly over time between readings. If any unknown interval overlaps the slot for a positive length of time, `kwh` is left empty.\n\n**Spot checks on this data:**\n- **Empty slots:** 9 slots came out empty. Eight are MTR-0506B from 06:00 to 13:00 on Feb 1, because of a gap longer than 6 hours. The ninth is MTR-0934D at 14:00, because of a reset between 14:12:30 (15688.575) and 14:34:30 (0.485). The slots either side of that reset have normal values.\n- **Rollover:** MTR-0779A rolls over from about 99994 to about 10 and has no empty slots, as expected.\n- **Values:** None are negative, and none are larger than 20 kWh in an hour.\n\nNothing is hard-coded, so it should run unchanged on the fresh file.\n\nFiles are in ./usecase-runner/runs/g09-claude-opus/work:\n- hourly.py\n- hourly.csv",
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