GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Hourly kWh from raw cumulative meter readings (mixed time zones, duplicates, counter rollover, resets, >6 h gaps, linear interpolation)
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.04
- Wall time
- 46s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
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. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
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 batch7 case g09-meter-hourly-consumption (offline fixture). Prepared with batch7/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 3/3 checks. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 5 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.04
- Wall time
- 46s
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 65,333 | 1,602 | $0.04 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), 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
{"type": "thread.started", "thread_id": "01a1644b-1586-7510-a878-67e56083f888"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the CSV, implement the hourly calculation with decimal arithmetic, and run it to produce `hourly.csv`.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'readings.csv' -g 'hourly.py' -g 'hourly.csv'\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'readings.csv' -g 'hourly.py' -g 'hourly.csv'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/188_g09/work\nreadings.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'head -n 8 readings.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'head -n 8 readings.csv'", "aggregated_output": "meter_id,ts,reading\r\nMTR-0934D,2026-02-01T10:49:30Z,15684.933\r\nMTR-0779A,2026-02-01T09:32:00+05:30,99979.042\r\nMTR-0506B,2026-02-02 09:37:00-05:00,30748.725\r\nMTR-0764C,2026-02-02 15:34:30,26400.164\r\nMTR-0934D,2026-02-01T14:12:30Z,15688.575\r\nMTR-0506B,2026-02-02T12:23:00Z,30740.844\r\nMTR-0764C,2026-02-01 01:46:30,26345.363\r\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/188_g09/work/hourly.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/188_g09/work/hourly.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python hourly.py && head -n 6 hourly.csv && wc -l hourly.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python hourly.py && head -n 6 hourly.csv && wc -l hourly.csv'", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc 'python3 hourly.py && head -n 6 hourly.csv && wc -l hourly.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc 'python3 hourly.py && head -n 6 hourly.csv && wc -l hourly.csv'", "aggregated_output": "meter_id,hour_start,kwh\r\nMTR-0506B,2026-02-01T01:00:00Z,1.645\r\nMTR-0506B,2026-02-01T02:00:00Z,1.838\r\nMTR-0506B,2026-02-01T03:00:00Z,1.025\r\nMTR-0506B,2026-02-01T04:00:00Z,1.574\r\nMTR-0506B,2026-02-01T05:00:00Z,0.630\r\n184 hourly.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv\nimport os\nimport tempfile\nfrom pathlib import Path\nimport hourly\nwith tempfile.TemporaryDirectory() as directory:\n original = os.getcwd()\n os.chdir(directory)\n try:\n with open('readings.csv', 'w', newline='') as f:\n w = csv.writer(f)\n w.writerow(['meter_id','ts','reading'])\n w.writerows([\n ['A','2026-01-01T00:00:00Z','99999'],\n ['A','2026-01-01T03:00:00Z','0'],\n ['A','2026-01-01T02:00:00Z','1'],\n ['A','2026-01-01T01:00:00+01:00','99998'],\n ['A','2026-01-01T02:00:00Z','2,000'],\n ['A','2026-01-01T10:00:00Z','7'],\n ])\n hourly.main()\n rows = list(csv.DictReader(open('hourly.csv')))\n assert len(rows) == 10\n assert [r['kwh'] for r in rows] == ['2.000','2.000'] + [''] * 8\n print('Synthetic check passed: UTC duplicates, decimal comma, rollover, reset, and long gap.')\n finally:\n os.chdir(original)\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv\nimport os\nimport tempfile\nfrom pathlib import Path\nimport hourly\nwith tempfile.TemporaryDirectory() as directory:\n original = os.getcwd()\n os.chdir(directory)\n try:\n with open('readings.csv', 'w', newline='') as f:\n w = csv.writer(f)\n w.writerow(['meter_id','ts','reading'])\n w.writerows([\n ['A','2026-01-01T00:00:00Z','99999'],\n ['A','2026-01-01T03:00:00Z','0'],\n ['A','2026-01-01T02:00:00Z','1'],\n ['A','2026-01-01T01:00:00+01:00','99998'],\n ['A','2026-01-01T02:00:00Z','2,000'],\n ['A','2026-01-01T10:00:00Z','7'],\n ])\n hourly.main()\n rows = list(csv.DictReader(open('hourly.csv')))\n assert len(rows) == 10\n assert [r['kwh'] for r in rows] == ['2.000','2.000'] + [''] * 8\n print('Synthetic check passed: UTC duplicates, decimal comma, rollover, reset, and long gap.')\n finally:\n os.chdir(original)\nPY\"", "aggregated_output": "Synthetic check passed: UTC duplicates, decimal comma, rollover, reset, and long gap.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "agent_message", "text": "Created [hourly.py](hourly.py) and ran it with Python 3, producing [hourly.csv](hourly.csv) with 183 data rows.\n\nSynthetic checks passed for UTC duplicate handling, decimal commas, rollover, resets, and gaps over six hours."}}
{"type": "turn.completed", "usage": {"input_tokens": 65333, "cached_input_tokens": 56448, "cache_write_input_tokens": 0, "output_tokens": 1602, "reasoning_output_tokens": 34}}