GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Multi-window burn-rate alerts from per-minute request counts (integer thresholds, flap smoothing, page-over-ticket priority)
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.03
- Wall time
- 44s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Multi-window burn-rate alerts from per-minute request counts `minutes.csv` has one day of per-minute request counts for a service with a **99.9% availability SLO** (error budget 0.001). Write `slo.py` (Python 3 stdlib only) that, run as `python slo.py` in this directory, reads `minutes.csv` and writes `alerts.csv` and `summary.json` following these rules. We re-run it on a freshly generated file, so do not hard-code anything. ## Input cleaning 1. The first line is a header. Each other row must have exactly 3 fields `minute,total,errors`; each field is stripped of spaces and must parse as an integer, otherwise the row is skipped. Rows with a negative value or `errors > total` are skipped. 2. If several valid rows have the same minute, the **last** valid one in the file wins. 3. Minutes run from 0 to M, where M is the largest valid minute. Minutes with no valid row have total = errors = 0. ## Burn rates 4. For minute i and window w (minutes), the window is minutes `max(0, i-w+1) .. i` (shorter at the start of the day). With T and E the sums of total and errors in it, the window is **hot at threshold X** when T > 0 and E / T >= X × 0.001. Compare exactly with integers: `E * 10000 >= (X*10) * T` (X*10 is 144 or 60). 5. A minute is a **page** minute if the 60-minute and the 5-minute windows are both hot at 14.4. It is a **ticket** candidate if the 360-minute and the 30-minute windows are both hot at 6. 6. **Flap smoothing for pages:** for every two page minutes a < b with no page minute between them and b - a equal to 2 or 3 (one or two quiet minutes between), the minutes between become page minutes too. 7. **Pages win:** ticket minutes = ticket candidates that are not page minutes (after rule 6). Then apply the same smoothing as rule 6 to ticket minutes, except a gap is only filled if none of the gap minutes is a page minute. 8. An **alert** is a maximal run of consecutive minutes of the same severity. ## Output 9. `alerts.csv`: header `severity,start,end`, one row per alert, `severity` is `page` or `ticket`, `start`/`end` are the first/last minute (inclusive). Sorted by start (page before ticket on equal start, which cannot normally happen). 10. `summary.json`: an object with `budget_remaining` = `1 - (all errors / all totals) / 0.001` over minutes 0..M rounded to 4 decimals with Python `round` (1.0 if all totals are 0; it can be negative), `page_minutes` and `ticket_minutes` (counts after rules 6-7) and `minutes` = M + 1. ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `page_alerts_exact` and `ticket_alerts_exact` (rows of that severity in `alerts.csv` match exactly), `summary_exact` (all 4 values; `budget_remaining` within 0.00005), then `slo.py` is re-run on a fresh file and `fresh_page_alerts`, `fresh_ticket_alerts`, `fresh_summary` are checked the same way. Each run has a 60-second limit.
Exact alert intervals depend on input cleaning (last valid duplicate wins), short windows at the start, exact integer comparisons, page smoothing, then ticket smoothing that may not bridge page minutes; 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. page_alerts_exact 2. ticket_alerts_exact 3. summary_exact 4. fresh_page_alerts 5. fresh_ticket_alerts 6. fresh_summary
Setup notes: Use Case Scout batch8 case i05-slo-burn-rate-alerts (offline fixture). Prepared with batch8/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: 6/6. 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
- $0.03
- Wall time
- 44s
- 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 | 67,269 | 1,478 | $0.03 |
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. page_alerts_exact 2. ticket_alerts_exact 3. summary_exact 4. fresh_page_alerts 5. fresh_ticket_alerts 6. fresh_summary
- 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": "01a16455-790e-7760-b12f-fa3d2dab3135"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll check the directory, implement the rules in `slo.py`, and verify the window calculations and smoothing.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'minutes.csv' -g 'slo.py'\"", "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 'minutes.csv' -g 'slo.py'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/196_i05/work\nminutes.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'head -n 5 minutes.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 5 minutes.csv'", "aggregated_output": "minute,total,errors\n0,286,0\n1,212,0\n2,184,0\n3,202,0\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/196_i05/work/slo.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/196_i05/work/slo.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python slo.py && cat summary.json && head alerts.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python slo.py && cat summary.json && head alerts.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 slo.py && cat summary.json && head alerts.csv'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc 'python3 slo.py && cat summary.json && head alerts.csv'", "aggregated_output": "{\n \"budget_remaining\": -9.1657,\n \"page_minutes\": 66,\n \"ticket_minutes\": 363,\n \"minutes\": 1440\n}\nseverity,start,end\r\npage,322,357\r\nticket,358,382\r\nticket,395,396\r\npage,397,402\r\nticket,403,410\r\npage,411,411\r\nticket,412,685\r\npage,908,918\r\nticket,919,942\r\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport random\nimport slo\nfor n in range(50):\n for _ in range(100):\n original = [random.choice([False, True]) for _ in range(n)]\n blocked = [random.choice([False, True]) for _ in range(n)]\n original = [a and not b for a,b in zip(original,blocked)]\n for barrier in (None, blocked):\n expected = original[:]\n positions = [i for i,a in enumerate(original) if a]\n for a,b in zip(positions, positions[1:]):\n if b-a in (2,3) and (barrier is None or not any(barrier[a+1:b])):\n expected[a+1:b] = [True]*(b-a-1)\n actual = original[:]\n slo.smooth(actual, barrier)\n assert actual == expected\nprint('Randomized smoothing checks passed')\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 random\nimport slo\nfor n in range(50):\n for _ in range(100):\n original = [random.choice([False, True]) for _ in range(n)]\n blocked = [random.choice([False, True]) for _ in range(n)]\n original = [a and not b for a,b in zip(original,blocked)]\n for barrier in (None, blocked):\n expected = original[:]\n positions = [i for i,a in enumerate(original) if a]\n for a,b in zip(positions, positions[1:]):\n if b-a in (2,3) and (barrier is None or not any(barrier[a+1:b])):\n expected[a+1:b] = [True]*(b-a-1)\n actual = original[:]\n slo.smooth(actual, barrier)\n assert actual == expected\nprint('Randomized smoothing checks passed')\nPY\"", "aggregated_output": "Randomized smoothing checks passed\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "agent_message", "text": "Created [slo.py](/home/bob/usecase-runs/batch/out/gpt-6.1-sol/196_i05/work/slo.py) and generated `alerts.csv` and `summary.json`.\n\nVerified execution with `python3` and randomized smoothing checks. This environment has no `python` command; use `python3 slo.py`."}}
{"type": "turn.completed", "usage": {"input_tokens": 67269, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 1478, "reasoning_output_tokens": 10}}