Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Multi-window burn-rate alerts from per-minute request counts (integer thresholds, flap smoothing, page-over-ticket priority)

AClaude Sonnet 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: $0.06
Wall time: 16s
Human step-ins: 0
Outcome: Pass

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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 (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. 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 batch 8 case i05-slo-burn-rate-alerts (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. Claude Code's own reported cost for claude-sonnet-5.5: $0.060742. Score: 6/6. 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: $0.06
Wall time: 16s
Human step-ins: 0
Turns: 4

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on	79,526	2,035	$0.06

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-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. 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

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   "server_tool_use": {
    "web_search_requests": 0,
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     "cache_creation": {
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     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
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    "outputTokens": 2035,
    "cacheReadInputTokens": 71204,
    "cacheCreationInputTokens": 8314,
    "webSearchRequests": 0,
    "costUSD": 0.0607424,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 292,
    "canonicalModel": "claude-sonnet-5-5",
    "provider": "firstParty",
    "costBasis": "list"
   }
  },
  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
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   "requested": {
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  "num_turns": 4,
  "subtype": "success",
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  "result": "I wrote `slo.py` in `./usecase-runner/runs/i05-claude-sonnet/work`. It runs and writes `alerts.csv` and `summary.json`. I haven't checked the output against the hidden checks, so I can't say whether they pass.\n\nOn the current `minutes.csv` it produced 12 alerts: 4 page and 8 ticket. The summary is `budget_remaining` -9.1657, `page_minutes` 66, `ticket_minutes` 363 and `minutes` 1440.\n\n- **Python command:** the environment has no `python` command, only `python3`, so I ran it with `python3 slo.py`. The grader's `python slo.py` will need a `python` on its path.\n- **What counts as an integer:** I accept an optional `+` or `-` sign followed by ASCII digits. A field like `1_0` or a non-ASCII digit string makes the row get skipped. The spec only says \"parse as an integer\", so this was my reading.\n- **Empty file:** if no rows are valid, it writes an empty `alerts.csv` (header only) and a summary with `minutes` 0 and `budget_remaining` 1.0.\n- **Nothing hard-coded:** the script reads whatever `minutes.csv` is in the directory, so a freshly generated file is handled the same way.",
  "ttft_ms": 1748,
  "type": "result",
  "duration_ms": 15792,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1133,
  "time_to_request_ms": 338,
  "first_content_frame_ms": 1133,
  "queued_turn_count": 0,
  "result_index": 0
 }
]