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Get Started Free →Create or audit SLOs, SLIs, alert rules, incident response steps, escalation paths, postmortems, operational runbooks, and customer-impact communication. Use when defining production reliability, preparing launch readiness, responding to an outage, writing a runbook, tuning alerts, or closing the loop after an incident.
.claude/skills/majiayu000-incident-slo-runbook/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 4% | 0% |
Use this skill to connect observability to action. Metrics and logs are not enough; each critical user journey needs an SLO, alert, owner, response path, and post-incident learning loop.
Define:
Avoid vanity metrics. Prefer user-visible success and latency over internal counters unless internal counters are the only reliable proxy.
Each runbook should include:
Commands must be safe to run or explicitly labeled destructive.
textservice_or_journey: slo: alerts: dashboard_or_queries: runbook: escalation: postmortem_template: verification:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 33,310 | 23,121 | -31% | 1 | 1 | 0% | 6,219 | 4,080 | -34% | 0 | 0 | — |
case-02 | fail→fail | 34,655 | 24,287 | -30% | 1 | 1 | 0% | 6,228 | 4,677 | -25% | 0 | 0 | — |
case-03 | fail→fail | 35,915 | 23,626 | -34% | 1 | 1 | 0% | 6,203 | 4,547 | -27% | 0 | 0 | — |
case-04 | fail→pass | 23,777 | 22,166 | -7% | 1 | 1 | 0% | 3,991 | 4,296 | +8% | 0 | 0 | — |
case-05 | fail→fail | 27,422 | 18,401 | -33% | 1 | 1 | 0% | 4,563 | 3,345 | -27% | 0 | 0 | — |
case-06 | fail→fail | 28,579 | 28,454 | -0% | 1 | 1 | 0% | 5,387 | 5,380 | -0% | 0 | 0 | — |
case-07 | fail→pass | 33,801 | 22,719 | -33% | 1 | 1 | 0% | 5,844 | 4,230 | -28% | 0 | 0 | — |
case-08 | fail→pass | 27,364 | 19,930 | -27% | 1 | 1 | 0% | 4,436 | 3,473 | -22% | 0 | 0 | — |
case-09 | fail→pass | 24,417 | 23,263 | -5% | 1 | 1 | 0% | 4,071 | 4,222 | +4% | 0 | 0 | — |
case-10 | fail→pass | 36,569 | 28,368 | -22% | 1 | 1 | 0% | 6,147 | 5,389 | -12% | 0 | 0 | — |
case-11 | fail→pass | 24,780 | 20,653 | -17% | 1 | 1 | 0% | 4,239 | 3,721 | -12% | 0 | 0 | — |
case-12 | fail→pass | 24,385 | 18,986 | -22% | 1 | 1 | 0% | 3,860 | 3,440 | -11% | 0 | 0 | — |
case-13 | fail→pass | 23,572 | 18,859 | -20% | 1 | 1 | 0% | 3,471 | 3,279 | -6% | 0 | 0 | — |
case-14 | fail→pass | 24,161 | 25,729 | +6% | 1 | 1 | 0% | 3,811 | 4,582 | +20% | 0 | 0 | — |
case-15 | fail→pass | 26,223 | 25,134 | -4% | 1 | 1 | 0% | 4,280 | 4,466 | +4% | 0 | 0 | — |
case-16 | fail→pass | 25,888 | 22,207 | -14% | 1 | 1 | 0% | 4,109 | 3,898 | -5% | 0 | 0 | — |
case-17 | fail→pass | 28,533 | 22,808 | -20% | 1 | 1 | 0% | 4,656 | 4,225 | -9% | 0 | 0 | — |
case-18 | fail→pass | 22,073 | 18,966 | -14% | 1 | 1 | 0% | 3,548 | 3,461 | -2% | 0 | 0 | — |
case-19 | fail→pass | 22,893 | 19,637 | -14% | 1 | 1 | 0% | 3,917 | 3,675 | -6% | 0 | 0 | — |
case-20 | pass→fail | 17,247 | 21,519 | +25% | 1 | 1 | 0% | 3,260 | 4,392 | +35% | 0 | 0 | — |
case-21 | pass→pass | 19,557 | 16,019 | -18% | 1 | 1 | 0% | 4,031 | 3,890 | -3% | 0 | 0 | — |
case-22 | pass→fail | 16,360 | 17,105 | +5% | 1 | 1 | 0% | 2,893 | 3,244 | +12% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +59 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.