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Get Started Free →Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.
.claude/skills/dokhacgiakhoa-observability-engineer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 12% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 30% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 16% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 54% | 0% |
You are an observability engineer specializing in production-grade monitoring, logging, tracing, and reliability systems for enterprise-scale applications.
Expert observability engineer specializing in comprehensive monitoring strategies, distributed tracing, and production reliability systems. Masters both traditional monitoring approaches and cutting-edge observability patterns, with deep knowledge of modern observability stacks, SRE practices, and enterprise-scale monitoring architectures.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 12,920 | 10,075 | -22% | 1 | 1 | 0% | 1,366 | 1,529 | +12% | 0 | 0 | — |
case-02 | pass→pass | 19,482 | 27,872 | +43% | 1 | 1 | 0% | 3,207 | 4,167 | +30% | 0 | 0 | — |
case-03 | pass→pass | 18,417 | 17,311 | -6% | 1 | 1 | 0% | 2,754 | 3,188 | +16% | 0 | 0 | — |
case-04 | pass→pass | 22,755 | 22,014 | -3% | 1 | 1 | 0% | 3,147 | 4,844 | +54% | 0 | 0 | — |
case-05 | pass→pass | 10,094 | 11,308 | +12% | 1 | 1 | 0% | 1,944 | 2,825 | +45% | 0 | 0 | — |
case-06 | pass→pass | 19,221 | 17,094 | -11% | 1 | 1 | 0% | 2,489 | 3,597 | +45% | 0 | 0 | — |
case-07 | pass→pass | 17,312 | 16,923 | -2% | 1 | 1 | 0% | 1,997 | 2,522 | +26% | 0 | 0 | — |
case-08 | pass→pass | 16,794 | 26,930 | +60% | 1 | 1 | 0% | 3,016 | 4,871 | +62% | 0 | 0 | — |
case-09 | pass→pass | 21,458 | 31,308 | +46% | 1 | 1 | 0% | 2,804 | 5,244 | +87% | 0 | 0 | — |
case-10 | pass→pass | 22,270 | 24,459 | +10% | 1 | 1 | 0% | 2,892 | 3,979 | +38% | 0 | 0 | — |
case-11 | pass→pass | 18,900 | 20,935 | +11% | 1 | 1 | 0% | 2,334 | 4,028 | +73% | 0 | 0 | — |
case-12 | pass→pass | 13,393 | 14,603 | +9% | 1 | 1 | 0% | 2,285 | 3,120 | +37% | 0 | 0 | — |
case-13 | pass→pass | 18,899 | 20,129 | +7% | 1 | 1 | 0% | 2,462 | 3,231 | +31% | 0 | 0 | — |
case-14 | fail→pass | 21,682 | 22,787 | +5% | 1 | 1 | 0% | 2,711 | 3,583 | +32% | 0 | 0 | — |
case-15 | pass→pass | 17,230 | 13,369 | -22% | 1 | 1 | 0% | 1,997 | 2,821 | +41% | 0 | 0 | — |
case-16 | pass→pass | 17,006 | 17,470 | +3% | 1 | 1 | 0% | 2,035 | 3,515 | +73% | 0 | 0 | — |
case-17 | pass→pass | 18,148 | 14,935 | -18% | 1 | 1 | 0% | 2,488 | 3,439 | +38% | 0 | 0 | — |
case-18 | pass→pass | 10,146 | 15,922 | +57% | 1 | 1 | 0% | 1,743 | 2,558 | +47% | 0 | 0 | — |
case-19 | pass→pass | 12,626 | 15,446 | +22% | 1 | 1 | 0% | 2,177 | 3,268 | +50% | 0 | 0 | — |
case-20 | pass→pass | 22,733 | 27,194 | +20% | 1 | 1 | 0% | 2,909 | 4,583 | +58% | 0 | 0 | — |
case-21 | pass→pass | 18,721 | 23,395 | +25% | 1 | 1 | 0% | 2,454 | 3,803 | +55% | 0 | 0 | — |
case-22 | pass→pass | 13,318 | 23,470 | +76% | 1 | 1 | 0% | 2,265 | 3,603 | +59% | 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 +5 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.