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Get Started Free →Observability Designer (POWERFUL)
.claude/skills/leoyeai-observability-designer/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 13 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 104% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 103% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 70% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 115% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 69% | 0% |
Category: Engineering Tier: POWERFUL Description: Design comprehensive observability strategies for production systems including SLI/SLO frameworks, alerting optimization, and dashboard generation.
Observability Designer enables you to create production-ready observability strategies that provide deep insights into system behavior, performance, and reliability. This skill combines the three pillars of observability (metrics, logs, traces) with proven frameworks like SLI/SLO design, golden signals monitoring, and alert optimization to create comprehensive observability solutions.
This skill includes three powerful Python scripts for comprehensive observability design:
slo_designer.py)Generates complete SLI/SLO frameworks based on service characteristics:
alert_optimizer.py)Analyzes and optimizes existing alert configurations:
dashboard_generator.py)Creates comprehensive dashboard specifications:
This comprehensive observability design skill enables organizations to build robust, scalable monitoring and alerting systems that provide actionable insights while maintaining cost efficiency and operational excellence.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 13,236 | 11,586 | -12% | 1 | 1 | 0% | 2,092 | 4,255 | +103% | 0 | 0 | — |
case-02 | pass→pass | 12,159 | 8,336 | -31% | 1 | 1 | 0% | 2,236 | 3,801 | +70% | 0 | 0 | — |
case-03 | pass→pass | 13,992 | 14,391 | +3% | 1 | 1 | 0% | 2,285 | 4,906 | +115% | 0 | 0 | — |
case-04 | pass→pass | 14,389 | 8,916 | -38% | 1 | 1 | 0% | 2,296 | 3,890 | +69% | 0 | 0 | — |
case-05 | fail→pass | 11,069 | 8,915 | -19% | 1 | 1 | 0% | 1,912 | 3,904 | +104% | 0 | 0 | — |
case-06 | pass→pass | 11,419 | 10,240 | -10% | 1 | 1 | 0% | 1,872 | 3,949 | +111% | 0 | 0 | — |
case-07 | pass→pass | 14,578 | 14,096 | -3% | 1 | 1 | 0% | 2,094 | 4,719 | +125% | 0 | 0 | — |
case-08 | pass→pass | 9,693 | 9,757 | +1% | 1 | 1 | 0% | 1,644 | 4,067 | +147% | 0 | 0 | — |
case-09 | pass→pass | 9,735 | 8,610 | -12% | 1 | 1 | 0% | 1,650 | 3,985 | +142% | 0 | 0 | — |
case-10 | pass→pass | 13,810 | 15,909 | +15% | 1 | 1 | 0% | 2,594 | 5,483 | +111% | 0 | 0 | — |
case-11 | pass→pass | 17,779 | 19,845 | +12% | 1 | 1 | 0% | 2,762 | 5,847 | +112% | 0 | 0 | — |
case-12 | pass→pass | 18,360 | 16,667 | -9% | 1 | 1 | 0% | 3,253 | 5,738 | +76% | 0 | 0 | — |
case-13 | pass→pass | 15,602 | 19,066 | +22% | 1 | 1 | 0% | 2,642 | 6,138 | +132% | 0 | 0 | — |
case-14 | pass→pass | 7,656 | 9,968 | +30% | 1 | 1 | 0% | 1,343 | 4,401 | +228% | 0 | 0 | — |
case-15 | pass→pass | 8,995 | 9,885 | +10% | 1 | 1 | 0% | 1,674 | 4,171 | +149% | 0 | 0 | — |
case-16 | pass→pass | 15,399 | 19,140 | +24% | 1 | 1 | 0% | 2,620 | 5,561 | +112% | 0 | 0 | — |
case-17 | pass→pass | 18,148 | 22,248 | +23% | 1 | 1 | 0% | 2,775 | 6,228 | +124% | 0 | 0 | — |
case-18 | pass→pass | 6,490 | 13,919 | +114% | 1 | 1 | 0% | 1,144 | 4,753 | +315% | 0 | 0 | — |
case-19 | pass→pass | 10,168 | 10,783 | +6% | 1 | 1 | 0% | 1,780 | 4,014 | +126% | 0 | 0 | — |
case-20 | pass→pass | 16,018 | 20,836 | +30% | 1 | 1 | 0% | 3,121 | 6,258 | +101% | 0 | 0 | — |
case-21 | pass→pass | 20,182 | 13,690 | -32% | 1 | 1 | 0% | 3,350 | 4,708 | +41% | 0 | 0 | — |
case-22 | pass→pass | 15,468 | 15,935 | +3% | 1 | 1 | 0% | 3,368 | 6,133 | +82% | 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.