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Get Started Free →Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.
.claude/skills/alirezarezvani-performance-profiler/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | -34% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -52% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -46% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 0% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | 20% | 0% |
Tier: POWERFUL Category: Engineering Domain: Performance Engineering
Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after.
bash# Analyze a project for performance risk indicators python3 scripts/performance_profiler.py /path/to/project # JSON output for CI integration python3 scripts/performance_profiler.py /path/to/project --json # Custom large-file threshold python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256
bash# Establish baseline BEFORE any optimization # Record: P50, P95, P99 latency | RPS | error rate | memory usage # Wrong: "I think the N+1 query is slow, let me fix it" # Right: Profile → confirm bottleneck → fix → measure again → verify improvement
→ See references/profiling-recipes.md for details
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 5,702 | 5,149 | -10% | 1 | 1 | 0% | 463 | 906 | +96% | 0 | 0 | — |
case-01 | fail→fail | 5,332 | 4,880 | -8% | 1 | 1 | 0% | 289 | 857 | +197% | 0 | 0 | — |
case-03 | fail→fail | 17,245 | 4,874 | -72% | 1 | 1 | 0% | 2,310 | 858 | -63% | 0 | 0 | — |
case-04 | pass→pass | 11,490 | 8,187 | -29% | 1 | 1 | 0% | 2,030 | 2,032 | +0% | 0 | 0 | — |
case-05 | pass→pass | 8,464 | 6,982 | -18% | 1 | 1 | 0% | 1,578 | 1,889 | +20% | 0 | 0 | — |
case-06 | pass→pass | 7,653 | 4,797 | -37% | 1 | 1 | 0% | 1,296 | 1,404 | +8% | 0 | 0 | — |
case-07 | pass→pass | 5,774 | 2,003 | -65% | 1 | 1 | 0% | 934 | 852 | -9% | 0 | 0 | — |
case-08 | pass→pass | 8,660 | 4,677 | -46% | 1 | 1 | 0% | 1,355 | 1,332 | -2% | 0 | 0 | — |
case-09 | pass→pass | 13,223 | 10,195 | -23% | 1 | 1 | 0% | 2,442 | 2,318 | -5% | 0 | 0 | — |
case-10 | pass→pass | 4,989 | 5,244 | +5% | 1 | 1 | 0% | 784 | 1,427 | +82% | 0 | 0 | — |
case-11 | pass→pass | 9,119 | 7,278 | -20% | 1 | 1 | 0% | 1,589 | 1,857 | +17% | 0 | 0 | — |
case-12 | pass→pass | 11,146 | 12,375 | +11% | 1 | 1 | 0% | 1,985 | 2,830 | +43% | 0 | 0 | — |
case-13 | pass→pass | 6,728 | 7,379 | +10% | 1 | 1 | 0% | 1,062 | 1,928 | +82% | 0 | 0 | — |
case-14 | fail→pass | 10,253 | 10,537 | +3% | 1 | 1 | 0% | 1,491 | 984 | -34% | 0 | 0 | — |
case-15 | fail→pass | 10,141 | 2,559 | -75% | 1 | 1 | 0% | 2,145 | 1,029 | -52% | 0 | 0 | — |
case-16 | fail→pass | 8,473 | 1,431 | -83% | 1 | 1 | 0% | 1,425 | 776 | -46% | 0 | 0 | — |
case-17 | pass→pass | 11,741 | 2,746 | -77% | 1 | 1 | 0% | 2,007 | 1,067 | -47% | 0 | 0 | — |
case-18 | pass→pass | 9,368 | 3,475 | -63% | 1 | 1 | 0% | 1,603 | 1,190 | -26% | 0 | 0 | — |
case-19 | pass→pass | 13,070 | 12,256 | -6% | 1 | 1 | 0% | 1,987 | 2,579 | +30% | 0 | 0 | — |
case-20 | pass→pass | 3,811 | 4,249 | +11% | 1 | 1 | 0% | 670 | 1,275 | +90% | 0 | 0 | — |
case-21 | pass→pass | 3,594 | 3,888 | +8% | 1 | 1 | 0% | 789 | 1,335 | +69% | 0 | 0 | — |
case-22 | pass→pass | 4,942 | 6,030 | +22% | 1 | 1 | 0% | 1,033 | 1,817 | +76% | 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, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +14 percentage points is the difference between those two pass rates over the 19 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.