Skill: alirezarezvani/cs-performance-profiler (version 1.0.0)

--- name: "performance-profiler" description: "Performance Profiler" --- # Performance Profiler **Tier:** POWERFUL **Category:** Engineering **Domain:** Performance Engineering --- ## Overview 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. ## Core Capabilities - **CPU profiling** — flamegraphs for Node.js, py-spy for Python, pprof for Go - **Memory profiling** — heap snapshots, leak detection, GC pressure - **Bundle analysis** — webpack-bundle-analyzer, Next.js bundle analyzer - **Database optimization** — EXPLAIN ANALYZE, slow query log, N+1 detection - **Load testing** — k6 scripts, Artillery scenarios, ramp-up patterns - **Before/after measurement** — establish baseline, profile, optimize, verify --- ## When to Use - App is slow and you don't know where the bottleneck is - P99 latency exceeds SLA before a release - Memory usage grows over time (suspected leak) - Bundle size increased after adding dependencies - Preparing for a traffic spike (load test before launch) - Database queries taking >100ms --- ## Golden Rule: Measure First ```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 ``` --- ## Node.js Profiling → See references/profiling-recipes.md for details ## Before/After Measurement Template ```markdown ## Performance Optimization: [What You Fixed] **Date:** 2026-03-01 **Engineer:** @username **Ticket:** PROJ-123 ### Problem [1-2 sentences: what was slow, how was it observed] ### Root Cause [What the profiler revealed] ### Baseline (Before) | Metric | Value | |--------|-------| | P50 latency | 480ms | | P95 latency | 1,240ms | | P99 latency | 3,100ms | | RPS @ 50 VUs | 42 | | Error rate | 0.8% | | DB queries/req | 23 (N+1) | Profiler evidence: [link to flamegraph or screenshot] ### Fix Applied [What changed — code diff or description] ### After | Metric | Before | After | Delta | |--------|--------|-------|-------| | P50 latency | 480ms | 48ms | -90% | | P95 latency | 1,240ms | 120ms | -90% | | P99 latency | 3,100ms | 280ms | -91% | | RPS @ 50 VUs | 42 | 380 | +804% | | Error rate | 0.8% | 0% | -100% | | DB queries/req | 23 | 1 | -96% | ### Verification Load test run: [link to k6 output] ``` --- ## Optimization Checklist ### Quick wins (check these first) ``` Database □ Missing indexes on WHERE/ORDER BY columns □ N+1 queries (check query count per request) □ Loading all columns when only 2-3 needed (SELECT *) □ No LIMIT on unbounded queries □ Missing connection pool (creating new connection per request) Node.js □ Sync I/O (fs.readFileSync) in hot path □ JSON.parse/stringify of large objects in hot loop □ Missing caching for expensive computations □ No compression (gzip/brotli) on responses □ Dependencies loaded in request handler (move to module level) Bundle □ Moment.js → dayjs/date-fns □ Lodash (full) → lodash/function imports □ Static imports of heavy components → dynamic imports □ Images not optimized / not using next/image □ No code splitting on routes API □ No pagination on list endpoints □ No response caching (Cache-Control headers) □ Serial awaits that could be parallel (Promise.all) □ Fetching related data in a loop instead of JOIN ``` --- ## Common Pitfalls - **Optimizing without measuring** — you'll optimize the wrong thing - **Testing in development** — profile against production-like data volumes - **Ignoring P99** — P50 can look fine while P99 is catastrophic - **Premature optimization** — fix correctness first, then performance - **Not re-measuring** — always verify the fix actually improved things - **Load testing production** — use staging with production-size data --- ## Best Practices 1. **Baseline first, always** — record metrics before touching anything 2. **One change at a time** — isolate the variable to confirm causation 3. **Profile with realistic data** — 10 rows in dev, millions in prod — different bottlenecks 4. **Set performance budgets** — `p(95) < 200ms` in CI thresholds with k6 5. **Monitor continuously** — add Datadog/Prometheus metrics for key paths 6. **Cache invalidation strategy** — cache aggressively, invalidate precisely 7. **Document the win** — before/after in the PR description motivates the team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)