About workflow-performance
workflow-performance is a performance optimization skill for teams diagnosing and improving application performance. It organizes work from baseline measurement through optimization, validation, load testing, and monitoring.
Key features
- 01Baseline measurement of latency and resource use
- 02Profiling and bottleneck prioritization
- 03Database, code, frontend, and infrastructure optimization
- 04Target-based validation and load testing
- 05Monitoring and alert setup
Use cases
- 01Investigating slow responses or high resource use
- 02Prioritizing performance fixes by impact and effort
- 03Checking changes against performance targets
- 04Preparing load tests and ongoing monitoring
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/workflow-performancein Claude Code to confirm it loaded.
Bundled filesreferences/optimization-techniques.md
Details
From the project READMEPerformance Optimization Workflow
Systematic approach to finding and fixing performance issues.
Phase 1: Baseline
Agents: performance-engineer
Measure current state:
- Response times (p50, p95, p99)
- Memory usage
- CPU utilization
- Database query times
- Bundle sizes (frontend)
- Render performance
Output: Baseline metrics report
Phase 2: Bottleneck Identification
Agents: performance-engineer
Analysis:
- Profiling (CPU, memory)
- Query analysis (slow query log, EXPLAIN)
- Bundle analysis (webpack-bundle-analyzer)
- Network analysis (waterfall, latency)
Output: Bottleneck list with priority ranking
Phase 3: Optimization Planning
Agents: requirements-analyst
- Prioritize by impact vs effort
- Define expected improvements
- Determine implementation order
- Set target metrics
Phase 4: Database Optimization
Agents: database-optimizer
Tasks:
- Query optimization (rewrite slow queries)
- Index creation/optimization
- Caching strategy (Redis, memcached)
- Connection pooling
Phase 5: Code Optimization
Agents: performance-engineer
Focus:
- Algorithm efficiency (O(n) → O(log n))
- Memory management (leaks, allocation)
- Async operations (parallelize I/O)
- Application-level caching
Phase 6: Frontend Optimization
Agents: performance-engineer
Tasks:
- Bundle size reduction
- Code splitting
- Lazy loading
- Asset optimization (images, fonts)
- Render optimization (virtualization, memoization)
Phase 7: Infrastructure Optimization
Agents: devops-architect
Areas:
- Scaling strategy (horizontal/vertical)
- Caching layers (CDN, reverse proxy)
- Load balancing
- Resource allocation
Phase 8: Validation
Agents: performance-engineer
Blocking: Must meet targets
Targets:
- Response time: <200ms (p95)
- Memory usage: <200MB
- Bundle size: <500KB
Phase 9: Load Testing
Agents: performance-engineer
Scenarios:
- Normal load (expected traffic)
- Peak load (2-3x normal)
- Stress test (find breaking point)
Duration: 30min per scenario
Phase 10: Monitoring Setup
Agents: devops-architect
- Performance dashboards
- Alerting rules (degradation detection)
- Automated profiling (continuous)
Success Criteria
- Performance targets met
- Load tests pass
- Monitoring in place
- Documentation complete
Targets
| Metric | Target |
|---|---|
| Response time improvement | 50% |
| Memory reduction | 30% |
| Cost reduction | 20% |
Quick Reference
| Resource | Reference File |
|---|---|
| Optimization Techniques | skills/workflow-performance/references/optimization-techniques.md |
Anti-patterns
- Optimizing without measuring first
- Micro-optimizations before algorithmic fixes
- Optimizing code that isn't the bottleneck
- No load testing before production