nickcrew/workflow-performance
skills/workflow-performance/SKILL.md
Systematic performance analysis and optimization. Use when things are slow, need optimization, or preparing for scale.
$ install --global
skillsauthnpx skillsauth add nickcrew/claude-ctx-plugin workflow-performanceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 15, 2026, 12:07 PM11.7s2 files scanned
SKILL.md
- name:
- workflow-performance
- description:
- Systematic performance analysis and optimization. Use when things are slow, need optimization, or preparing for scale.
Performance 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
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