Skill

workflow-performance

Runs 10-phase workflow for performance optimization: baseline metrics, bottleneck identification, database/code/frontend/infrastructure fixes, validation, load testing, monitoring setup. Use for slow apps or scaling.

Redis

Webpack

performance

npx claudepluginhub nickcrew/claude-cortex

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This skill uses the workspace's default tool permissions.

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Systematic approach to finding and fixing performance issues.

Supporting Assets

references/optimization-techniques.md

SKILL.md

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Last CommitApr 23, 2026

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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