omar-obando/qwen-orchestrator/performance

Claude Code and agent skill by omar-obando/qwen-orchestrator

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Performance optimization skill with profiling techniques, bottleneck identification, and optimization strategies. Use when improving application speed, reducing resource usage, or diagnosing performance issues.

Install

npx vskill@latest install omar-obando/qwen-orchestrator/performance

SKILL.md

Performance Skill — Optimization & Profiling

This skill provides systematic performance analysis and optimization guidance.

Performance Budget Template

MetricTargetCritical Threshold
First Contentful Paint< 1.5s< 3s
Largest Contentful Paint< 2.5s< 4s
Total Bundle Size< 200KB gzipped< 500KB
API Response Time (p50)< 100ms< 500ms
API Response Time (p99)< 500ms< 2s
Memory Usage< 100MB< 500MB
CPU Usage (steady)< 30%< 80%

Profiling Methodology

1. Measure First
NEVER optimize without measuring first.
ALWAYS establish a baseline before changes.
ALWAYS compare after optimization with real numbers.
2. Identify Bottleneck
LayerToolWhat to Look For
FrontendLighthouse, DevToolsRender blocking, layout thrash
NetworkWireshark, CharlesUnnecessary requests, large payloads
APIAPM toolsSlow endpoints, N+1 queries
DatabaseEXPLAIN, slow query logFull table scans, missing indexes
ComputeCPU profilerHot loops, unnecessary computation
MemoryHeap snapshotLeaks, retained objects
3. Optimize by Category
Algorithmic
  • Replace O(n²) with O(n log n) or O(n)
  • Use appropriate data structures (Set for lookups, Map for key-value)
  • Cache repeated computations (memoization)
  • Lazy evaluation for expensive operations
I/O
  • Batch database queries
  • Use connection pooling
  • Implement pagination
  • Compress responses (gzip/brotli)
  • Use CDN for static assets
Memory
  • Stream large data instead of buffering
  • Release references when done
  • Use object pooling for frequent allocations
  • Avoid closure capture of large objects
Concurrency
  • Use async/await for I/O-bound work
  • Worker threads for CPU-bound work
  • Connection pooling for database/HTTP
  • Backpressure handling for streams

Optimization Patterns

Caching Strategy
Layer 1: In-memory (LRU, TTL: seconds)
Layer 2: Redis/Memcached (TTL: minutes)
Layer 3: Database (materialized views)
Layer 4: CDN (static assets, TTL: hours)
Database Optimization
  1. Index Strategy: Index all WHERE/JOIN columns
  2. Query Optimization: Avoid SELECT \*, use projections
  3. Connection Pooling: Reuse connections
  4. Pagination: Cursor-based for large datasets
  5. Denormalization: For read-heavy workloads

Performance Testing

typescript
describe('Performance', () => {
it('should process 10k records in under 100ms', async () => {
const data = generateTestData(10_000);
const start = performance.now();
const result = processRecords(data);
const elapsed = performance.now() - start;
expect(elapsed).toBeLessThan(100);
expect(result).toHaveLength(10_000);
});
});

Anti-Patterns to Avoid

  • Premature optimization without profiling data
  • Caching everything (cache invalidation is hard)
  • Denormalizing too early
  • Micro-optimizing cold paths
  • Ignoring GC pressure from excessive allocations

When NOT to Use

Do NOT use this skill when:

  • Writing application business logic (use domain-driven skill)
  • Designing database schemas (use database-design skill)
  • Writing API endpoint implementations (use api-design skill)
  • Writing SQL queries (use sql-best-practices skill)
  • Designing multi-page website layouts (use design-system skill)
  • Reviewing git workflows (use git-workflow skill)
  • Analyzing deployment configurations (use deployment skill)

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

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