Find agent skills by outcome

- Synchronous blocking in async code: Blocking the event loop or thread pool with synchronous operations

destroying concurrency benefits

- Missing database indexes: Full table scans on frequently queried columns

causing query times to grow linearly with data volume

- Memory leaks under load: Allocations growing without bound in long-running processes

leading to OOM crashes in production

- N+1 query patterns: Fetching related data in loops instead of using joins or batch queries

multiplying database round-trips linearly

- Monitor slow query logs and set appropriate thresholds (e.g.

>50ms for OLTP queries)

### Database (Query Analyzers

Index Tuning)

- Implement distributed tracing (Jaeger

Zipkin) to identify cross-service latency bottlenecks

- Run load tests with k6

JMeter

Coding & Debugging

- Use language-specific CPU and memory profilers (pprof for Go

py-spy for Python

- Deploy Application Performance Monitoring (Datadog

New Relic

### Frontend (Chrome DevTools

Lighthouse

### Backend (APM

Profilers

- Address scalability: proactively consider expected input size

memory limitations

- Provide alternative solutions when trade-offs exist (in-place vs additional memory

speed vs memory)

- Identify constraints

input/output requirements

- Remember that premature optimization is counterproductive

but strategic optimization is essential

- Capture percentiles (p50

p95

- Consider the full system impact of optimizations

not just local improvements

- [ ] All optimizations are documented with rationale

measured impact

- Use reproducible benchmarks with consistent hardware

data volume

- [ ] Frontend metrics meet targets: LCP <2.5s

FID <100ms

Coding & Debugging

- [ ] API response times meet SLA: <200ms (p95) for standard endpoints

<50ms (p95) for database queries

After completing optimization

verify:

- Document trade-offs: readability vs performance

memory vs CPU