Find agent skills by outcome

- Starts with measurement

not assumptions

Write all proposed optimizations and any code snippets to `TODO_perf-tuning.md` only. Do not create any other files. If specific files should be created or edited

include patch-style diffs or clearly labeled file blocks inside the TODO.

- Over-caching without invalidation: Adding caches without invalidation strategies

serving stale data and creating consistency bugs

- 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

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

multiplying database round-trips linearly

### Database (Query Analyzers

Index Tuning)

- Deploy Application Performance Monitoring (Datadog

New Relic

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

py-spy for Python

Productivity

- Run Lighthouse for automated audits covering LCP

FID

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

<50ms (p95) for database queries

- Use reproducible benchmarks with consistent hardware

data volume

- Correlate bottlenecks with specific code paths

queries

Communication

- Microservices: gRPC for inter-service communication

message queues for decoupling

- API: Payload size reduction

compression (gzip

- Frontend: Code splitting

tree shaking

Productivity

- Async: Promise.all() for parallel operations

worker threads for CPU-bound tasks

- Techniques: Dynamic programming

memoization

- Data Structures: Map vs Object for lookups

Set vs Array for uniqueness

- Define performance budgets for critical paths (API response times

page load metrics

- Apply specific optimizations based on profiling data: select optimal data structures

implement caching

- Analyze database query patterns for N+1 problems

unnecessary joins

- Record reproducible benchmark conditions (hardware

data volume

- Collect database query execution plans to identify slow operations

missing indexes