| name | performance |
| description | Diagnose and improve runtime performance including N+1 queries, slow queries, caching, latency, memory, throughput, and resource efficiency using framework-agnostic principles. |
| metadata | {"internal":true} |
Performance
Use this skill when work involves latency, throughput, resource usage, query efficiency, rendering cost, memory pressure, or scaling bottlenecks.
This skill is technology agnostic.
Measure First
Do not optimize solely from intuition.
Identify:
- the slow operation
- current baseline
- dominant cost
- expected improvement
- acceptable tradeoffs
Use measurements appropriate to the system.
Common Bottlenecks
Investigate relevant:
- repeated database queries
- N+1 access
- unnecessary network calls
- repeated computation
- inefficient algorithms
- excessive serialization
- large payloads
- blocking I/O
- unnecessary rendering
- memory growth
- contention
- cache misses
- excessive file operations
Do not assume the database is always the bottleneck.
Database Performance
Consider:
- query count
- query plans
- indexes
- join behavior
- selected columns
- pagination
- batch operations
- eager/bulk loading
- connection usage
Add indexes based on actual query patterns.
Caching
Cache when:
- computation or retrieval is meaningfully expensive
- reuse is likely
- consistency requirements are understood
Define:
- cache key
- lifetime
- invalidation
- ownership
- failure behavior
Do not add caching merely to hide an inefficient design without understanding correctness implications.
Memory
Avoid loading unbounded datasets into memory.
Prefer:
- streaming
- chunking
- pagination
- iterators
- bounded batches
when processing large data.
Concurrency
Parallelism can improve throughput but may increase:
- contention
- memory usage
- rate-limit pressure
- database load
- ordering complexity
Use bounded concurrency.
Verification
Compare before and after.
Verify:
- functional behavior remains correct
- measured metric improves
- resource usage remains acceptable
- no significant regression is introduced elsewhere
Performance work without measurement should be treated as a hypothesis, not a proven optimization.
Framework Adaptation
Use native profiling, caching, ORM, worker, and runtime mechanisms from the detected project stack.
Do not invent framework-specific APIs.
