Performance

Purpose: Optimize application speed, throughput, and resource usage for production loads. Strategy: Profile first, optimize bottlenecks, measure impact. Note: For language-specific implementations, see C# Development or Python Development.

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When to Use This Skill

• Diagnosing slow application endpoints
• Implementing caching strategies
• Profiling CPU or memory bottlenecks
• Optimizing database query performance
• Setting up performance monitoring and alerting

Prerequisites

• Application running in a profiling-capable environment
• Access to monitoring tools

Decision Tree

Performance concern?
+- Not yet measured? -> Profile FIRST (don't guess)
| +- .NET -> dotnet-trace / BenchmarkDotNet
| +- Python -> cProfile / py-spy
| - Node.js -> clinic.js / --prof
+- Slow API response?
| +- Database query? -> EXPLAIN ANALYZE -> add index
| +- External service? -> Add caching + async calls
| - Computation? -> Optimize algorithm or add memoization
+- High memory usage?
| +- Large collections? -> Stream/paginate instead of loading all
| - Memory leaks? -> Profile allocations, check dispose patterns
+- Concurrency bottleneck?
| +- I/O bound? -> async/await (don't block threads)
| - CPU bound? -> Parallel processing / background workers
- Quick wins? -> See Quick Wins table below

Quick Wins

Optimization	Impact	Effort
Enable Response Compression	70-90% size reduction	Low
Add Database Indexes	10-100x query speed	Low
Implement Caching	50-99% latency reduction	Medium
Use Async I/O	5-10x throughput	Medium
Fix N+1 Queries	10-1000x DB performance	Medium

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Performance Anti-Patterns

Anti-Pattern	Problem	Solution
Premature Optimization	Optimize before profiling	Profile first, optimize bottlenecks
Over-Caching	Cache everything	Cache strategically based on access patterns
Blocking I/O	Synchronous network calls	Use async/await
No Pagination	Load all results	Paginate large datasets
Missing Indexes	Full table scans	Add indexes on frequently queried columns
N+1 Queries	Loop over queries	Use JOINs or batch loading

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Optimization Checklist

Before Production:

• Profile application under realistic load
• Add database indexes on frequently queried columns
• Implement caching for expensive operations
• Enable response compression
• Fix N+1 query problems
• Use connection pooling
• Implement async I/O where applicable
• Paginate large result sets
• Set up monitoring and alerts
• Conduct load testing
• Set performance budgets
• Optimize static asset delivery

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Resources

Profiling Tools:

• .NET: BenchmarkDotNet, dotTrace, PerfView
• Python: cProfile, py-spy, Scalene
• Node.js: clinic.js, 0x, Chrome DevTools
• Java: JProfiler, VisualVM

Load Testing:

• k6 - Modern load testing
• Apache JMeter - Industry standard
• Gatling - Scala-based testing

Guides:

• Web Performance Working Group
• High Performance Browser Networking

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See Also: Skills.md - AGENTS.md

Last Updated: January 27, 2026

Scripts

Script	Purpose	Usage
run-benchmark.ps1	Run benchmarks (.NET/Python/Node) with baseline comparison	./scripts/run-benchmark.ps1 [-Baseline baseline.json]

Troubleshooting

Issue	Solution
Cache stampede on expiry	Use cache-aside with staggered TTL or background refresh
Memory leak in production	Profile with dotMemory/py-spy, check for unbounded collections
High latency spikes	Check GC pauses, database connection pool, and external service timeouts

References

• Profiling Caching Db
• Optimization Techniques