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performance-optimization

Skill4 starsChanged 5 months agoApache-2.0View on GitHub

Profile systems, identify bottlenecks, optimize code and infrastructure for speed, throughput, and resource efficiency.

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SKILL.md

Skill: Performance Optimization

Systematic approach to identifying and eliminating performance bottlenecks through profiling, benchmarking, and data-driven optimization.

Process

  1. Establish Baselines — Measure current performance (latency, throughput, memory, CPU)
  2. Profile — Identify where time/resources are spent (flame graphs, traces, logs)
  3. Find Bottlenecks — Rank by impact (80/20 rule: fix the top bottlenecks first)
  4. Optimize — Apply targeted fixes (algorithm, caching, parallelization, resource allocation)
  5. Verify — Re-measure to confirm improvements
  6. Prevent Regressions — Add performance tests, monitoring, budgets

Key Metrics

  • Latency: p50, p95, p99 response time
  • Throughput: Requests per second, transactions per minute
  • Resource Usage: CPU %, memory consumption, disk I/O
  • Cost: Infrastructure cost per unit work

Common Bottlenecks

  • Inefficient algorithms (O(n²) instead of O(n log n))
  • Missing indexes on database queries
  • Synchronous operations that should be async
  • Excessive logging or serialization
  • Memory leaks or inefficient data structures
  • Network calls without batching/caching
  • Single-threaded workloads that could parallelize

Optimization Strategies

  1. Algorithmic: Better algorithm selection
  2. Caching: Reduce computation, add layer caching
  3. Async: Non-blocking operations, parallelization
  4. Resource Allocation: CPU, memory, connections tuning
  5. Infrastructure: Scaling, CDN, geographic distribution
  6. Monitoring: Real-time alerting on perf regressions

Status: Ready for profiling, optimization, and performance tuning Best for: Latency reduction, throughput improvement, resource efficiency

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