performance-optimization

name	Performance Optimization
description	Performance analysis, profiling, and optimization. Use when diagnosing slow code, optimizing database queries, reducing memory usage, or improving application responsiveness.

Performance Optimization

You are optimizing performance. Measure first — never optimize based on intuition alone. Performance work is almost always multi-phase. Start with a todo plan that covers baseline measurement, bottleneck identification, implementation, and verification, then keep it current as you move through the work.

Available Tools

• read — read source code, configs, query definitions
• search — find hot paths, database queries, bottleneck patterns
• execute — run profilers, benchmarks, EXPLAIN queries, time commands
• edit — apply optimizations
• web — research optimization techniques, library performance guides
• todo — track optimization phases and measurements

Process

1. Plan the Optimization

todo({ todoList: [
  { id: 1, title: "Define problem and target", status: "in-progress" },
  { id: 2, title: "Measure baseline performance", status: "not-started" },
  { id: 3, title: "Identify bottleneck", status: "not-started" },
  { id: 4, title: "Apply optimization", status: "not-started" },
  { id: 5, title: "Measure improvement", status: "not-started" },
  { id: 6, title: "Verify correctness preserved", status: "not-started" }
]})

2. Define the Problem

• What is slow? (API response, build, query, rendering)
• How slow is it? Use execute to measure current performance
• What is the target? (acceptable latency, throughput)

3. Measure Baseline

• Use execute to run benchmarks, time commands, profile
• For DB: use execute to run EXPLAIN on slow queries
• Record exact numbers for before/after comparison

4. Identify Bottleneck

• Use read to trace the hot path from entry point to response
• Use search to find N+1 patterns, repeated computations, blocking calls
• Check: sequential vs. parallelizable operations, unnecessary allocations, missing indexes

5. Optimize (in order of impact)

1. Algorithm/approach changes — use edit to restructure
2. Eliminate unnecessary work — use search to find redundant calls
3. Parallelize — edit to run independent operations concurrently
4. Cache — edit to add caching with proper invalidation
5. Batch — combine multiple small operations

6. Verify

• Use execute to measure after the change — confirm improvement
• Use execute to run tests — confirm correctness preserved
• Compare before/after numbers explicitly

Rules

• Update todo as you complete each phase
• Treat todo as required unless the task is a single measurement with no follow-up change
• Always measure before and after — "it feels faster" is not evidence
• Optimize the biggest bottleneck first
• Don't sacrifice readability for marginal gains in cold paths
• Document why an optimization exists if it makes code less obvious