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Performance optimization

Skill vignesh2027/AI-AGENT-SKILLS/skills/performance-optimization

Turn your ai agent into senior engineer..The result is fast code that fails slowly. AI Agent Skills solves this by giving agents the same disciplined workflows senior engineers use

Install
npx -y skills add vignesh2027/AI-AGENT-SKILLS --skill performance-optimization

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Profile before optimizing; optimize with evidence, not intuition

SKILL.md

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Overview

Performance optimization without profiling is guessing. This skill enforces: measure first, optimize the bottleneck, measure again. It prevents wasted effort on non-bottlenecks and ensures optimizations don't regress correctness.

When to Use

  • When performance doesn't meet SLO
  • Before any "performance improvement" PR
  • When a feature is slow and the cause is unknown
  • As part of the /review workflow for latency-sensitive paths

Process

Step 1: Measure the baseline

Before touching any code, record: current p50/p95/p99 latency, throughput, error rate under representative load. Without a baseline, you can't prove improvement.

Step 2: Profile to find the bottleneck

Run a profiler, not your intuition:

  • CPU-bound: CPU profiler (flamegraph)
  • Memory-bound: heap profiler, allocation profiler
  • I/O-bound: database query analyzer, network profiler
  • Web frontend: Chrome DevTools Performance tab, Lighthouse

The bottleneck is almost never where you think it is.

Step 3: Identify the worst offender

The single slowest operation in the critical path. Fix that first. Do not optimize non-bottlenecks.

Step 4: Write a benchmark before optimizing

Create a benchmark that isolates the bottleneck and can be run repeatedly. This is your before/after comparison.

Step 5: Optimize

Common patterns:

  • Database: add missing indexes, eliminate N+1 queries, batch reads, use projections (don't SELECT *)
  • Memory: streaming vs loading, lazy evaluation, object pooling
  • CPU: algorithmic improvement, caching, memoization
  • Network: batching, compression, HTTP/2, CDN, edge caching
  • Frontend: code splitting, lazy loading, virtual scrolling, image optimization

Step 6: Measure the improvement

Run the benchmark before and after. Calculate: % improvement in p99, % reduction in resource usage. If the improvement is not measurable, the optimization was not worth the complexity.

Step 7: Verify correctness

Run the full test suite. Performance optimizations frequently introduce bugs.

Step 8: Document the optimization

Record: what was slow, why, what was done, and the measured improvement. Future engineers will need to understand why this code looks unusual.

Anti-Rationalizations

"I know this is slow — I don't need to profile" Everyone thinks they know where the bottleneck is. Profilers are always more accurate than intuition.

"This optimization is obvious — I don't need a benchmark" Without a benchmark, "obvious improvement" is also "unmeasured claim."

Verification Requirements

  • Baseline measured (p50/p95/p99) before any changes
  • Profiler output reviewed to identify actual bottleneck
  • Benchmark written before optimization
  • Improvement measured and quantified (not "feels faster")
  • Test suite passes after optimization

What ships with it

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