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Performance

Skill byerlikaya/claude-starter-kit/plugin/skills/performance

Enterprise engineering workflow for Claude Code — not just prompts. AI agents that plan, build, audit, and ship with security gates, privacy checks, and approval-controlled commits. Safely adopt it into new or existing repositories.

Install
npx -y skills add byerlikaya/claude-starter-kit --skill performance

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One thing to look at

  • 20 stars20 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Stack-agnostic performance: measure first, find the bottleneck, then optimise. N+1, needless allocation, wrong async boundary, missing index/cache, heavy payload. No premature optimisation. Trigger phrases: "performance", "slow", "optimization", "profiling", "N+1", "latency", "memory leak", "load test"

SKILL.md

1.8 KB, as published. Nobody here has run it

Performance

Core rule: measure first, optimize later. Optimization without measurement is a guess; it usually speeds up the wrong place and adds complexity. Stack-agnostic; do a web search when you need the profiling tool/library.

Method (in order)

  1. Set a target — what is "acceptable"? (p95 latency, throughput, memory ceiling). Numeric.
  2. Measure — find the real bottleneck with a profiler/APM/benchmark; don't start from a guess.
  3. Fix the single most expensive thing — Amdahl: speeding up a 5% path by 2x is wasted; target the hot path.
  4. Measure again — did it actually improve, is there a regression.
  5. Stop — once you hit the target, finish; no endless micro-optimization.

Common bottlenecks

Catalog of common bottlenecks + fixes to consult: references/bottlenecks.md.

Measurement tips

  • Measure under load (a single request misleads); with a realistic data volume.
  • Not p50, but p95/p99 — tail latency is what burns the user.
  • Don't trust micro-benchmarks; an end-to-end profile is more honest.

Invariant rules

  1. Don't optimize without measuring — a change without a profile = a guess.
  2. Target the hot path — don't speed up the small share.
  3. Don't break correctness — don't sacrifice behavior/edge cases for speed.
  4. Complexity budget — make an optimization that seriously hurts readability only if there is a measured gain; comment it.
  5. Stop once you hit the target — YAGNI; no premature/excessive optimization.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.