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PradnyeshpPradnyeshp/Claude-Skills/.claude/skills/optimize/SKILL.md

optimize

Skill2 starsChanged 4 months agoMITView on GitHub

Find and fix performance bottlenecks in a function, file, or hot path — slow loops, N+1 queries, redundant work, bad complexity, excess allocations. Use when the user says "optimize this", "this is slow", "improve performance", "speed this up", "why is this slow", or "reduce latency/memory".

78 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 75 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Verify: Run the tests to confirm correctness.

    line 23
  2. Distinctive:

    Never trade correctness for speed.

    line 27
  3. Distinctive:

    Don't micro-optimize readable code for negligible gains — call it out if the bottleneck is elsewhere (or not worth fixing).

    line 28

“Common” is measured: kept by at least 6 in 10 of the 75 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Test setup (used to confirm behavior is preserved after optimizing)

!ls package.json pyproject.toml Cargo.toml go.mod Makefile 2>/dev/null

Instructions

Optimize the target in $ARGUMENTS. If no target is given, ask what's slow and how it's measured (input size, latency observed, profiler output).

Method — measure, don't guess

  1. Establish the cost. Identify what "slow" means here: time complexity, query count, allocations, I/O, or wall-clock. Use any profiler output, benchmark, or timing the user has. If none exists, reason explicitly about the dominant cost — and say it's an estimate.
  2. Find the real bottleneck. Read the hot path and locate where the time/memory actually goes. Common culprits: nested loops (O(n²)), N+1 queries, repeated work that could be hoisted or memoized, unnecessary copies/allocations, sync I/O in a loop, missing indexes, reprocessing unchanged data.
  3. Optimize the dominant cost first. A 10× win on 5% of runtime is noise. Target the part that actually dominates. Prefer algorithmic/structural wins (better data structure, batching, caching) over micro-tuning.
  4. Preserve behavior. The optimized code must produce identical results. Keep the public interface stable.
  5. Verify. Run the tests to confirm correctness. If a benchmark exists, run it before and after and report the delta; otherwise explain the expected improvement (e.g. "O(n²) → O(n log n)").

Rules

  1. Never trade correctness for speed. Flag any change that alters edge-case behavior.
  2. Don't micro-optimize readable code for negligible gains — call it out if the bottleneck is elsewhere (or not worth fixing).
  3. Note any trade-offs introduced (added memory for caching, added complexity, eventual consistency).
  4. End with: what was slow, what you changed, and the measured or expected impact.

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