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complexity-optimizer

Skill16 starsChanged 5 months agoMITView on GitHub

Analyze or improve codebase complexity and performance hotspots without broad rewrites. Use when reviewing nested loops, repeated scans, N+1 queries, rendering churn, excessive recomputation, slow tests, large-input paths, memory pressure, or requests to reduce complexity while preserving behavior and tests.

3 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    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:

    Before editing, preserve behavior with existing tests, a focused regression test, a fixture, or a clear invariant.

    line 33
  2. Distinctive:

    avoid caches without invalidation strategy.

    line 40
  3. Distinctive:

    Run narrow checks first, then broader relevant checks.

    line 41
  4. Distinctive:

    before/after complexity estimates.

    line 59

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

Complexity Optimizer

Optimize only when behavior is understood and can be preserved. A small measured improvement beats a clever rewrite.

Modes

  • Report mode: rank opportunities and do not edit.
  • Implementation mode: change scoped code after identifying a repro, test, benchmark, or reviewable invariant.
  • Audit mode: scan broad areas, then inspect only the highest-impact leads.

Workflow

  1. Establish scope: language, framework, hot paths, data sizes, tests, build command, and user-facing risk.
  2. Find leads: nested scans, repeated filtering, loops inside render paths, N+1 database/API calls, redundant serialization, unbounded recursion, cache misses, and avoidable work in hooks or lifecycle methods.
  3. Rank by likely impact and blast radius. Treat static findings as leads, not proof.
  4. For each serious finding, record:
    • file and location;
    • current pattern;
    • estimated current complexity;
    • proposed change;
    • expected complexity after change;
    • correctness risk;
    • verification needed.
  5. Before editing, preserve behavior with existing tests, a focused regression test, a fixture, or a clear invariant.
  6. Optimize conservatively:
    • replace repeated linear lookup with maps or sets when equality is stable;
    • pre-index or group data before joins;
    • batch database/API access while preserving tenant, permission, filtering, ordering, and pagination;
    • memoize derived UI data only when inputs are stable;
    • virtualize or paginate large rendered collections;
    • avoid caches without invalidation strategy.
  7. Run narrow checks first, then broader relevant checks.

Safety Checklist

Before finalizing:

  • output ordering is preserved or intentionally changed;
  • duplicates and null/missing values are handled;
  • mutation and object identity assumptions are not broken;
  • authorization and tenant filters survive batching;
  • cache invalidation is explicit;
  • the original performance or failure signal was rerun.

Final Report

Include:

  • top findings or changed files;
  • before/after complexity estimates;
  • verification commands and results;
  • residual risks and missing measurement gaps.

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