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YosoyepaYosoyepa/write-check-trust/skills/wct-dry/SKILL.md

wct-dry

Skill0 starsChanged 3 months agoMITView on GitHub

Detect fuzzy structural duplication with normalized Python AST fingerprints and extraction pressure. Use for duplication review, generated boilerplate, G-DRY failures, or deciding whether similar code should be extracted or deliberately remain separate.

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    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:

    Prefer deletion or reuse of an existing function over introducing a new generic helper.

    line 12

≈ “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

Detect structural duplication

  1. Run uv run wct dry --json [paths...].
  2. Interpret Jaccard score >= 0.82 as a candidate, not an automatic refactor.
  3. Act only on ai_actionability: EXTRACT; inspect variation_points and extraction_pressure first.
  4. Preserve small test case matrices; repeated test shape can encode useful coverage.
  5. Prefer deletion or reuse of an existing function over introducing a new generic helper.

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