Plan and execute regression-tested code cleanup workflows on changed-file scopes.
npx skills add https://github.com/Linnanli/xClaw --skill ai-slop-cleaner-linnanli
Please help me install this Agent Skill. Skill: ai-slop-cleaner Source: https://github.com/Linnanli/xClaw/tree/main/.codex/skills/ai-slop-cleaner Command: npx skills add https://github.com/Linnanli/xClaw --skill ai-slop-cleaner-linnanli
AI-generated code tends to accumulate slop—bloated logic, dead branches, and weak boundaries. This skill provides a disciplined workflow to plan, scope, and execute targeted cleanups that preserve behavior.
Bound the scope with a changed-files list and run a single-pass, regression-tested cleanup workflow with verification after each smell.
High-intent search queries and answers about installing and using this skill.
To clean up AI-generated code safely, use a regression-tested workflow that targets dead code and duplication while enforcing lint and type checks to preserve behavior. Bound the scope to changed files for reversibility.
The best way to refactor dead code and code smells is applying a plan-first approach that scopes edits to changed files, executes bounded refactors, and verifies each smell with regression tests to ensure reversibility.
Yes, you can run a code cleanup pass on just the changed files. The workflow supports scope-bounded edits based on a changed-files list or explicit file lists, enabling targeted and reversible refactors without touching the entire codebase.
AI-generated code accumulates bloat and weak boundaries because models often produce bloated logic, dead branches, and duplicated code. A structured cleanup pipeline with plan-before-edit execution is needed to resolve these specific code smells.
Yes, you need regression tests before removing dead branches and duplication. The cleanup workflow enforces regression tests and lint/type checks after each smell is addressed to verify that the refactor preserves the original behavior.
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