ai-slop-cleaner

name	ai-slop-cleaner
description	Clean AI-generated code slop with a regression-safe, deletion-first workflow and optional reviewer-only mode
requires-omc	true
level	3

AI Slop Cleaner

Use this skill to clean AI-generated code slop without drifting scope or changing intended behavior. In OMC, this is the bounded cleanup workflow for code that works but feels bloated, repetitive, weakly tested, or over-abstracted.

When to Use

Use this skill when:

• the user explicitly says deslop, anti-slop, or AI slop
• the request is to clean up or refactor code that feels noisy, repetitive, or overly abstract
• follow-up implementation left duplicate logic, dead code, wrapper layers, boundary leaks, or weak regression coverage
• the user wants a reviewer-only anti-slop pass via --review
• the goal is simplification and cleanup, not new feature delivery

When Not to Use

Do not use this skill when:

• the task is mainly a new feature build or product change
• the user wants a broad redesign instead of an incremental cleanup pass
• the request is a generic refactor with no simplification or anti-slop intent
• behavior is too unclear to protect with tests or a concrete verification plan

OMC Execution Posture

• Preserve behavior unless the user explicitly asks for behavior changes.
• Lock behavior with focused regression tests first whenever practical.
• Write a cleanup plan before editing code.
• Prefer deletion over addition.
• Reuse existing utilities and patterns before introducing new ones.
• Avoid new dependencies unless the user explicitly requests them.
• Keep diffs small, reversible, and smell-focused.
• Stay concise and evidence-dense: inspect, edit, verify, and report.
• Treat new user instructions as local scope updates without dropping earlier non-conflicting constraints.

Scoped File-List Usage

This skill can be bounded to an explicit file list or changed-file scope when the caller already knows the safe cleanup surface.

• Good fit: oh-my-claudecode:ai-slop-cleaner skills/ralph/SKILL.md skills/ai-slop-cleaner/SKILL.md
• Good fit: a Ralph session handing off only the files changed in that session
• Preserve the same regression-safe workflow even when the scope is a short file list
• Do not silently expand a changed-file scope into broader cleanup work unless the user explicitly asks for it

Ralph Integration

Ralph can invoke this skill as a bounded post-review cleanup pass.

• In that workflow, the cleaner runs in standard mode (not --review)
• The cleanup scope is the Ralph session's changed files only
• After the cleanup pass, Ralph re-runs regression verification before completion
• --review remains the reviewer-only follow-up mode, not the default Ralph integration path

Review Mode (--review)

--review is a reviewer-only pass after cleanup work is drafted. It exists to preserve explicit writer/reviewer separation for anti-slop work.

• Writer pass: make the cleanup changes with behavior locked by tests.
• Reviewer pass: inspect the cleanup plan, changed files, and verification evidence.
• The same pass must not both write and self-approve high-impact cleanup without a separate review step.

In review mode:

1. Do not start by editing files.
2. Review the cleanup plan, changed files, and regression coverage.
3. Check specifically for:
   • leftover dead code or unused exports
   • duplicate logic that should have been consolidated
   • needless wrappers or abstractions that still blur boundaries
   • missing tests or weak verification for preserved behavior
   • cleanup that appears to have changed behavior without intent
4. Produce a reviewer verdict with required follow-ups.
5. Hand needed changes back to a separate writer pass instead of fixing and approving in one step.

Workflow

1. Protect current behavior first

   • Identify what must stay the same.
   • Add or run the narrowest regression tests needed before editing.
   • If tests cannot come first, record the verification plan explicitly before touching code.

2. Write a cleanup plan before code

   • Bound the pass to the requested files or feature area.
   • List the concrete smells to remove.
   • Order the work from safest deletion to riskier consolidation.

3. Classify the slop before editing

   • Duplication — repeated logic, copy-paste branches, redundant helpers
   • Dead code — unused code, unreachable branches, stale flags, debug leftovers
   • Needless abstraction — pass-through wrappers, speculative indirection, single-use helper layers
   • Boundary violations — hidden coupling, misplaced responsibilities, wrong-layer imports or side effects
   • Missing tests — behavior not locked, weak regression coverage, edge-case gaps

4. Run one smell-focused pass at a time

   • Pass 1: Dead code deletion
   • Pass 2: Duplicate removal
   • Pass 3: Naming and error-handling cleanup
   • Pass 4: Test reinforcement
   • Re-run targeted verification after each pass.
   • Do not bundle unrelated refactors into the same edit set.

5. Run the quality gates

   • Keep regression tests green.
   • Run the relevant lint, typecheck, and unit/integration tests for the touched area.
   • Run existing static or security checks when available.
   • If a gate fails, fix the issue or back out the risky cleanup instead of forcing it through.

6. Close with an evidence-dense report Always report:

   • Changed files
   • Simplifications
   • Behavior lock / verification run
   • Remaining risks

Usage

• /oh-my-claudecode:ai-slop-cleaner <target>
• /oh-my-claudecode:ai-slop-cleaner <target> --review
• /oh-my-claudecode:ai-slop-cleaner <file-a> <file-b> <file-c>
• From Ralph: run the cleaner on the Ralph session's changed files only, then return to Ralph for post-cleanup regression verification

Good Fits

Good: deslop this module: too many wrappers, duplicate helpers, and dead code

Good: cleanup the AI slop in src/auth and tighten boundaries without changing behavior

Bad: refactor auth to support SSO

Bad: clean up formatting

Related skills

• requesting-code-review → receiving-code-review — use this skill before requesting review to strip boilerplate and unfounded claims. Polish the work, then request review.
• writing-skills — when authoring new skill documentation, run this skill afterward to remove AI-generated filler (clichés, unsupported claims, boilerplate marketing language).