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

1. Produce a reviewer verdict with required follow-ups.
2. 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.

1. 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.

1. 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
• UI/design defaults — generic visual patterns that make an AI-built interface feel unreviewed

UI/Design Reviewer Checklist

Use these as review prompts, not absolute bans. Keep intentional brand, accessibility, product-density, or design-system choices when they have a clear rationale.

• Korean readability: flag body text set around 11-12px; Korean body copy generally needs at least 14px unless a validated dense-data exception applies.
• Shadow restraint: question box shadows on every surface, logo, background, card, or icon; keep shadows only where they clarify elevation or interaction.
• Content hierarchy: remove repetitive eyebrow/title/description/extra <p> stuffing when the title already carries the message; avoid generic emoji badges unless they are part of the product voice.
• Palette rationale: challenge default AI blue/purple palettes, especially Tailwind-like #3B82F6, when no brand or system rationale exists.
• Layout rhythm: avoid overly perfect 3- or 4-column uniform grids when the product context benefits from rhythm, emphasis, asymmetry, carousel/bento treatment, or varied card weights.
• Gradient restraint: tone down extreme gradients unless the brand deliberately owns that visual language.

1. 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.

1. 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.

1. 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