| id | skill-oh-my-claudecode |
| name | Oh My ClaudeCode |
| version | 1.0.0 |
| description | 5 种执行模式多 Agent 编排：3K Stars，灵活切换 Agent 协作模式 |
| category | prompt |
| tags | ["multi-agent","orchestration","execution-modes","flexibility"] |
| author | Yeachan-Heo |
| repositoryUrl | https://github.com/Yeachan-Heo/oh-my-claudecode |
| parameters | {"transport":"bundle","configTemplate":"{\"name\": \"Oh My ClaudeCode\", \"transport\": \"bundle\", \"type\": \"mcp\"}"} |
Oh My ClaudeCode
name: ai-slop-cleaner
description: Clean AI-generated code slop with a regression-safe, deletion-first workflow and optional reviewer-only mode
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:
- Do not start by editing files.
- Review the cleanup plan, changed files, and regression coverage.
- 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
- Produce a reviewer verdict with required follow-ups.
- Hand needed changes back to a separate writer pass instead of fixing and approving in one step.
Workflow
-
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.
-
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.
-
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.
-
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.
-
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.
-
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
