Agent skill

AI Slop Cleaner

by Yeachan-Heo in Yeachan-Heo/oh-my-claudecode

Cleans up AI-generated code that works but is bloated or repetitive, locking behavior with tests first and deleting before adding, with a reviewer-only mode.

MITAuto-check passedDevelopment

Install AI Slop Cleaner

skills CLI
$ npx skills add Yeachan-Heo/oh-my-claudecode --skill ai-slop-cleaner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Yeachan-Heo/oh-my-claudecode ai-slop-cleaner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Yeachan-Heo/oh-my-claudecode.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-slop-cleaner .claude/skills/ai-slop-cleaner && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-slop-cleaner
GitHub stars
40k
Token cost
~1.9k tokens
SKILL.md length
979 words
Files
1
Skills in repo
47
Repo updated
First seen
Licence
MIT

At a glance

Cleans up AI-generated code that works but is bloated or repetitive, locking behavior with tests first and deleting before adding, with a reviewer-only mode.

  • Works in 5 steps: Do not start by editing files. → Review the cleanup plan, changed files,… → Check specifically for → …
  • Removing duplicate logic and dead code left behind after an AI-assisted change
  • SKILL.md covers When to Use, When Not to Use, OMC Execution Posture and Scoped File-List Usage, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The skill runs a bounded cleanup of code that feels noisy, duplicated, over-abstracted or weakly tested, such as duplicate logic, dead code, wrapper layers and boundary leaks left behind by earlier implementation work. It triggers on words like deslop or anti-slop, and passing --review gives a reviewer-only pass.

The posture is conservative: preserve behavior, lock it with focused regression tests first when practical, write a cleanup plan before touching code, prefer deletion, reuse existing utilities, avoid new dependencies and keep diffs small. It can be limited to an explicit file list or a changed-file scope without silently widening, and in the Ralph workflow it runs on the session's changed files before regression checks are repeated. It is not meant for new features, broad redesigns or code whose behavior is too unclear to protect.

When your agent uses it

  • Removing duplicate logic and dead code left behind after an AI-assisted change
  • Collapsing needless wrapper layers without altering behavior
  • Running a review-only anti-slop pass over a branch

Example prompts

  • “Deslop the files changed in this session and keep the tests passing.”
  • “Run an anti-slop review of src/billing and list what looks bloated.”
  • “The retry code wraps one call in three layers, so clean it up and add regression tests first.”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Do not start by editing files.
  2. Review the cleanup plan, changed files, and regression coverage.
  3. Check specifically for
  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.

What it can do on your machine

Read from SKILL.md and the folder at commit 454bae0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Slop Cleaner loads about 1.9k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 979 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Yeachan-Heo/oh-my-claudecode at commit 454bae0, republished under its MIT licence (© Yeachan-Heo). 979 words, ~1,854 tokens.

Download SKILL.mdSave it as .claude/skills/ai-slop-cleaner/SKILL.md (or your agent's skills folder).
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:

  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.
Show full SKILL.md (457 more words)Show less

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
    • 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.
  2. 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.
  3. 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

© Yeachan-Heo, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/ai-slop-cleaner of Yeachan-Heo/oh-my-claudecode.

Open the folder on GitHubat commit 454bae0

Compare with similar skills

AI Slop Cleaner next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Slop Cleaner compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Slop Cleaner this skillYeachan-Heo/oh-my-claudecode40k—~1.9kAutomated safety check: PassMIT
Ponytail Lazy Developer ModeDietrichGebert/ponytail160k1 repos~873Automated safety check: PassMIT
Codebase Health Refactoringkucherenko/jscpd6.4k—~2.5kAutomated safety check: PassMIT
DRY Refactoring With jscpdkucherenko/jscpd6.4k—~2.1kAutomated safety check: PassMIT
Simplify Recent ChangesQwenLM/qwen-code28k—~1.3kAutomated safety check: PassApache-2.0
Review And Simplify ChangesDimillian/Skills4k—~2kAutomated safety check: PassMIT

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Categories

Questions about AI Slop Cleaner

What does AI Slop Cleaner do?

Cleans up AI-generated code that works but is bloated or repetitive, locking behavior with tests first and deleting before adding, with a reviewer-only mode. The skill runs a bounded cleanup of code that feels noisy, duplicated, over-abstracted or weakly tested, such as duplicate logic, dead code, wrapper layers and boundary leaks left behind by earlier implementation work. It triggers on words like deslop or anti-slop, and passing --review gives a reviewer-only pass.

When should I use AI Slop Cleaner?

AI Slop Cleaner fits situations like: removing duplicate logic and dead code left behind after an AI-assisted change; collapsing needless wrapper layers without altering behavior; running a review-only anti-slop pass over a branch.

How do I install AI Slop Cleaner in Claude Code?

Run `npx skills add Yeachan-Heo/oh-my-claudecode --skill ai-slop-cleaner -a claude-code`. Or copy the skill folder (skills/ai-slop-cleaner in Yeachan-Heo/oh-my-claudecode) into .claude/skills/ai-slop-cleaner in your project. Claude Code loads it when a task matches its description.

How do I install AI Slop Cleaner in Codex?

Run `npx skills add Yeachan-Heo/oh-my-claudecode --skill ai-slop-cleaner -a codex`. Or copy the skill folder (skills/ai-slop-cleaner in Yeachan-Heo/oh-my-claudecode) into .agents/skills/ai-slop-cleaner in your project. Codex loads it when a task matches its description.

Can I use AI Slop Cleaner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Yeachan-Heo/oh-my-claudecode --skill ai-slop-cleaner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-slop-cleaner, .gemini/skills/ai-slop-cleaner, .github/skills/ai-slop-cleaner and .opencode/skills/ai-slop-cleaner in your project.

What does AI Slop Cleaner need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Slop Cleaner is instructions for the agent only.

Does AI Slop Cleaner access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Slop Cleaner safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Slop Cleaner use?

AI Slop Cleaner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Slop Cleaner use?

About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Slop Cleaner?

Skills that share tags, products or a category with AI Slop Cleaner: Ponytail Lazy Developer Mode (DietrichGebert/ponytail, 160k stars), Codebase Health Refactoring (kucherenko/jscpd, 6.4k stars), DRY Refactoring With jscpd (kucherenko/jscpd, 6.4k stars) and Simplify Recent Changes (QwenLM/qwen-code, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Slop Cleaner?

Yeachan-Heo (a GitHub user) maintains it in Yeachan-Heo/oh-my-claudecode, which has 39,751 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on October 8, 2026.

Source: Yeachan-Heo/oh-my-claudecode on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.