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General coding70k · #1743

remove-ai-slops

Helps your agent remove unwanted code patterns or 'smells' from AI-generated code. It's useful for cleaning up code after automatic generation.

What it does

This tool removes 'fat' or unnecessary code generated by artificial intelligence in your code changes. It does so safely, ensuring the program continues to function as before.

When to use it

  • ✓When you ask to 'clean up' or 'remove unnecessary code' from an update.
  • ✓After using an AI to generate code and wanting to review it.
  • ✓To ensure AI-generated code follows best practices.

Example of what to ask your AI

“Clean up AI-generated code in my latest commits.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/code-yeongyu/oh-my-openagent/blob/dev/packages/shared-skills/skills/remove-ai-slops/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/code-yeongyu/oh-my-openagent/blob/dev/packages/shared-skills/skills/remove-ai-slops/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The tool first creates tests to ensure the code works. Then, it analyzes and removes code patterns considered 'fat' by AI. Finally, it re-checks with tests to make sure nothing broke.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add code-yeongyu/oh-my-openagent --skill remove-ai-slops

Or download just the file:

curl -L https://raw.githubusercontent.com/code-yeongyu/oh-my-openagent/dev/packages/shared-skills/skills/remove-ai-slops/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Remove AI Slops Skill

Inputs

  • Default scope: branch diff vs merge-base main (no arguments needed)
  • Optional scope: explicit file list passed by the caller (e.g., a Ralph workflow's changed-files set)

What this skill does

Cleans AI-generated slop from a bounded set of changed files while strictly preserving behavior. Locks behavior with regression tests first, then runs a categorized multi-pass cleanup, then verifies with quality gates and a critical review. Reverts and direct-edits when verification fails.

The core safety invariant: behavior is locked by green tests before a single line is removed. A checklist alone is not safety; a passing regression test is.


Categories (what counts as slop)

The agent looks for these nine categories. The first three are stylistic, the next three are structural, the next two are about hidden cost, and the last is about behavior coverage.

Stylistic

  1. Obvious comments — comments restating code, trivial docstrings, section dividers, commented-out code, vague TODOs/Notes.

    • KEEP: comments explaining WHY (business logic, edge cases, workarounds), ticket links, regex/algorithm explanations.
    • KEEP: BDD markers (# given, # when, # then, # when/then).
  2. Over-defensive code — null checks for guaranteed values, try/except around code that cannot raise, isinstance checks for statically typed params, default values for required params, backward-compat shims, redundant validation duplicated at multiple layers, broad exception catching (except Exception/except BaseException in Python, empty catch {} or catch (e) { console.error(e) } without narrowing in TypeScript/JavaScript).

    • KEEP: validation at system boundaries (user input, external APIs), I/O error handling, nullable DB fields. Top-level boundary catch-all (CLI main(), HTTP handler) with explicit logging + re-raise is acceptable.
    • REFACTOR: except Exception → catch the specific exception you expect. Empty catch {} → add instanceof narrowing or re-throw. catch (e) { log(e) } → narrow with instanceof, handle known cases, re-throw unknown.
    • PROOF REQUIRED: before deleting any validation or error handling at a trust boundary, Phase 2 must include an adversarial regression (malformed or hostile input) that fails if the guard is removed. No adversarial test → the guard stays. Redundant defense to remove is a duplicate of a check that already runs inside the boundary; a guard with no proof of redundancy is load-bearing.
  3. Excessive complexity — deep nesting (>3 levels), nested ternaries, complex boolean expressions (combine 4+ predicates), long parameter lists (>5 args without a struct/dataclass/object), god functions (>50 lines doing many things), overly clever one-liners that sacrifice readability, if/elif/else chains for type/enum/literal discrimination (must be match/case + assert_never), object used as a type annotation (must be Protocol, TypeVar, or explicit union).

    • KEEP: established complexity patterns in this codebase, performance-critical hot paths that intentionally use a complex idiom. if/else for boolean conditions and range checks (not variant discrimination).
    • REFACTOR: nested if-chains → guard clauses / early returns. Complex ternaries → explicit if/else. isinstance/enum if/elif chains → match/case with assert_never on the wildcard. object annotations → Protocol (structural), TypeVar (generic), or union (known variants).

Structural

  1. Needless abstraction — pass-through wrappers, single-use helpers, speculative indirection ("we might need this later"), interfaces with one implementer where the interface adds no testability win, factory functions that just call a constructor.

    • KEEP: abstractions that provide a real seam (testability, multiple implementers, framework-required boundaries).
  2. Boundary violations — wrong-layer imports (UI importing DB driver), leaky responsibilities (handler doing business logic that belongs in a service), hidden coupling (module A reads module B's private state), side effects in pure-named functions.

    • KEEP: pragmatic short-circuits already established as a pattern in this codebase. Flag for human judgment if unsure.
  3. Dead code — unused imports, unused private functions/methods, unreachable branches, stale feature flags, debug leftovers (console.log, print(...), dbg!), removed-but-still-referenced code.

…

FAQ

What are 'AI slops'?

These are code patterns that AIs sometimes generate and which are not ideal, such as obvious comments, overly defensive code, or unnecessary complexity.

Can this break my code?

No, the tool is designed to be safe. It only removes code after confirming with tests that the program's behavior has not changed.

Does it remove useful comments?

No, it keeps comments that explain the 'why' of something, ticket links, or explanations of complex algorithms. It only removes comments that merely restate the code.

Details

Original description by the author

Removes AI-generated code smells from branch changes or an explicit file list behind regression tests. Use when the user asks to clean up, deslop, or remove AI-slop patterns from recent changes.

repo: code-yeongyu/oh-my-openagent

Popularity

GitHub stars
70k
Ranking position
#1743
License
NOASSERTION
Updated
10/16/2026

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