ai-slop-cleaner

name	ai-slop-cleaner
description	AI-generated code cleanup — deletion-first approach, regression-safe. Removes unnecessary abstractions, verbose comments, and over-engineering.
version	1.0.0
triggers	[{"pattern":"user asks to clean up AI-generated code, remove slop, simplify over-engineered code, or audit for unnecessary abstractions"}]
category	review

AI Slop Cleaner

Adapted from oh-my-claudecode.

Purpose

AI-generated code accumulates "slop" — unnecessary abstractions, verbose comments, defensive code for impossible scenarios, and premature generalizations. This skill systematically identifies and removes it.

Slop Categories

1. Unnecessary Abstractions

• Single-use helper functions that obscure the flow
• Abstract base classes with one implementation
• Strategy/factory patterns for 2 concrete cases
• Wrapper classes that just delegate to the wrapped object

2. Verbose Comments

• Comments restating the code (# increment counter above counter += 1)
• JSDoc/docstrings on obvious functions
• "TODO" comments that will never be done
• Commented-out code blocks

3. Defensive Over-Engineering

• Try/except catching impossible exceptions
• Null checks on values that can't be null
• Feature flags for features that shipped months ago
• Backward-compatibility shims for removed code

4. Premature Generalization

• Config options nobody uses
• Plugin systems with one plugin
• Generic type parameters used once
• "Utils" modules with unrelated functions

Process

Step 1: Audit (Read-Only)

Scan the codebase for slop patterns. Report findings:

## Slop Audit Results
- 12 single-use helper functions
- 8 obvious comments
- 3 unnecessary try/except blocks
- 2 abstract classes with 1 implementation

Estimated lines removable: ~200 (15% of codebase)

Step 2: Prioritize

Rank by impact:

1. High: Abstractions that make code harder to understand
2. Medium: Verbose comments and dead code
3. Low: Minor style issues

Step 3: Delete (with safety net)

For each deletion:

1. Identify all callers/references
2. Inline or remove the abstraction
3. Run tests to verify no regression
4. Commit atomically (one logical change per commit)

Rule: Delete first, add only if tests fail.

Step 4: Verify

# Run full test suite after each batch
uv run pytest tests/ --cov=<src> --cov-fail-under=80

# Check nothing was accidentally removed
git diff --stat

Anti-Patterns to Preserve

NOT slop — keep these:

• Error handling at system boundaries (API inputs, file I/O)
• Type hints on public interfaces
• Abstractions with 3+ implementations
• Comments explaining non-obvious business logic ("why", not "what")
• Configuration for deployment-varying values

Integration

• Use after /review identifies code quality issues
• Complements /simplify (which focuses on recently changed code)
• Run before /ship to reduce PR diff noise