AI Slop Cleaner

AI Slop Cleaner Skill

Reduce AI-generated slop with a regression-tests-first, smell-by-smell cleanup workflow that preserves behavior and raises signal quality.

When to Use

Use this skill when:

• A code path works but feels bloated, noisy, repetitive, or over-abstracted
• A user asks to “cleanup”, “refactor”, or “deslop” AI-generated output
• Follow-up implementation left duplicate code, dead code, weak boundaries, missing tests, or unnecessary wrapper layers
• You need a disciplined cleanup workflow without broad rewrites

GPT-5.4 Guidance Alignment

• Keep outputs concise and evidence-dense unless risk or the user requests more detail.
• Treat newer user instructions as local workflow updates without discarding earlier non-conflicting constraints.
• Keep using inspection, tests, diagnostics, and verification until the cleanup is grounded.
• Proceed automatically through clear, reversible cleanup steps; ask only when a choice materially changes scope or behavior.

Procedure

1. Lock behavior with regression tests first

   • Identify the behavior that must not change
   • Add or run targeted regression tests before editing cleanup candidates
   • If behavior is currently untested, create the narrowest test coverage needed first

2. Create a cleanup plan before code

   • List the specific smells to remove
   • Bound the pass to the requested files/scope
   • Order fixes from safest/highest-signal to riskiest
   • Do not start coding until the cleanup plan is explicit

3. Categorize issues 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, leaky responsibilities, wrong-layer imports or side effects
   • Missing tests — behavior not locked, weak regression coverage, gaps around edge cases

4. Execute passes one smell at a time

   • Pass 1: Dead code deletion
   • Pass 2: Duplicate removal
   • Pass 3: Naming/error handling cleanup
   • Pass 4: Test reinforcement
   • Re-run targeted verification after each pass
   • Avoid bundling unrelated refactors into the same edit set

5. Run quality gates

   • Regression tests stay green
   • Lint passes
   • Typecheck passes
   • Relevant unit/integration tests pass
   • Static/security scan passes when available
   • Diff stays minimal and scoped
   • No new abstractions or dependencies unless explicitly required

6. Finish with an evidence-dense report

   • Changed files
   • Simplifications made
   • Tests/diagnostics/build checks run
   • Remaining risks
   • Residual follow-ups or consciously deferred cleanup

Output Format

AI SLOP CLEANUP REPORT
======================

Scope: [files or feature area]
Behavior Lock: [targeted regression tests added/run]
Cleanup Plan: [bounded smells and order]

Passes Completed:
1. Pass 1: Dead code deletion - [concise fix]
2. Pass 2: Duplicate removal - [concise fix]
3. Pass 3: Naming/error handling cleanup - [concise fix]
4. Pass 4: Test reinforcement - [concise fix]

Quality Gates:
- Regression tests: PASS/FAIL
- Lint: PASS/FAIL
- Typecheck: PASS/FAIL
- Tests: PASS/FAIL
- Static/security scan: PASS/FAIL or N/A

Changed Files:
- [path] - [simplification]

Remaining Risks:
- [none or short deferred item]

Scenario Examples

Good: The user says continue after tests already lock behavior and the next smell pass is clear. Continue with the next bounded cleanup pass.

Good: The user narrows the scope to a specific file after planning. Keep the regression-tests-first workflow, but apply the new scope locally.

Bad: Start rewriting architecture before protecting behavior with tests.

Bad: Collapse multiple smell categories into one large refactor with no intermediate verification.