ai-slop-cleaner

name	ai-slop-cleaner
category	execution
status	active
description	Run a regression-tests-first, smell-by-smell deslop/refactor workflow that preserves behavior and raises signal quality. Use when the user asks to "cleanup", "refactor", "deslop", or when follow-up code has duplicates, dead code, weak boundaries, or fallback-like slop.
agent_created	true
triggers	["cleanup","refactor","deslop","ai slop","clean up this code"]

Ported from oh-my-codex ai-slop-cleaner. OMX runtime conventions ($macro invocation, omx CLI, .omx/ state directory) are replaced with WorkBuddy idioms (Skill tool, Agent tool, task list, .workbuddy/memory).

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, fallback-like code, or unnecessary wrapper layers
• You need a disciplined cleanup workflow without broad rewrites

Workflow 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.

Scoped File Lists

• This skill can accept a file list scope instead of a whole feature area.
• When the caller provides a changed-files list, keep the cleanup strictly bounded to those files.
• When invoked as a mandatory deslop pass after a coding run, run on the changed files only, in standard mode unless the caller requests otherwise.

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.
   • For fallback-like code, cover the primary path and any preserved compatibility/fail-safe fallback before cleanup.

2. Create a cleanup plan before code

   • List the specific smells to remove.
   • Bound the pass to the requested files/scope.
   • Include fallback findings, classifications, and escalation status in the plan.
   • Order fixes from safest/highest-signal to riskiest.
   • Do not start coding until the cleanup plan is explicit. Track the plan with the task list (TaskCreate/TaskUpdate).

3. Inventory fallback-like code before editing

   • Search the requested scope for fallback-like detection signals: quick hacks, temporary workaround, temporary fallback, just bypass, just skip, fallback if it fails, swallowed errors, silent defaults, broad compatibility shims, and duplicate alternate execution paths.
   • Classify each finding before changing it:
     • Masking fallback slop — hides errors or evidence, bypasses the primary contract, suppresses tests or validation, swallows failures, silently defaults, or adds untested alternate paths.
     • Grounded compatibility/fail-safe fallback — is scoped to an external/version/fail-safe boundary, documents the rationale, preserves failure evidence, and has regression tests for both the primary and fallback behavior.
   • Prefer root-cause repair, deletion, boundary repair, or explicit failure behavior before preserving fallback paths.
   • For broad, ambiguous, cross-layer, or architectural fallback-like code, invoke the ralplan skill (skill: ralplan) for consensus resolution before edits.
   • Recursion guard: when already inside ralplan, a coding run, team, or another workflow, do not spawn a nested ralplan; record the finding and attach it to the active plan handoff instead.

4. Categorize issues before editing

   • Fallback-like code — masking fallbacks, workaround branches, bypasses, swallowed errors, silent defaults, broad shims, alternate execution paths.
   • 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.
   • UI/design slop — review visual outputs as context-sensitive signals, not absolute bans; preserve intentional brand, design-system, accessibility, or product-context exceptions when the rationale is clear.
     • Korean body text that is too small: challenge 11–12px body copy; Korean body text generally needs 14px or larger unless a dense, accessible system explicitly supports smaller text.
     • Gratuitous depth: avoid putting box shadows on every logo, surface, card, icon, background, and step block when hierarchy or affordance does not need it.
     • Repetitive content scaffolding: trim repeated eyebrow + title + description + paragraph stacks, filler explanation text, and generic emoji badges that do not add meaning.
     • Default AI palettes: question blue/purple defaults such as #3B82F6 when there is no brand, semantic, or system rationale.
     • Over-perfect grids: avoid reflexive uniform 3-column or 4-column card grids when the product context would benefit from rhythm, asymmetry, carousel cuts, bento composition, or varied emphasis.
     • Extreme gradients: tone down "AI demo" gradients unless the brand or campaign intentionally calls for that intensity.
   • Missing tests — behavior not locked, weak regression coverage, gaps around edge cases.

5. Execute passes one smell at a time

   • Fallback-like code resolution gate — remove masking fallback slop, repair root causes, or escalate ambiguous cases before continuing.
   • 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.

6. 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.

7. Finish with an evidence-dense report (see Output Format below).

Output Format

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

Scope: [files or feature area]
Behavior Lock: [targeted regression tests added/run]
Cleanup Plan: [bounded smells and order]
Fallback Findings: [none, or finding -> masking fallback slop / grounded compatibility/fail-safe fallback -> escalation status]
UI/Design Findings: [none/N/A, or signal -> action taken/deferred -> intentional exception rationale]

Passes Completed:
- Fallback-like code resolution gate - [root-cause repair, explicit failure behavior, preserved grounded fallback, or ralplan handoff]
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]

Fallback Review:
- Findings: [fallback-like findings detected]
- Classification: [masking fallback slop | grounded fallback]
- Escalation Status: [none | raised to leader/ralplan | no escalation]

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.

Bad: Keep a fallback if it fails branch that silently defaults after a swallowed error instead of fixing the root cause or making failure explicit.

Good: A version-specific compatibility shim is narrow, documented, preserves error evidence, has primary and fallback regression tests, and is reported as a grounded compatibility/fail-safe fallback.