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.
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add code-yeongyu/oh-my-openagent --skill remove-ai-slops -a cursor -y
Download packages/shared-skills/skills/remove-ai-slops/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does remove-ai-slops live?
The publisher keeps it at https://github.com/code-yeongyu/oh-my-openagent.
Which agents can use remove-ai-slops?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
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.
name: remove-ai-slops
description: "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."
argument-hint: "[file ...]"
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.
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.
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
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).
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.
Dead code — unused imports, unused private functions/methods, unreachable branches, stale feature flags, debug leftovers (console.log, print(...), dbg!), removed-but-still-referenced code.
KEEP: code referenced via reflection, dynamic dispatch, or string lookup. Code intentionally kept as a feature flag rollback path (verify with the user).
Hidden cost
Duplication — copy-pasted branches with trivial differences, redundant helpers that do the same thing in two places, repeated literal/magic-number sequences.
KEEP: incidental duplication (two pieces of code that look similar but serve different intents that could diverge). Prefer leaving them separate over forcing a premature shared abstraction.
Performance equivalences (behavior-preserving optimizations) — changes that are provably equivalent in semantics but cheaper in time/space:
O(n²) → O(n) when correctness preserved (e.g., set lookup vs list scan)
Repeated computation inside a loop → hoist outside
Unnecessary intermediate collections (eager list(...) when only iterated once → generator)
String concatenation in loop → join
Redundant DB/API calls in a loop → batch
Redundant deep copies / clones
.length / len() recomputed inside loop → cache
Hard rule: only apply when behavior equivalence is obvious. Do NOT change algorithms with subtle correctness implications. Do NOT micro-optimize hot paths without a benchmark. If in doubt, SKIP.
Behavior coverage
Missing tests — behavior present in changed files that is not locked by any regression test. The fix is not to remove code but to ADD the narrowest test that pins the behavior. EXCEPTION: a PROSE file (prompt, SKILL.md, rule, markdown) has no behavioral seam — do NOT add a text/word-count/phrase pin for it; that guards a diff, not behavior. Cover only a machine-consumed value (parsed field, sentinel a runtime greps, a doc JSON sample through its real validator) or leave it to review.
Structural
Oversized modules — any source file exceeding 250 pure LOC (non-blank, non-comment lines). This is an architectural defect, not a style preference. Measure: awk '!/^[[:space:]]*$/ && !/^[[:space:]]*(#|\/\/)/' <file> | wc -l.
When found, do NOT just flag it. Execute a full modular refactoring:
Run check-no-excuse-rules.py recursively on scope to list all violations.
For each oversized file, identify distinct responsibilities (single-responsibility principle).
Plan the split: name each new file after the concept it owns (never utils.py, helpers.py, common.py, part_1.py).
Present the split plan to the user before executing.
Extract into clean modules with explicit __init__.py re-exports (re-exports ONLY, no logic in __init__.py).
Verify: run check-no-excuse-rules.py again — every file must be ≤250 pure LOC. Run tests, typecheck, lint.
Forbidden escapes:
Counting blanks/comments toward budget.
Splitting by token count (foo_1.py, foo_2.py) — split by what each file DOES.
"It's generated" — only valid if the file lives in a build output directory.
"230 LOC, close enough" — a 230-LOC file about to grow is already over. Split now.
KEEP: genuinely self-contained single-responsibility scripts (e.g., a standalone CLI checker). Opt out with # noqa: SIZE_OK in first 5 lines and a comment explaining why.
Quality Gates
A pass is complete only when all applicable gates are green. Skip gates that are genuinely N/A for the project (e.g., no security scanner configured), and report N/A explicitly — do not silently skip.
Gate
Tool
Pass condition
Regression tests
project's test runner
all green
Lint
project's linter
zero errors (warnings OK if pre-existing)
Typecheck
lsp_diagnostics on changed files + project type-checker
zero new errors
Unit/integration tests
project's test runner
all green (pre-existing failures noted, not introduced)
Static/security scan
project's scanner
zero new findings, or N/A if not configured
Process
Phase 0: Plan with TodoWrite
Create todos for all phases below. Mark in_progress one at a time.
Phase 1: Determine scope
If file paths were passed as arguments, that is the scope. Otherwise:
git diff $(git merge-base main HEAD)..HEAD --name-only
Filter out: deleted files, binary files, generated/vendored files (node_modules/, dist/, target/, lockfiles). List the final scope.
Phase 2: Lock behavior with regression tests (NEW — non-negotiable)
For each in-scope source file:
Identify the public/observable behavior the file exposes (exported functions, HTTP handlers, CLI commands, classes used elsewhere).
Check whether existing tests cover that behavior. Use git grep / project test conventions to find related test files.
If behavior is uncovered or weakly covered, write the narrowest regression test that pins current behavior BEFORE editing the file. Tests should pin observable outputs, not implementation details. A PROSE file (prompt/SKILL.md/rule/markdown) is exempt — its wording is not behavior; skip the test and rely on review, or assert only a machine-consumed value.
Run the test suite (or at minimum the relevant tests). They must be green before any cleanup begins.
If you cannot establish a green baseline (e.g., test runner is broken), STOP and report. Do not proceed with cleanup on unverified ground.
Phase 3: Cleanup plan — existence first, then smells
The largest, safest deletion is code that should not have existed. Before categorizing smells, run the deletion ladder on each changed unit:
Delete entirely — the behavior is not needed (YAGNI, speculative, dead on arrival).
Reuse — an existing helper or pattern in this repo already does it; replace the reimplementation with a call to it.
Platform / stdlib / native / dependency — the language stdlib, the runtime, or an already-installed dependency already does it (a hand-rolled date picker → <input type="date">, a custom query parser → URLSearchParams, a bespoke debounce → the util already imported).
Simplify in place — it must exist; make it smaller.
Only code that lands on Simplify in place proceeds to the smell categories. This turns the pass from "find smells to trim" into "first decide whether the code should exist, then trim what survives." One function replaced by a platform call is a bigger, safer win than any in-place cleanup — and it needs no per-line smell analysis.
For a diff that fixes a bug, grep the callers of every shared function it touches. Prefer one root-cause fix at the shared seam over repeated guards at each caller — a per-caller patch that leaves a sibling caller broken is a partial fix, not a cleanup.
Then produce an explicit plan before spawning the removal agents:
File: src/foo.py
Ladder: 2 units simplify-in-place; 1 unit delete (native <input> replaces custom picker)
Categories: dead code, excessive complexity, performance
Order: dead code → complexity → performance
Risk: medium (touches caching layer)
File: src/bar.py
Ladder: all simplify-in-place
Categories: obvious comments, over-defensive
Order: comments → defensive
Risk: low
Intentional shortcuts: if the plan deliberately keeps a bounded simplification (a naive scan fine under N rows, a global lock, an O(n²) path), mark it in-code with a debt: comment naming the ceiling and the upgrade trigger (in omo, prefix with // @allow so the comment-checker treats it as intentional), and list it under "Remaining Risks / Deferred" in the report. That section is the debt ledger — a simplification with a known ceiling and no marker is indistinguishable from a bug.
Order rule (safest → riskiest): comments → dead code → defensive → duplication → complexity → abstraction/boundary → performance → tests → oversized-modules. This minimizes blast radius of any one change.
Phase 4: Parallel slop removal via deep-low agents in batches of 5
Files are processed by deep-low category agents with the $omo:remove-ai-slops skill loaded, batched 5 at a time in parallel. The executable skill name is remove-ai-slops. The deep-low category gives the agent enough thoroughness to correctly evaluate the 9 categories and respect the KEEP rules without slipping into surface fixes; the 5-wide batch is the sweet spot — more than 5 creates result-merging noise and context contention, fewer wastes parallelism.
Batching protocol (strict):
Slice the in-scope file list into chunks of up to 5 files.
For each chunk, launch all task calls in a single message, every one with run_in_background=true.
End your turn. Wait for the system to send <system-reminder> notifications as each task finishes.
Once all 5 in the batch complete, collect each result via background_output(task_id=...).
Launch the next batch of 5. Repeat until every file is processed.
If total files ≤ 5, launch all in one batch.
Never launch all files at once when there are more than 5; never launch them serially when more than one remains in the current batch.
Per-file invocation (one of the 5 in a batch):
task(
category="deep-low",
load_skills=["remove-ai-slops"],
run_in_background=true,
description="Slop removal: {filename}",
prompt="""
Remove AI slops from: {file_path}
First run the deletion ladder from Phase 3 on this file (delete entirely / reuse existing repo code / platform-stdlib-native / simplify in place); only code that must exist proceeds to smell removal.
Then evaluate EVERY category defined in this skill's "Categories (what counts as slop)" section, applying that section's KEEP and REFACTOR rules verbatim — the Categories section you have loaded is canonical, do not work from a restated subset.
Apply changes in this order (safest → riskiest): comments → dead code → defensive → duplication → complexity → abstraction/boundary → performance → oversized-modules.
Hard constraints:
- Behavior MUST be preserved. When equivalence is not obvious, SKIP.
- Do NOT change public API signatures.
- Do NOT remove type hints.
- Do NOT introduce new abstractions or dependencies.
- Diff stays minimal and scoped to slop removal.
Report changes grouped by category. For each change, give before/after, why-slop, why-safe.
For each skipped issue, give reason.
"""
)
Batch failure handling: a multi_agent_v1.wait_agent timeout only means no new mailbox update arrived, not that a deep-low agent failed. For long passes, require each child to send WORKING: <file> - <current phase> and BLOCKED: <reason> only when it cannot progress. Treat a running child as alive. Mark a file for retry only when the child is completed without the deliverable, ack-only after followup, explicitly BLOCKED:, or no longer running. Do NOT block the remaining 4 in that batch; collect successful results and retry the failed file once later. If retry also fails, escalate that file under "Issues Found & Fixed" in the final report.
Phase 5: Verify with quality gates + critical review
Run the five quality gates listed above. Then walk the critical review checklist:
Safety:
No functional logic accidentally removed
All error handling preserved (especially around I/O, network, external APIs)
Type hints intact and correct
Imports still valid
No breaking changes to public APIs
Behavior:
Return values unchanged (verified by Phase 2 regression tests)
Side effects unchanged
Exception behavior unchanged
Edge case handling preserved
Quality:
Removed changes are genuinely slop, not intentional patterns
Remaining code follows project conventions
No orphaned code or dead references
Performance changes are obviously equivalent (no subtle algorithm shifts)
No new abstractions introduced
Phase 6: Fix issues
If any gate fails or any checklist item flips:
Identify the specific change that caused the failure.
Explain why it broke things.
git checkout the affected file (or use git diff + targeted Edit to revert just the problematic hunk).
If genuine slop remains after revert, edit the file directly yourself — in parallel per file via multiple Edit calls — applying only the changes you can prove are safe.
Re-run the failing gate and re-walk the checklist for the affected file.
Repeat until all gates green AND checklist clean.
If you fail three times on the same file, STOP and escalate to the user with: the file, what you tried, what failed, your hypothesis. Do not keep editing.
Output Format
AI SLOP REMOVAL REPORT
======================
Scope: [branch diff vs merge-base main / explicit file list]
Files: [N files]
- path/to/file1.ts
- path/to/file2.py
Behavior Lock:
- Existing coverage: [N files already covered]
- Tests added: [M new regression tests at path/to/test_X.py]
- Baseline status: GREEN
Cleanup Plan:
- path/to/file1.ts: [ladder: 1 delete (native) + simplify-in-place] → [dead code → complexity → performance]
- path/to/file2.py: [ladder: all simplify-in-place] → [comments → defensive]
Per-File Results (each cut shows what replaces it):
path/to/file1.ts
- Ladder/delete: custom DatePicker (48 lines) → <input type="date"> (native), flatpickr import removed
- Dead code: 3 removed (lines X-Y, A-B, C) → nothing (unreachable)
- Excessive complexity: 1 simplified (nested ternary at L42 → if/else)
- Performance: 1 (line N: list scan → set lookup, O(n²)→O(n), behavior identical)
- Skipped (preserved): 2 (defensive null check at boundary; commented WHY at L88)
path/to/file2.py
- Obvious comments: 5 removed → nothing
- Over-defensive: 1 simplified (redundant isinstance on typed param)
Quality Gates:
- Regression tests: PASS (12 tests, 0 failed)
- Lint: PASS
- Typecheck (lsp_diagnostics + project): PASS (0 new errors on changed files)
- Unit/integration tests: PASS (45 tests, 0 failed)
- Static/security scan: N/A (not configured)
Critical Review:
- Safety: PASS
- Behavior: PASS
- Quality: PASS
Issues Found & Fixed:
- [None] OR [Issue description → Fix applied]
Net Impact:
- LOC: -74 (removed 91, added 17)
- Dependencies: -1 (flatpickr removed; native <input type="date"> used)
- Files deleted: 1 (src/date-picker-wrapper.ts — platform-native replacement)
Remaining Risks / Deferred (this section is the debt ledger):
- [None] OR [e.g., "boundary violation in module X flagged but not refactored — needs human judgment"]
- `debt:` markers kept this pass: [None] OR [file:line — ceiling → upgrade trigger]
Final Status: CLEAN | ISSUES FIXED | REQUIRES ATTENTION
Anti-Patterns (do not do these)
Skipping Phase 2. Removing code on uncovered ground is a behavior-change time bomb regardless of how careful the agent is. The regression test IS the safety mechanism; the checklist is its complement, not its replacement.
Bundling unrelated refactors. A single "cleanup" commit with dead code deletion + abstraction removal + performance change is impossible to review and impossible to bisect. Stay scoped to slop.
Algorithm changes disguised as performance optimization. If equivalence requires a proof, it is not a slop fix — it is a refactor and belongs in a separate change.
Silent skips. If a quality gate is N/A, say N/A and why. If a check failed and you could not fix it, say so. Never claim PASS without evidence.
Removing comments that explain WHY. "It is obvious from the code" is rarely true for the next reader. Only remove comments that restate WHAT.
Touching files outside scope. If a file was not in the branch diff or explicit list, do not edit it, even if you notice slop in passing. Report it under "Remaining Risks".
Tool Persistence
When a tool call fails, retry with adjusted parameters.
Never silently skip a failed tool call.
Never claim a gate passed without running it and reading the output.
If correctness depends on further inspection, keep using lsp_diagnostics, the test runner, and direct file reads until the result is grounded.
Quality Assurance
NEVER remove code that serves a functional purpose.
ALWAYS verify changes compile/parse and pass type-check.
ALWAYS preserve test coverage; add tests rather than remove them.
If uncertain about a change, err on the side of keeping the original code.
The default action when in doubt is SKIP, not GUESS.
How do I install remove-ai-slops?
Download packages/shared-skills/skills/remove-ai-slops/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does remove-ai-slops live?
The publisher keeps it at https://github.com/code-yeongyu/oh-my-openagent.
Which agents can use remove-ai-slops?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.