Omh Ai Slop Cleaner

Ai Slop Cleaner

This is an OMH ai-slop-cleaner workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

ai-slop-cleaner exists to keep maintenance work explicit, evidence-backed, and inside the Hermes/executor boundary instead of relying on ad hoc chat narration.

Do Not Use When

• The goal is new or changed behavior rather than removing existing code; a plain refactor, feature, or fix request belongs to ultrawork.
• The cleanup would change architecture or module boundaries and needs its execution shaped into phases first; use refactor-plan, or ralplan when the direction itself is still contested.
• The user wants existing code judged rather than changed; use code-review for a bug-first review and failure-signal-audit for swallowed failures.

Examples

Good example:

• Prompt: $ai-slop-cleaner remove duplicated router branches and lock behavior with regression tests before refactoring.
• Expected behavior: Plan cleanup, preserve behavior, delete or simplify code, and prove it with targeted tests.
• Why: The request is maintenance cleanup with regression risk.

Bad example:

• Prompt: ai-slop-cleaner: treat casual chat or unaccepted work as if this workflow already produced verified results.
• Expected behavior: Ask a clarification question or route to a narrower workflow instead of forcing ai-slop-cleaner.
• Why: The request lacks the required inputs or would overclaim work that Hermes did not observe.

Completion Checklist

• The selected coding or runtime owner is named before any implementation claim.
• Prepared handoff, dispatch, execution, verification, review, CI, and merge states are separated.
• The final status cites observed runtime evidence or keeps the work prepared_not_observed.
• When Hermes is the selected coding owner, use hermes_coding_harness/v1 to keep builder, verifier, reviewer, docs, and PR lanes separate.
• Report the current harness stage, owner, next action, and missing evidence without claiming PR creation, review, CI, merge-readiness, or merge until matching runtime observations exist.

Recovery Notes

• If the selected executor is unavailable, ask for Codex, Claude Code, Hermes, or another runtime before retrying.
• If dispatch or result evidence is missing, keep the handoff prepared_not_observed and expose the next observable action.

Use When

Use when the goal is removing existing low-quality, duplicated, or AI-generated code and the observable behavior must not change; lock behavior with tests before and after the edits.

Copy code
Strong routing signals: `ai-slop-cleaner`, `$ai-slop-cleaner`, `cleanup`, `deslop`, `refactor`, `risky`, `behavior-preserving refactor`, `risk analysis`, `refactor workflow`, `legacy refactor`, `리팩터링`, `리팩토링`, `위험 분석`, `변경 범위 제한`, `회귀 테스트`

Catalog Metadata

Category: maintenance Phase: cleanup Quality tier: regression-gated Reasoning demand: heavy

Quality bar:

• Lock current behavior with regression checks before non-trivial cleanup.
• Classify before deleting: every finding names one category from the slop taxonomy - duplication, dead code, needless abstraction, boundary violation, missing tests, or templated defaults - so the pass order below can own it.
• Run single-smell passes in fixed order, re-verifying between passes and never bundling categories: dead-code deletion, then duplicate removal, then naming and error handling, then test reinforcement; the full contract is omh-ai-slop-cleaner/references/cleanup-passes.md.
• When the user names no target smell, run detection first and hand back the inventory: prepared linter and dead-code commands are named per stack in the reference and stay prepared_not_observed until run.
• Prefer deletion, reuse, and boundary repair over new abstractions.
• Rerun verification after cleanup before claiming behavior is preserved, and close with the four-part report: changed files, simplifications, behavior lock, remaining risks.

Required inputs:

• target smell, or a scoped file list when the user has not named one
• current behavior
• regression checks

Expected outputs:

• smell inventory naming each finding's category before any edit
• small cleanup diff, one pass at a time
• before/after verification
• closing report: changed files, simplifications, behavior lock, remaining risks

Artifact expectations:

• cleanup plan and regression evidence for non-trivial work

Safety rules:

• Lock behavior with tests before risky cleanup.
• Prefer deletion and existing utilities over new layers.
• Do not add dependencies for cleanup unless explicitly requested.
• A scoped file list is a boundary: never widen it silently; out-of-scope findings are reported, not edited.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Report actual tool results or not_observed / not_available; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.