[omh] Hermes AI slop cleaner workflow: delete AI-generated slop, dead code, and duplication while observable behavior stays identical.
SkillScore77Maintained
Automatic
◆Coding pack
Last verified 8/18/2026
What it changes
Passed 14 of 18 test cases with the skill, and 5 of 18 without it.
Without skill27%
With skill77%
Checks it passed that the model alone missed
Specifies coding-handling as the preferred harness
Specifies ~/.claude/settings.json as the user-scope configuration path
Reports current harness stage, owner, next action, and missing evidence
and 9 more in the full report
12 fixed5 passed either way4 still failing1 broke
Pass-rate lift
+50 pp
Tokens
+80%
Agent turns
0%
Response time
-42%
Model gemini-3.6-flashMeasured 8/18/2026v317 of 22 cases passed with skill · 18 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 11
Prompt
If configuring Claude Code permissions permanently at the user scope on a developer workstation, state the exact filesystem path where configuration rules belong.
What was checked, without and with the skill
Checked
Without skill
With skill
Specifies ~/.claude/settings.json as the user-scope configuration path
Missed
Passed
case-16 · gemini-3.6-flash · 8/18/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md122 linessingle file
Ai Slop Cleaner
This is a Hermes-native ai-slop-cleaner workflow skill.
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, module boundaries, or carry regression risk that needs a reviewed plan first; use ralplan.
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.