oh-my-hermes is an operating layer for Hermes Agent that organizes requests into workflows for planning, research, creation, coding handoffs, operations, and project memory. Hermes users run these workflows through the desktop app, CLI, or messenger app, while the catalogue add-ons extend its native capabilities.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rlaope/oh-my-hermes --skill omh-ai-slop-cleanergit clone --depth 1 https://github.com/rlaope/oh-my-hermesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-ai-slop-cleaner)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-ai-slop-cleaner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-ai-slop-cleaner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 98 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
- high Agent Snooping · line 98 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
- high Agent Snooping · line 98 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.02033 |
| Opus 5.5 | $0.00029 | $0.00813 |
| Sonnet 5.5 | $0.00014 | $0.00407 |
| Haiku 4.5 | $0.00007 | $0.00203 |
Grade A, and why
omh-ai-slop-cleaner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
ulw-work. - The cleanup would change architecture or module boundaries and needs its execution shaped into phases first; use
omh-refactor-plan, orulw-planwhen the direction itself is still contested. - The user wants code judged for bugs or merge readiness with no drafted slop removal to inspect; use
omh-code-reviewfor a bug-first review andomh-failure-signal-auditfor 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/v1to 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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed f241ab2baf40
- 5d ago Changed · +1 lines 49b8c6380996
- 14d ago Changed · +5 lines 864afd1d662f
- 27d ago Changed · +3 lines 51fa0d8d39f3
- 1mo ago Changed · +3 tokens per session fe8c6ee82fe3
- 1mo ago Changed · +6 lines c2be8f464b39
- 1mo ago Changed · +1 lines a87547c00d37
- 1mo ago Changed · -24 lines scan B → A 47448f964853
- 1mo ago Changed · +5 lines 48d55c038a83
- 1mo ago First seen · 135 lines · 69 tokens per session scan B 93daa6e0c766
omh-ai-slop-cleaner is a skill published in the GitHub repository rlaope/oh-my-hermes (4,366 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 2,033 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
nlpm-audit
Audit SKILL.md, AGENTS.md, prompts, hooks, and plugin manifests for instruction conflicts, quality, broken references, and manifest-to-disk drift.
api-design-reviewer
Use when reviewing API designs for consistency, usability, versioning, error semantics, security, backward compatibility, and developer experience before implementation or release.
pr-review-expert
Review GitHub PRs or GitLab MRs for correctness, security, compatibility, and affected test coverage, with actionable evidence tied to the diff.
improve-codebase-architecture
Review module boundaries, interface depth and high-leverage refactors with evidence.
gh-address-comments
Use when addressing GitHub PR review comments or issue comments on the current branch with gh CLI, including auth checks, comment triage, edits, verification, and replies.
doubt-driven-development
Review high-risk or uncertain engineering decisions adversarially when independent verification can expose costly assumptions before implementation.