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-frontend-refactorgit 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-frontend-refactor)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-frontend-refactor"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-frontend-refactor/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-frontend-refactor"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-frontend-refactor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 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 99 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 99 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 99 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
- medium Rogue Agent · line 51 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00093 | $0.01828 |
| Opus 5.5 | $0.00037 | $0.00731 |
| Sonnet 5.5 | $0.00019 | $0.00366 |
| Haiku 4.5 | $0.00009 | $0.00183 |
Grade A, and why
omh-frontend-refactor 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 3d ago.
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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontend Refactor
This is an OMH frontend-refactor workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
frontend-refactor exists so UI restructuring runs as a previewed, behavior-locked, impact-ordered process instead of ad-hoc rewrites: the plan comes before any edit, state fixes come before polish, and every change carries its safety reason.
Do Not Use When
- The target is not UI code, or the smell is generic slop, duplication, or dead code outside a component tree; use
omh-ai-slop-cleaner. - The user wants new UI built or redesigned rather than restructured; use
omh-frontend. - The user wants findings and a verdict without changing the code; use
omh-code-review. - The restructuring crosses module boundaries or changes architecture beyond the component tree; use
omh-refactor-planfor the phased execution shape, orulw-planfirst when the direction itself is still contested.
Examples
Good example:
- Prompt: This dashboard component is 800 lines and has six useState booleans - refactor it without changing behavior.
- Expected behavior: Preview first: characterization-test gate, then a plan that folds the booleans into one state union, extracts along change reasons found by the scroll test, and lists per-change line refs with safety reasons; apply only as the explicit second step.
- Why: Oversized component plus flag-cluster state is exactly the impact-ordered, behavior-locked restructuring this workflow owns.
Bad example:
- Prompt: Refactor and also add the dark-mode feature while you are in there.
- Expected behavior: Split the request: the behavior-preserving refactor runs under this workflow, and the dark-mode feature is new
frontendwork planned separately. - Why: A refactor that changes behavior cannot claim behavior preservation; mixing the two hides the feature from review.
Completion Checklist
- The preview plan was emitted before any file changed, and the apply step was an explicit second decision.
- Behavior evidence exists on both sides of apply, and unsafe-in-isolation changes are listed as notes, not half-applied.
- Pass order was impact-first and each finding names its category and safety reason.
- Out-of-scope smells were routed: generic slop to
ai-slop-cleaner, new UI tofrontend, verdict-only review tocode-review.
What ships with it
2 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.
- 3d ago Changed · +1 lines a4e554d63770
- 12d ago Changed · +1 lines db9c2623d45a
- 25d ago Changed · +3 lines 347d22b3dd7d
- 1mo ago Changed · +1 tokens per session e79447852ceb
- 1mo ago Changed · +6 lines c72e827d0335
- 1mo ago Changed · -24 lines scan B → A dcdc20d6d0d3
- 1mo ago First seen · 141 lines · 92 tokens per session scan B 52cb4df97a25
omh-frontend-refactor is a skill published in the GitHub repository rlaope/oh-my-hermes (4,206 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 1,828 once invoked, about $0.0004 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 2026-09-03.
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