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-voice-inputgit 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-voice-input)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-voice-input"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-voice-input/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-voice-input"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-voice-input.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00075 | $0.01263 |
| Opus 5.5 | $0.00030 | $0.00505 |
| Sonnet 5.5 | $0.00015 | $0.00253 |
| Haiku 4.5 | $0.00007 | $0.00126 |
Grade A, and why
omh-voice-input 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 9d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Voice Operator
This is an OMH voice-operator workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
voice-operator exists so Hermes users can ask for this workflow in chat and get a structured, checkable answer instead of an improvised one.
Do Not Use When
- The request is already handled by a narrower explicit skill with stronger evidence.
- The user asks OMH to secretly run external platforms, connectors, schedulers, file exports, or runtime agents.
- The only safe answer is to ask for missing authority, credentials, target, or observed evidence first.
Examples
Good example:
- Prompt: voice-operator 'release before lunch, check risky parts' from mobile.
- Expected behavior: Produce
prepare_voice_operator_cardwith required context, wrapper actions, and not-evidence boundaries. - Why: The prompt names a real workflow surface that Hermes can orchestrate without hiding execution.
Bad example:
- Prompt: voice-operator assume the user approved a destructive action from a vague voice note.
- Expected behavior: Report the missing observed evidence or authority instead of claiming the external step happened.
- Why: Prepared OMH guidance is not platform, runtime, connector, file, memory, or delivery evidence.
Completion Checklist
- The short-input or voice-like request is clarified enough to avoid accidental action.
- The next action is readable, reversible when possible, and confirmation-gated when risky.
- Delivery, notification, or platform behavior is not claimed without wrapper evidence.
Recovery Notes
- If transcript confidence or intent is weak, ask one short clarification before action.
- If platform delivery is unavailable, keep the response in chat and mark delivery not_observed.
When to Use
Use when Hermes receives terse voice/mobile-style requests and should produce concise clarification, plan, or status UX.
Strong routing signals: `voice-operator`, `voice operator`, `voice-first`, `voice command`, `mobile command`, `short command`, `dictated command`, `dictated request`, `spoken request`, `speech command`, `accessibility`, `hands free`, `hands-free`, `phone command`, `phone request`, `push command`, `음성`, `음성으로`, `음성 명령`, `모바일 명령`, `모바일 음성`, `핸드폰`, `폰으로`, `말로`, `말로 한 요청`, `접근성`, `짧은 명령`, `짧게 말한 요청`
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.
- 9d ago Changed · +1 lines a33db05e027b
- 18d ago Changed · +1 lines 458a8ccafa1f
- 1mo ago Changed · +3 lines d15fab885fb5
- 1mo ago Changed · +3 tokens per session 2ef55fec39d8
- 1mo ago Changed · +6 tokens per session 778b2d42adf9
- 1mo ago Changed · +6 lines c1c6124525be
- 1mo ago Changed · -11 lines a78951eaf35a
- 1mo ago Changed · +1 lines f7f71cfe1a65
- 1mo ago Changed eef374a22132
- 1mo ago First seen · 120 lines · 66 tokens per session scan A 41af8796930e
omh-voice-input is a skill published in the GitHub repository rlaope/oh-my-hermes (4,981 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 1,263 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 2026-09-03.
Other skills, from other repositories
todos
Use when the user asks for a shared plan, a task needs at least three substantive execution steps, or a loose TODO is pending. Without an explicit plan request, not for one-shot answers or checks, or one- to two-step work that is not already in the list.
lark-workflow-standup-report
A workflow that combines calendar events with unfinished Lark tasks to produce a daily or weekly work summary.
notion-knowledge-capture
Convert conversations and notes into structured, linkable Notion pages for easy reuse.
obsidian
Use when reading, searching, creating, or editing notes in an authorized Obsidian vault while preserving frontmatter, wikilinks, and unrelated notes.
docs-cleaner
Consolidate overlapping documentation and remove repetition while preserving useful content, references, and ownership.
lark-contact
A Feishu (Lark) contacts tool for finding users or bots and looking up their profiles and contact details.