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-award-bar-scoregit 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-award-bar-score)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-award-bar-score"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-award-bar-score/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-award-bar-score"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-award-bar-score.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 | $0.00067 | $0.01708 |
| Opus 5 | $0.00034 | $0.00854 |
| Sonnet 5 | $0.00013 | $0.00342 |
| Haiku 4.5 | $0.00007 | $0.00171 |
Grade A, and why
omh-award-bar-score 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 6d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Award Bar Score
This is a Hermes-native award-bar-score workflow skill.
Why This Exists
award-bar-score gives "make it award-winning" a measurable meaning: published axes, published weights, a published threshold, and the one axis holding the surface below it — instead of a taste argument nobody can settle.
Do Not Use When
- The request is broad premium quality across decks, PDFs, or posters; use
design-quality-gate. - The request is frontend implementation, layout, or design-system work; use
frontend. - The request is WCAG, keyboard, or screen-reader conformance; use
accessibility-audit. - The request is a rendered capture or a pixel verdict; use
visual-qa. - The award is a business, sales, or team award with no judged web surface.
Examples
Good example:
- Prompt: score our landing page against the css design awards bar and tell me what is holding it back
- Expected behavior: Prepare award_bar_score/v1 with per-axis UI/UX/innovation scores from rendered evidence, the weighted total against the 8.0 threshold, the binding constraint, and the accessibility/performance tradeoff ledger.
- Why: The request asks for a measured comparison against a published external bar, not a general polish pass.
Bad example:
- Prompt: award-bar-score confirm this site will win website of the day
- Expected behavior: Score the axes against the published model and refuse the outcome claim; a jury scores submissions and OMH does not.
- Why: A rubric self-assessment cannot predict a jury result.
Completion Checklist
- Each of UI, UX, and innovation carries its own score and the rendered evidence it was read from.
- The weighted total is computed from the stated weights and compared against the published threshold.
- The binding constraint names one axis and what moving it requires.
- Any innovation move that costs accessibility or performance budget is recorded as a tradeoff the user chooses.
- No award, jury, placement, or selection outcome is claimed.
What ships with it
1 file 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.
- 6d ago Changed ba22589420ea
- 9d ago First seen · 134 lines · 67 tokens per session scan A 86e92d05cd2b
omh-award-bar-score is a skill published in the GitHub repository rlaope/oh-my-hermes (1,677 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 1,708 once invoked, about $0.0003 per session on Opus 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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