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-jit-learngit 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-jit-learn)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-jit-learn"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-jit-learn/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-jit-learn"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-jit-learn.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 | $0.00078 | $0.02472 |
| Opus 5 | $0.00039 | $0.01236 |
| Sonnet 5 | $0.00016 | $0.00494 |
| Haiku 4.5 | $0.00008 | $0.00247 |
Grade A, and why
omh-jit-learn 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 2d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jit Learn
This is a Hermes-native jit-learn workflow skill.
Why This Exists
jit-learn exists to choose what is worth learning for the user's present problem and convert credible sources into an immediate application path, instead of returning a generic self-help shelf or a popularity list.
Do Not Use When
- The user asks OMH to learn from workflow outcomes, missed routes, or evaluation traces; use
workflow-learning. - The learning goal is already chosen and the user wants a multi-week syllabus, instructional sequence, or assessment plan; use
curriculum-design. - The user supplied a paper, PDF, arXiv entry, or excerpt and wants it explained; use
paper-learning. - The requested output is a typed source candidate inventory or acquisition status rather than a fitted learning brief; use
source-finder. - The research question and target are already scoped and the user wants current facts, citations, or source synthesis rather than choosing what to learn; use
research.
Examples
Good example:
- Prompt: What should I learn next to solve my current onboarding blocker? Recommend books, podcasts, creators, and courses I can apply this week.
- Expected behavior: Ask one confirmation question, confirm the immediate target, then prepare a source-backed four-section learning brief ranked by fit and time-to-first-value.
- Why: The user needs target selection and immediate transfer, not a generic curriculum or popularity-ranked resource list.
Bad example:
- Prompt: Design a six-week Python syllabus with weekly assessments.
- Expected behavior: Route to
curriculum-designbecause the target is already chosen and the requested output is a sequenced curriculum. - Why: Just-in-time target selection should not displace an explicit curriculum-design request.
Completion Checklist
- At least one confirmation question was answered, no turn contained more than one question, and the shared interview ceiling was respected.
- Urgency/trigger, current level, application window, and the target statement are explicit before research.
- Every admitted recommendation is source-gated and popularity signals did not influence admission or rank.
- Books, Podcasts, Creators, and Courses are present with complete fields or an honest empty-section reason.
- Competing targets, filtered-out defaults, unresolved gaps, and one starting action are visible.
- The final status says the brief is prepared and does not claim consumption, learning, application, progress, or blocker resolution.
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.
- 2d ago Changed 7a20844439c7
- 6d ago Changed 9c8e571ebbc6
- 7d ago Changed bd8dfb482960
- 11d ago First seen · 177 lines · 78 tokens per session scan A 55198bca0e80
omh-jit-learn is a skill published in the GitHub repository rlaope/oh-my-hermes (1,648 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 2,472 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
qiuzhi2046-skill
A Chinese-language, hands-on approach to teaching and reviewing AI tools, starting with a user problem and ending with a practical solution. It combines beginner-to-advanced tutorials, tool testing, short and long content, and organized knowledge bases.
eq-emotional-intelligence
An emotional-intelligence coaching aid based on models that describe recognizing, understanding, and managing emotions. It covers self-awareness, self-management, awareness of others, and relationship skills.
sel-social-emotional-learning
A social and emotional learning guide for schools and families. Social and emotional learning means teaching people to understand feelings, manage behaviour, relate to others, and make responsible decisions.
interview-system-designer
Design hiring interviews, competency matrices, question banks, scoring rubrics, and interviewer calibration for a specified role.
github-contributor
Find suitable open-source contribution opportunities and prepare focused GitHub pull requests with project-specific validation and review context.
教程类H5-小红书小工具
A method for building offline, single-page course experiences for Xiaohongshu’s Mini Tools, which are interactive pages published inside the platform. It combines written lessons, generated illustrations, and JavaScript page switching in a constrained container.