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-paper-learninggit 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-paper-learning)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-paper-learning"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-paper-learning/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-paper-learning"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-paper-learning.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 | $0.00073 | $0.01930 |
| Opus 5.5 | $0.00029 | $0.00772 |
| Sonnet 5.5 | $0.00015 | $0.00386 |
| Haiku 4.5 | $0.00007 | $0.00193 |
Grade A, and why
omh-paper-learning 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 5d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Learning
This is an OMH paper-learning workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
paper-learning exists so Hermes can act like a strong human tutor for papers: choose the right explanation level, walk through the full paper section by section, and keep PDF extraction and validation evidence honest.
Do Not Use When
- The request asks to export, convert, render, or package a file; use
omh-materials-package. - The request asks for daily/weekly paper monitoring, digest, source inbox, or Scout/Analyst/Briefer operations.
- The request asks to find current papers or sources when no supplied paper exists; use
ulw-research. - The request asks for a visual/image card; use
omh-image-cards. - The request asks to implement or reproduce the paper's code; prepare a coding handoff only after a paper learning or reproduction plan is accepted.
Examples
Good example:
- Prompt: paper-learning 이 논문 PDF를 아주 쉽게 설명해줘. 내용은 줄이지 말고 섹션별로.
- Expected behavior: Prepare paper_learning_card/v1, ask or record level=very_easy, mark PDF extraction/source_state evidence, then explain section-by-section with a coverage ledger.
- Why: The user supplied a paper/PDF explanation intent with an explicit level and coverage-preserving constraint.
Bad example:
- Prompt: paper-learning 이 PDF를 PPT로 변환해서 공유용 파일 만들어줘.
- Expected behavior: Route to
materials-packagebecause the user wants file conversion/export, not conceptual paper explanation. - Why: PDF file output and render QA are material packaging work, not paper learning evidence.
Completion Checklist
- The selected explanation level is one of: very_easy, moderate, expert, choose.
- The source_state is recorded and scoped to observed text or extraction evidence.
- The coverage ledger lists observed, missing, or prepared sections before claiming completion.
- The explanation is section-aware and does not compress away claims, equations, figures, limitations, or reproducibility notes.
- Not-observed boundaries remain visible: full_pdf_extraction, figure_ocr, external_citation_check, math_proof_validation, code_or_benchmark_reproduction, peer_review_or_claim_correctness.
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.
- 5d ago Changed · +1 lines 3570ec1b8c8e
- 12d ago Changed 0cc3e2668946
- 14d ago Changed · +3 lines a53974bcc9b8
- 27d ago Changed · +3 lines f0db3970f7d6
- 1mo ago Changed · +2 tokens per session 5cd3c4d56e10
- 1mo ago Changed · +6 lines 248c2883d8f1
- 1mo ago Changed · +2 lines 06a38802ce70
- 1mo ago Changed · -11 lines 235f9801c747
- 1mo ago Changed 104900250fdd
- 1mo ago First seen · 134 lines · 71 tokens per session scan A 11254a7d341c
omh-paper-learning is a skill published in the GitHub repository rlaope/oh-my-hermes (4,366 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 1,930 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.
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