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-rules-distillgit 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-rules-distill)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-rules-distill"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-rules-distill/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-rules-distill"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-rules-distill.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 | $0.00077 | $0.01266 |
| Opus 5 | $0.00039 | $0.00633 |
| Sonnet 5 | $0.00015 | $0.00253 |
| Haiku 4.5 | $0.00008 | $0.00127 |
Grade A, and why
omh-rules-distill 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 7d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rules Distill
This is a Hermes-native rules-distill workflow skill.
Why This Exists
rules-distill gives OMH a disciplined way to learn from large skill ecosystems like ECC without wholesale copying: extract principles, review them, then patch OMH only through explicit verified work.
Do Not Use When
- The user wants a single workflow route regression; use
workflow-learning. - The user wants durable factual project memory; use
wikior memory curation. - The user already approved a concrete code/doc change; use the implementation workflow.
Examples
Good example:
- Prompt: rules-distill 최근 실패 trace와 스킬들을 보고 OMH AGENTS에 넣을 만한 반복 원칙 후보만 뽑아줘.
- Expected behavior: Prepare principle_candidate_set/v1, duplication/conflict report, review queue, and approved patch handoff only after approval.
- Why: The request is meta-guidance learning and needs review before mutating rules.
Bad example:
- Prompt: rules-distill 한 번 본 실패를 바로 모든 스킬 규칙으로 써버려.
- Expected behavior: Keep it as a low-confidence candidate or regression case until repeated evidence and review approval exist.
- Why: Rule distillation should not turn one-off anecdotes into global behavior.
Completion Checklist
- The durable fact, source evidence, retrieval hint, and staleness risk are recorded.
- Uncertain or conflicting knowledge is marked as review-needed rather than permanent truth.
- Separate coding or docs tasks are extracted instead of buried in notes.
Recovery Notes
- If source evidence conflicts, route to memory or knowledge review before writing durable guidance.
- If the fact may be stale, record the staleness warning and next refresh action.
Workflow Lane
- Current lane: Automation and status (
achievements,workspace-audit,production-audit,automation-blueprint,github-event-ops,github-issue-intake,buzz,agent-board,+35 more) - schedules, status, health, and ops review. - If intent belongs to another lane, hand back to
oh-my-hermesor name the adjacent workflow. - Shared product, routing, compatibility, and evidence rules:
omh-routing/references/skill-common-rail.md.
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.
- 7d ago Changed e11f19f3a6c2
- 9d ago First seen · 127 lines · 77 tokens per session scan A 57037b89a4fa
omh-rules-distill is a skill published in the GitHub repository rlaope/oh-my-hermes (1,677 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 1,266 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 2026-09-03.
Other skills, from other repositories
story-long-analyze
A structured process for deeply analysing a long online novel, starting with its opening three chapters and continuing chapter by chapter.
story-review
A review process for finding problems in a novel’s structure, characters, wording, and world rules. It can use several reviewers or one reviewer when others are unavailable.
moxiangtongxiu-perspective
A Chinese-language creative-writing guide built around character-led stories, interwoven plotlines, memorable dialogue, ensemble casts, and emotional contrasts. It is presented as a perspective associated with the author Mo Xiang Tong Xiu.
tiancantudou-perspective
A creative-writing guide based on the storytelling patterns associated with Chinese web novelist Tiancan Tudou. It focuses on stories where an underestimated character grows stronger through challenges and moves into new settings.
tianya-gods-team
A decision-making system in which 20 fictional specialist viewpoints analyze one question in parallel before a coordinating AI combines them. It covers areas such as history, economics, relationships, technology, mysteries, and culture.
lijigang-skill
A Chinese-language approach to writing precise, highly structured prompts, sometimes using Lisp-like notation. It combines concise wording, philosophical questioning, and a process for defining roles, conditions, output formats, and revisions.