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-research-briefgit 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-research-brief)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-research-brief"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-research-brief/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-research-brief"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-research-brief.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 | $0.00091 | $0.01201 |
| Opus 5 | $0.00046 | $0.00600 |
| Sonnet 5 | $0.00018 | $0.00240 |
| Haiku 4.5 | $0.00009 | $0.00120 |
Grade A, and why
omh-research-brief 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 4d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Brief
This is a Hermes-native research-brief workflow skill.
Why This Exists
research-brief exists to keep research work explicit, evidence-backed, and inside the Hermes/executor boundary instead of relying on ad hoc chat narration.
Do Not Use When
- The user needs to decide whether a customer problem deserves product investment with evidence typing and customer re-entry; use
product-discovery-validation. - The request is only fresh links, citations, or current facts without a business question or decision audience; use
research. - Sources have not yet been selected and the user wants source types, candidates, or acquisition state; use
source-finder.
Examples
Good example:
- Prompt: research-brief: compare three onboarding analytics vendors using customer notes and confidence gaps.
- Expected behavior: Prepare a source-backed brief with evidence, inference, confidence, and retrieval gaps separated.
- Why: The user needs business research synthesis, not recurring operations or coding.
Bad example:
- Prompt: research-brief: treat casual chat or unaccepted work as if this workflow already produced verified results.
- Expected behavior: Ask a clarification question or route to a narrower workflow instead of forcing
research-brief. - Why: The request lacks the required inputs or would overclaim work that Hermes did not observe.
Completion Checklist
- The research question, source boundaries, recency assumptions, and confidence level are named.
- Observed sources, inference, synthesis, and unresolved retrieval gaps are separated.
- Follow-up planning or handoff uses the research summary without calling it execution evidence.
Recovery Notes
- If sources cannot be accessed, state the retrieval gap and use only observed local context.
- If evidence is thin or one-sided, lower confidence and ask for a narrower source boundary.
Workflow Lane
- Current lane: Research and company ops (
product-docs,source-finder,web-research,research,best-practice-research,autoresearch-goal,model-optimization,inference-serving,+19 more) - research, signals, ops, and briefings. - 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.
- 4d ago Changed · +1 lines 1081e1042428
- 7d ago Changed 5e7f03fc787f
- 9d ago First seen · 121 lines · 91 tokens per session scan A 9f4f6e2a6b13
omh-research-brief is a skill published in the GitHub repository rlaope/oh-my-hermes (1,677 stars, last pushed today), licensed MIT. It adds 91 tokens to every session and 1,201 once invoked, about $0.0005 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.