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-source-findergit 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-source-finder)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-source-finder"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-source-finder/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-source-finder"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-source-finder.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 | $0.00084 | $0.01592 |
| Opus 5 | $0.00042 | $0.00796 |
| Sonnet 5 | $0.00017 | $0.00318 |
| Haiku 4.5 | $0.00008 | $0.00159 |
Grade A, and why
omh-source-finder 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Source Finder
This is a Hermes-native source-finder workflow skill.
Why This Exists
source-finder exists so Hermes can turn vague source discovery requests into typed candidates, acquisition status, and downstream workflow choice without pretending OMH searched, downloaded, or verified the material.
Do Not Use When
- The requested output is factual findings, comparison, or a summary rather than a typed candidate inventory and acquisition status; use
research. - The user needs a business decision brief with evidence-versus-inference treatment; use
research-brief. - The user asks for current citations, fact-finding, or source-backed synthesis; use
research. - The user supplies a paper/PDF/arXiv/DOI/excerpt and wants explanation; use
paper-learning. - The user asks for recurring monitoring, source inbox, or Scout/Analyst/Briefer operations; use
research-department. - The user asks to export, convert, render, package, or attach a file; use
materials-packageordeliverable-package. - The user asks for an image card or visual summary; use
img-summary.
Examples
Good example:
- Prompt: source-finder find papers, datasets, and GitHub repos for evaluating browser agent benchmarks.
- Expected behavior: Prepare source_finder_plan/v1 with typed candidates, acquisition states, missing observed evidence, and downstream choices.
- Why: The user needs source candidates before deciding whether to learn, research, package, or implement.
Bad example:
- Prompt: source-finder find current citations and summarize what the sources say.
- Expected behavior: Route to
researchbecause the user asks for current evidence and synthesis, not candidate acquisition status. - Why: Source-finder prepares acquisition lifecycle metadata; research owns current evidence synthesis.
Completion Checklist
- Source kinds, source boundaries, and downstream intent are named.
- Each candidate has a source_candidate/v1 shape and acquisition state.
- Observed states include provenance before being treated as evidence.
- The next downstream workflow is recommended without claiming it ran.
- Search, download, clone, extraction, hash, license, verification, and downstream processing gaps are explicit.
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 4d50e695ccdf
- 7d ago Changed d1c4afd2a36d
- 9d ago First seen · 134 lines · 84 tokens per session scan A b3e677c05586
omh-source-finder is a skill published in the GitHub repository rlaope/oh-my-hermes (1,716 stars, last pushed today), licensed MIT. It adds 84 tokens to every session and 1,592 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.
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