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-molecular-dockinggit 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-molecular-docking)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-molecular-docking"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-molecular-docking/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-molecular-docking"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-molecular-docking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00065 | $0.01445 |
| Opus 5.5 | $0.00026 | $0.00578 |
| Sonnet 5.5 | $0.00013 | $0.00289 |
| Haiku 4.5 | $0.00006 | $0.00145 |
Grade A, and why
omh-molecular-docking 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecular Docking
This is an OMH molecular-docking workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
Input validation and tutorial numbers are not measured docking. Require a real engine search and digest-bound poses, finite energies and replay settings.
First Steps
- Read
toolkit/README.mdandreferences/execution-and-qa.md. Use the explicitly invoked standalone Vina Python helper, not an invented ToolUniverse tool or hidden core execution service. - Recover prepared file paths, source/license and preparation receipts, angstrom binding box, seed, CPU, new output directory and host deadline. Never guess a site, silently prepare a molecule or install dependencies on skill load.
- Use
--validate-onlyfor dependency-free prerequisites, or explicitly invoke actual docking in an approved environment with a host-enforced timeout and stdout/stderr capture. Missing dependencies must refuse, not produce guessed findings.
Do Not Use When
- A general literature question belongs to
ulw-research; one supplied paper belongs toomh-paper-learning. - A boat reservation, molecular mechanism explanation without computation, drug recommendation or clinical decision is outside this scope.
Examples
Good example:
- Prompt: Run these prepared PDBQT inputs in this declared box with one CPU and fixed seed, preserving actual poses and replay settings.
- Expected behavior: Validate inputs, run the explicit optional Vina helper under a host deadline, then read back poses/scores/hashes and state limitations.
- Why: Actual engine output is distinguishable from preflight and source inspection.
Bad example:
- Prompt: These scores prove the compound is safe and effective; recommend a dose.
- Expected behavior: Reject the unsupported inference and do not produce clinical advice.
- Why: Computational score evidence cannot establish those claims.
Completion Checklist
- Input/format/resource checks and exact input hashes are observed; source rights and preparation assumptions remain explicit.
- Actual poses, finite matching energies, backend version/settings, host logs/exit/deadline and replay manifest are read back, or the run is reported BLOCKED without invented results.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 129 lines · 65 tokens per session scan A 7b0480991c54
omh-molecular-docking is a skill published in the GitHub repository rlaope/oh-my-hermes (3,912 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 1,445 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 2026-10-23.
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