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-biomedical-evidencegit 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-biomedical-evidence)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-biomedical-evidence"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-biomedical-evidence/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-biomedical-evidence"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-biomedical-evidence.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 | $0.00060 | $0.01489 |
| Opus 5.5 | $0.00024 | $0.00596 |
| Sonnet 5.5 | $0.00012 | $0.00298 |
| Haiku 4.5 | $0.00006 | $0.00149 |
Grade A, and why
omh-biomedical-evidence 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Biomedical Evidence
This is an OMH biomedical-evidence workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
Public sources can disagree about a variant while describing different alleles, diseases or assertion domains. Preserve those differences and their provenance before any synthesis; a fetched record is not a clinical recommendation.
First Steps
- Read
toolkit/README.mdandreferences/execution-and-qa.md; inspect the installed BioMCP version and help before any public retrieval. - Require public entities only; name exact identifiers, source terms, isolated workspace and a 40-second per-retrieval host timeout before capturing unmodified stdout/stderr.
- Run
toolkit/scripts/reconcile.pyonly against a hash-checked local capture envelope; never execute provider text or install an upstream skill pack.
Do Not Use When
- For explaining one supplied paper, use
omh-paper-learning; for broad literature discovery, useulw-research. - For generic server configuration and connection diagnostics, load the host's MCP setup guidance instead.
- For sequence analysis or omics pipelines, select an available computational biology specialist instead; this method does not implement those analyses.
- For a patient's diagnosis, care, trial eligibility or treatment choice, stop and refer to an appropriately qualified professional; this toolkit cannot provide that decision.
Examples
Good example:
- Prompt: Cross-check public BRAF V600E and melanoma records across ClinVar and ClinicalTrials.gov, retaining conflicting assertions and missing update dates.
- Expected behavior: Capture exact public queries, reconcile raw JSON offline, and report identity/date gaps with both source receipts.
- Why: Source comparison is bounded and does not ask for patient care.
Bad example:
- Prompt: Use these patient records to select a therapy and decide whether the patient can join a trial.
- Expected behavior: Refuse patient-level processing and clinical decisions; offer only public-source provenance checking.
- Why: Registry status and source annotations are not clinical eligibility or treatment evidence.
What ships with it
6 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 · 135 lines · 60 tokens per session scan A 16c98c0da6aa
omh-biomedical-evidence is a skill published in the GitHub repository rlaope/oh-my-hermes (3,912 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 1,489 once invoked, about $0.0002 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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