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-agent-evaluationgit 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-agent-evaluation)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-agent-evaluation"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-agent-evaluation/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-agent-evaluation"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-agent-evaluation.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 | $0.00069 | $0.01498 |
| Opus 5 | $0.00034 | $0.00749 |
| Sonnet 5 | $0.00014 | $0.00300 |
| Haiku 4.5 | $0.00007 | $0.00150 |
Grade A, and why
omh-agent-evaluation 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 6d 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.
Agent Evaluation
This is a Hermes-native agent-evaluation workflow skill.
Why This Exists
agent-evaluation gives OMH a way to improve executor choice empirically, not by vibes, while preserving executor-neutral product language across Codex, Claude Code, Hermes, and generic runtimes.
Do Not Use When
- The user needs current runtime readiness only; use
executor-runtime-readiness. - The user already selected an executor and wants implementation; use the coding handoff or delivery workflow.
- The user asks for workflow learning from a single failed route; use
workflow-learning. - The ask is to find and fix runtime, memory, cost, or rendering hotspots rather than score executor or model output quality; use
ultraperf.
Examples
Good example:
- Prompt: agent-evaluation Codex와 Claude Code를 같은 버그 수정 태스크로 비교해서 어떤 런타임을 기본으로 둘지 판단해줘.
- Expected behavior: Prepare paired_run_decision/v1 requirements and a scenario-specific recommendation.
- Why: The request compares executor choices and needs fair evaluation boundaries.
Bad example:
- Prompt: agent-evaluation 실행 증거 없이 Codex가 항상 최고라고 결론내줘.
- Expected behavior: Reject universal ranking and require observed runs or mark the recommendation as ungrounded.
- Why: Agent evaluation must be reproducible and evidence-backed.
Completion Checklist
- Confirm the workflow target, evidence boundary, and stop condition are named.
- Report which outputs are prepared, observed, blocked, or missing.
- Name the smallest next verification or handoff instead of claiming completion from narration.
Recovery Notes
- If required context is missing, ask one blocking question or route back to the narrower workflow.
- If runtime or wrapper evidence is unavailable, keep the status as not_observed and expose the next observable 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 ships with it
1 file 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.
- 6d ago Changed df843b8e1884
- 7d ago Changed · +4 lines 192961d33997
- 11d ago First seen · 125 lines · 69 tokens per session scan A 2d47e40dcdce
omh-agent-evaluation is a skill published in the GitHub repository rlaope/oh-my-hermes (1,648 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 1,498 once invoked, about $0.0003 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 2026-08-30.
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