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-model-optimizationgit 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-model-optimization)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-model-optimization"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-model-optimization/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-model-optimization"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-model-optimization.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 | $0.00082 | $0.01663 |
| Opus 5 | $0.00041 | $0.00831 |
| Sonnet 5 | $0.00016 | $0.00333 |
| Haiku 4.5 | $0.00008 | $0.00166 |
Grade A, and why
omh-model-optimization 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 yesterday.
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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Optimization
This is a Hermes-native model-optimization workflow skill.
Why This Exists
model-optimization exists so a new model release triggers one repeatable, evidence-ordered process instead of ad-hoc edits: recognition proves what the router sees, official-first research separates contracts from folklore, trait-to-counter keeps calibrations concrete, and the measurement close keeps them honest.
Do Not Use When
- The user wants their own machine's model routing configured or providers connected; use
model-setup. - The goal is measurable performance of an application or system, not model handling; use
performance-goalorultraperf. - The user wants benchmark-superiority or provider-readiness claims without measurements.
Examples
Good example:
- Prompt: GLM 5.3 and 5.3 Flash just shipped; check what we should optimize for them.
- Expected behavior: Probe recognition for both ids, verify family coverage, research the official thinking/tool contract plus community harness handling with labeled sources, draft version-aware trait-to-counter calibration, propose chain placement distinguishing the Flash sibling from the highspeed tier, and name the benchmark pair as the measurement close.
- Why: A new generation of a known family needs the whole process, not just a chain edit.
Bad example:
- Prompt: Just say the new model is the best and route everything to it.
- Expected behavior: Refuse the superiority claim, run the process, and place routing only with owner-approved config or repo changes backed by labeled sources.
- Why: Unmeasured superiority claims and blanket rerouting are exactly what the process exists to prevent.
Completion Checklist
- Recognition probe output exists for every new id, and the family label is the expected one.
- Every research finding is labeled official or community with its source kept.
- The calibration draft counters named traits and marks version-specific rules as such.
- Routing and pricing changes name their surface (operator config vs repo change) and their approval state.
- The measurement plan names the benchmark pair, or the recorded reason none can run, and the worse-measured-calibration rule is stated.
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.
- yesterday Changed 7db3ba11d7b4
- 4d ago Changed 9f0483cc205e
- 6d ago First seen · 127 lines · 82 tokens per session scan A 7de1ef5f6b42
omh-model-optimization is a skill published in the GitHub repository rlaope/oh-my-hermes (1,605 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 1,663 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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