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-llm-app-devgit 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-llm-app-dev)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-llm-app-dev"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-llm-app-dev/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-llm-app-dev"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-llm-app-dev.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 | $0.00102 | $0.03033 |
| Opus 5 | $0.00051 | $0.01517 |
| Sonnet 5 | $0.00020 | $0.00607 |
| Haiku 4.5 | $0.00010 | $0.00303 |
Grade A, and why
omh-llm-app-dev 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Llm App Dev
This is a Hermes-native llm-app-dev workflow skill.
Why This Exists
llm-app-dev exists because the failure modes of an LLM feature are not the failure modes of the code around it. A floating model alias, a prompt buried in a string literal, an output scraped out of prose with a regex, and a retrieval layer nobody measured all pass code review and all fail in production, and without a golden set nobody can tell whether the next prompt edit helped or hurt.
Do Not Use When
- The subject is comparing executors or agent harnesses - Codex against Claude Code against Hermes coding - rather than evaluating the product's own model calls; use
agent-evaluation. - An agent run is already stuck, looping, or drifting and needs diagnosis; use
agent-debug. - The subject is the harness's own context window, prompt caching, or token budget rather than the application being built; use
context-budget-review. - The request is a prompt-injection, secret-handling, or dependency risk gate on work that already exists; use
security-safety-review. - The feature makes no model call - the LLM is only mentioned as the subject being discussed - so this is a direct answer, not a build handoff.
Examples
Good example:
- Prompt: $llm-app-dev we are adding an invoice-field extractor that calls a model per upload - set it up so we can change the prompt later without guessing.
- Expected behavior: Name the rails, put the provider call behind one client module with a pinned model ID, declare the extraction schema and the repair path, lay the prompt out as a versioned file, and specify the golden set and validators that let the next prompt edit be compared against this baseline.
- Why: The feature is a real model call whose output another system consumes, which is exactly where an unpinned model, an inline prompt, and a missing golden set become expensive later.
Bad example:
- Prompt: $llm-app-dev the extractor is done - confirm the new prompt is better than the old one.
- Expected behavior: Prepare the paired baseline-vs-candidate comparison and state that no result exists until the run is observed; report nothing about which prompt is better.
- Why: Better is a claim about an observed run. Without one, the comparison is a design, and calling it a result is the false-green this workflow exists to prevent.
What ships with it
4 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 Changed · +1 lines ccab619ec4e7
- 5d ago Changed · +4 lines d70da311bb20
- 7d ago Changed · +2 lines 1801902ea0b7
- 11d ago First seen · 143 lines · 102 tokens per session scan A 9dc3623c1c1c
omh-llm-app-dev is a skill published in the GitHub repository rlaope/oh-my-hermes (1,648 stars, last pushed today), licensed MIT. It adds 102 tokens to every session and 3,033 once invoked, about $0.0005 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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