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-instinct-ledgergit 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-instinct-ledger)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-instinct-ledger"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-instinct-ledger/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-instinct-ledger"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-instinct-ledger.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 | $0.00070 | $0.01394 |
| Opus 5 | $0.00035 | $0.00697 |
| Sonnet 5 | $0.00014 | $0.00279 |
| Haiku 4.5 | $0.00007 | $0.00139 |
Grade A, and why
omh-instinct-ledger 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 8d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instinct Ledger
This is a Hermes-native instinct-ledger workflow skill.
Why This Exists
instinct-ledger exists so Hermes users can ask for this workflow in chat and receive a structured, evidence-bounded OMH operating surface instead of ad hoc narration.
Do Not Use When
- The request is already handled by a narrower explicit skill with stronger evidence.
- The user asks OMH to secretly run external platforms, connectors, schedulers, file exports, or runtime agents.
- The only safe answer is to ask for missing authority, credentials, target, or observed evidence first.
Examples
Good example:
- Prompt: instinct-ledger turn these repeated OMH review lessons into project-scoped instincts and show which ones could be promoted globally.
- Expected behavior: Produce
prepare_instinct_ledgerwith required context, wrapper actions, and not-evidence boundaries. - Why: The prompt names a real workflow surface that Hermes can orchestrate without hiding execution.
Bad example:
- Prompt: instinct-ledger silently install hooks, learn from every prompt, and mutate all skills globally.
- Expected behavior: Report the missing observed evidence or authority instead of claiming the external step happened.
- Why: Prepared OMH guidance is not platform, runtime, connector, file, memory, or delivery evidence.
Completion Checklist
- Each instinct is atomic: one trigger, one action, one scope, confidence, evidence refs, and review state.
- Project-specific conventions, global practices, project/global promotion candidates, imports, and exports are separated.
- No hooks, memory writes, skill edits, global promotion, import/export, or behavior-change claims are made without observed approval and implementation evidence.
Recovery Notes
- If the request is a single missed route or run trace, route to workflow-learning first.
- If the request is to mutate durable rules, prompts, skills, or AGENTS guidance, route to rules-distill or implementation after review approval.
- If evidence comes from a stuck run, use agent-debug before converting lessons into instincts.
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.
- 8d ago Changed 20add2da44c6
- 13d ago First seen · 126 lines · 70 tokens per session scan A 0b80e6af5a3d
omh-instinct-ledger is a skill published in the GitHub repository rlaope/oh-my-hermes (1,716 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 1,394 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.
Other skills, from other repositories
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Use when configuring shared team memory stores and knowledge graphs.
memory-types
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fable-handoff
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fable-memory
Manage persistent file-based memory, indexing cross-session user preferences, feedback, and architectural constraints in structured MEMORY.md stores. Use when recording user feedback, storing project conventions, recalling cross-session architectural constraints, or indexing durable project memory — even if the user…
experience-manager
A project knowledge system for recording, finding, reviewing, and improving lessons learned. It stores rules, working strategies, technical knowledge, and past history at different levels.
context-engineering
Diagnose missing or overloaded agent context and configure project instructions when setup or context quality is the task.