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 | $0.00071 | $0.01584 |
| Opus 5.5 | $0.00028 | $0.00634 |
| Sonnet 5.5 | $0.00014 | $0.00317 |
| Haiku 4.5 | $0.00007 | $0.00158 |
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 15d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instinct Ledger
This is an OMH instinct-ledger workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
instinct-ledger exists because a lesson seen once is an anecdote and one seen across runs or projects is a candidate rule: it records each as one atomic instinct with its trigger, action, scope, confidence, and evidence, and promotes one only through a reviewed store route, never by writing a host's memory or skills on its own.
Do Not Use When
- A single missed route or one run's trace.
- Writing reviewed lessons into AGENTS guidance, prompts, or skills; use
omh-rules-distill. - A run that is stuck right now; use
omh-agent-debugbefore drawing a lesson from it.
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.
- A promoted instinct leaves as a store route,
memory_candidateformemory-newor askill_update_candidate, never as a parallel store of its own. - On Hermes, the skill audit ledger (
skills/.curator_ledger.jsonlunder the active profile's Hermes home) is observed evidence of what the agent or its background review already wrote; an instinct that matches a ledger entry no store route accounts for is reported asunreviewed_native_write(OMH holds no record of that write, which is not to say nobody reviewed it), never counted as repeated evidence for the instinct.
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.
- 15d ago Changed · +1 lines ddb7535e61b0
- 22d ago Changed 3b2883aa3ab0
- 24d ago Changed · +3 lines abf9e2bc5a71
- 1mo ago Changed · +3 lines 62231b63f819
- 1mo ago Changed · +1 tokens per session b3ccb07d5b79
- 1mo ago Changed · +6 lines db74247a2a84
- 1mo ago Changed · -11 lines e634f25e8c81
- 2mo ago Changed 20add2da44c6
- 2mo 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 (5,182 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 1,584 once invoked, about $0.0003 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 2026-08-30.
omh-instinct-ledger compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| omh-instinct-ledgerrlaope/oh-my-hermes | Skill | 5.2k | yesterday | MIT | A | 71 | Claude Code, Codex |
| context-engineeringseaworld008/Commonly-used-high-value-skills | Skill | 74 | 2d ago | MIT | A | 24 | Claude Code, Codex |
| experience-managerkonglong87/methodology-skills | Skill | 6 | 3mo ago | MIT | A | 17 | Claude Code |
| castseaworld008/Commonly-used-high-value-skills | Skill | 74 | 2d ago | MIT | A | 32 | Claude Code, Codex |
| llm-wikiseaworld008/Commonly-used-high-value-skills | Skill | 74 | 2d ago | MIT | A | 33 | Claude Code, Codex |
| honchoseaworld008/Commonly-used-high-value-skills | Skill | 74 | 2d ago | MIT | A | 33 | Claude Code, Codex |
| loreseaworld008/Commonly-used-high-value-skills | Skill | 74 | 2d ago | MIT | A | 40 | Claude Code, Codex |