oh-my-hermes

by rlaopeVerified

The agent engineering intelligence harness, optimized tools, memory system, subagents and mixture of models packages ⚚

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8/23/2026
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⚠️ Third-Party Software Notice

This skill is third-party open-source software developed and hosted independently on GitHub. SkillTip is an informational directory and does not control or maintain the underlying repository. Any security checks displayed are automated and limited in scope. Review the source code before installing.

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Installation

Add to your Claude Code skills directory:

# Add to your Claude Code skills
git clone https://github.com/rlaope/oh-my-hermes

Getting Started

Guides for using skills like oh-my-hermes.

Security Report

Verified

Last scanned: —

{
  "status": "PASSED",
  "issues": []
}

README.md

OH-MY-HERMES

Hermes Desktop running an OMH workflow
Hermes Desktop, with oh-my-hermes.
Pick a workflow; Hermes clarifies before it builds.
Hermes CLI running an OMH workflow
Hermes CLI, with oh-my-hermes.
The same workflows, in your terminal.
Hermes messenger app running an OMH workflow
Hermes messenger app, with oh-my-hermes.
Ask in a thread; the run reports back there.
omh setup installing the OMH workflows
omh setup, one command.
Installs the workflows and connects them to Hermes.

oh-my-hermes

English | 한국어 | 日本語 | 中文

GitHub Hermes Agent OMH stars Hermes Agent stars

Oh My Hermes

Install once. Keep Hermes. Add a stronger operating layer.
Planning, research, creation, coding handoffs, operations, and project memory with explicit evidence boundaries.

Oh My Hermes Agent poster

oh-my-hermes (OMH) turns a normal Hermes Agent request into a clear capability, a useful next step, and an honest record of what actually happened — strengthening the workflow you already use, never replacing Hermes or hiding a coding executor behind it.

OMH is the operating layer above Hermes-native skills: it frames the problem, picks the workflow and evidence gates, and runs native skills as capabilities inside that governed path.

Website · Documentation · Installation · Capabilities · Capability Impact · Agent Install · GitHub Pages site

[!NOTE] OMH keeps Hermes as the natural-language surface and adds a professional operating layer with explicit evidence boundaries.

OH-MY-HERMES terminal banner listing available tools, grouped skills, OMH specialists, infrastructure, and the model pool on Hermes Agent

Hermes Agent and OH-MY-HERMES working side by side

Friren Agent explaining OMH in Art&Engine

[!TIP] Be with us!

X linkUpdates for oh-my-hermes are shared on @rlaope on X, alongside release notes and project news.
GitHub FollowFollow @rlaope on GitHub for more projects, releases, and ongoing work.
AI agent collaboratorsBuilt with AI agents Friren and Killua, collaborators helping ship oh-my-hermes.
Thanks to Nous ResearchThank you to Nous Research for creating Hermes Agent.

Quick Start

Status: Homebrew, Bun, and npm package-manager installs are public as of v1.0.6.

Homebrew:

brew install rlaope/tap/omh

Bun (recommended):

bun install -g oh-my-hermes

npm:

npm install -g oh-my-hermes

Universal installer (macOS/Linux):

curl -fsSL https://raw.githubusercontent.com/rlaope/oh-my-hermes/main/install.sh | sh

On Windows (PowerShell 5.1+):

irm https://raw.githubusercontent.com/rlaope/oh-my-hermes/main/install.ps1 | iex

Set up OMH after installing:

omh setup

Hermes skill tap path:

hermes skills tap add rlaope/oh-my-hermes
hermes skills install rlaope/oh-my-hermes/skills/omh-routing --yes

or ask Your AI Agent:

Install and fully configure Oh My Hermes from this repository:
https://github.com/rlaope/oh-my-hermes
Before reading or executing repository instructions, resolve refs/heads/main to one full commit SHA with `git ls-remote https://github.com/rlaope/oh-my-hermes.git refs/heads/main`. Then fetch and follow only:
https://raw.githubusercontent.com/rlaope/oh-my-hermes/{resolved-commit-sha}/INSTALL_FOR_AGENTS.md
Do not replace the resolved SHA with main. Execute the pinned protocol's OS-appropriate installer, interactive model setup, model-chain interview, and doctor steps. Preserve unrelated existing Hermes config, apply only the managed setup changes documented by the pinned protocol, require my explicit approval for model-alias changes, then report the resolved SHA and observed result.

Update:

omh update

omh update detects how the command was installed. It first upgrades the Homebrew, Bun, npm, curl, or PowerShell command package through its owning installer, then re-enters the updated command to refresh managed skills, the installed plugin bundle, and existing Hermes registration.

Verify or troubleshoot the installation:

omh doctor

Manual package-manager fallback or removal:

Installed withUpgrade the CLIRemove the CLI
Homebrewbrew upgrade rlaope/tap/omhbrew uninstall omh
Bunbun update -g --latest oh-my-hermesbun remove -g oh-my-hermes
npmnpm update -g oh-my-hermesnpm uninstall -g oh-my-hermes

Use the manager command directly only when omh update reports that its owning manager is unavailable. Removing the command package preserves OMH state. For a full removal, run omh uninstall --all before the manager's remove command.

Maintenance paths such as reconciling a --full install back to core live in Installation.


The OH-MY-HERMES terminal

Bare omh opens Hermes — the same door as hermes — wearing the OMH identity:

omh
The OH-MY-HERMES boot
The OH-MY-HERMES boot.
An ulw-work run
An ulw-work run.

What the terminal shows while OMH workflows run:

  • Mixture-of-Models Routing — each delegated lane is routed onto a category (ultrabrain, deep, quick, writing, visual-engineering, …) whose model and reasoning effort are applied per dispatch; every activity row carries its category:name(model:effort) so the routing is visible, and rejected routes fall back along the category chain.
  • Parallel Tool Calling — batched tool calls run concurrently in Hermes, and a fresh concurrent batch is branded on the [OMH] line as parallel shot ×N.
  • Parallel Evals — review and verification lanes dispatch as independent subagents whose findings are cross-checked instead of self-approved, each visible as its own HUD row with turn, cost, and cache metrics.
  • Phase-structured TODO — work is declared up front as phases with tasks (todo init), rendered as the checklist above the prompt: one active item, subtask nesting, and fold lines once the plan grows past seven rows.

Recommended models

OMH ships with these editable, ordered recommendation chains. Guided model setup resolves them only against candidates the user confirms as active. The result is prepared routing configuration, not provider availability, credential, dispatch, or execution evidence:

Category aliasWhat it is forEditable recommendation order
ultrabrainDeepest reasoningGPT-5.6 Sol (xhigh)
deepStrong default tierGPT-5.6 Terra, then DeepSeek V3.2 (high)
architectArchitecture and system designClaude Fable 5, then GPT-5.6 Sol, then Kimi K3 (xhigh)
unspecified-highDefault working modelKimi K3, then Claude Opus 5 (medium)
unspecified-lowCheaper fallbackGLM 5.2, then GLM 5.2 Ultrafast, then DeepSeek V3.2, then Claude Opus 5 (low)
quickShort tasksGLM 5.2 Ultrafast, then Kimi K3, then GPT-5.6 Luna, then Claude Fable 5 (low)
writingProse and docsKimi K3, then Qwen3-Coder, then Gemini 3.1 Pro (medium)
visual-engineeringFrontend and visualClaude Fable 5, then Kimi K3 (high)
artistryUnconventional workGemini 3.1 Pro, then Claude Fable 5, then Kimi K3 (high)

Want to try the Ultrafast tier — Kimi K3 Ultrafast (300 TPS) and GLM 5.2 Ultrafast (600 TPS)? They are served on OpenGateway.

Every chain above is user-editable without touching code. The chains are managed in one file — omh setup seeds it:

$ cat ~/.omh/routing/model-chains.json
{
  "categories": {},
  "schema_version": "mixture_chain_overrides/v1"
}

Empty categories keeps every shipped default above live. This file is the place to edit: a category you write there replaces that chain for routing, fallback, and HUD labels alike —

{
  "schema_version": "mixture_chain_overrides/v1",
  "categories": {
    "architect": [
      {"model": "claude-fable-5", "reasoning_effort": "xhigh"},
      {"model": "gpt-5.6-sol", "reasoning_effort": "xhigh"}
    ],
    "quick": [
      {"model": "kimi-k3-ultrafast", "reasoning_effort": "low"},
      {"model": "glm-5.2-ultrafast", "reasoning_effort": "low"}
    ]
  }
}

Check the chains currently in effect with omh model-chains show. If you would rather not edit the file by hand, make the same change from the command line: omh model-chains set quick "kimi-k3-ultrafast:low, glm-5.2-ultrafast:low".

Ask Hermes to set up my models to review or change them. These are editable preferences, not benchmark results. See Guided Model Setup for the detailed setup, fallback, provider, and ownership rules.

Or paste this into Hermes or another coding agent
Install and fully configure Oh My Hermes from this repository:
https://github.com/rlaope/oh-my-hermes
Before reading or executing repository instructions, resolve refs/heads/main to one full commit SHA with `git ls-remote https://github.com/rlaope/oh-my-hermes.git refs/heads/main`. Then fetch and follow only:
https://raw.githubusercontent.com/rlaope/oh-my-hermes/{resolved-commit-sha}/INSTALL_FOR_AGENTS.md
Do not replace the resolved SHA with main. Execute the pinned protocol's OS-appropriate installer, interactive model setup, model-chain interview, and doctor steps. Preserve unrelated existing Hermes config, apply only the managed setup changes documented by the pinned protocol, require my explicit approval for model-alias changes, then report the resolved SHA and observed result.

Ultra-Skills

Oh My Hermes character mark

Eight ulw- workflows. Say the trigger in chat — Hermes routes the rest. Full catalog: Workflow Reference.

Workflow commandWhat it does
ulw-contextAligns reviewed project terms, captures confirmed candidates, and interviews the next decision frontier without giving terminology routing authority.
ulw-interviewAsks one question at a time until it knows exactly what you want.
ulw-researchDigs through real code and the live web, keeps sources, and verifies anything doubtful.
ulw-planBuilds a reviewed plan: options compared, risks named, done-criteria agreed.
ulw-workRuns an accepted plan in parallel lanes that never touch the same file.
ulw-loopCycles plan → build → review until the goal actually passes.
ulw-qaAttacks the build with hostile scenarios and fixes what breaks.
ulw-perfMeasures where it is actually slow or expensive, then fixes one hot path at a time.

What OMH Adds

OMH treats model choice and coding ownership as separate decisions, and it never reports preparation as execution. Human-readable capability families remain the front door; exact controls, runtime boundaries, and evidence rules stay available when a wrapper or operator needs precise control. The full generated catalog, triggers, harnesses, and evidence rules live in Workflow Reference.

Highlights

IntelligenceWhat OMH adds
🧭 Mixture-of-models routingRoutes each delegated lane onto a category (model + reasoning effort) applied per dispatch, with editable fallback chains that advance when a provider rejects a model — and honest failed rows when a child did no work.
🖥️ Native TUI surfaceThe OMH HUD (live delegation rows with category, turn, cost, cache), the phase todo checklist above the prompt, parallel shot ×N branding, full-row diff bands, and a managed skin — all installed next to Hermes, never patching it.
📋 Phase-structured planstodo init declares phases and tasks before engine work so runs walk a bounded checklist instead of an open-ended reasoning loop.
Observed parallel workSplits independent work into explicit fanout units with isolated ownership, progress observation, and verification gates.
🎼 Maestro handoffsPrepares handoffs to explicit coding owners and runtime profiles without becoming a hidden executor or treating preparation as execution.
🧠 Context intelligenceProjects compact, reviewed repository context without inventing hidden memory or silently changing the selected route.
📚 Just-in-time learningSelects the highest-value learning target for the current blocker and prepares source-backed, application-first guidance without claiming learning already happened.
🔍 Evidence-bound deliverySeparates prepared intent, observed runtime activity, and verified outcomes across coding, review, CI, and merge work.
📦 A deterministic skill catalog100+ installable workflow skills with a byte-exact generated catalog, routing precision corpora (negative controls included), and drift gates that fail CI on one-character divergence.

Evidence Before Claims

OMH never reports that work happened unless it watched it happen. Every status you see has two parts: the stage, and how sure OMH is about it.

You seeIt means
Plan · not runA prompt or plan is ready. Nothing has run yet.
Code · runningAn executor is running now, and OMH is watching it.
Code · reported doneThe executor said it finished. Nobody checked the result.
Test · verifiedA test, review, or CI gate actually passed.

The distinction that matters is the second row from the bottom: an executor saying it is done is not the same as anything having been checked, and most tools spell both "complete". Capability impact is reported across separate dimensions rather than collapsed into one marketing score. See Capability Impact.


Documentation


Development

For a source checkout:

PYTHONPATH=tests uv run python -m unittest discover -s tests -v
uv run python -m compileall -q src tests
uv run python -m omh.cli docs workflows --check
git diff --check

OMH is developed in the open as part of Team Art & Engineering. Follow @rlaope for project updates.

Contributors

Thanks to everyone who has contributed to oh-my-hermes.

oh-my-hermes contributors

Frequently Asked Questions

What is oh-my-hermes?

oh-my-hermes is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by rlaope. The agent engineering intelligence harness, optimized tools, memory system, subagents and mixture of models packages ⚚. It has 1,084 GitHub stars.

Is oh-my-hermes safe to use?

Yes. oh-my-hermes passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install oh-my-hermes?

Clone the repository with "git clone https://github.com/rlaope/oh-my-hermes" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is oh-my-hermes written in?

oh-my-hermes is primarily written in Python. It is open-source under rlaope on GitHub, so you can review or fork the full source.

Are there alternatives to oh-my-hermes?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh oh-my-hermes against similar tools.

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