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Model Onboarding

CommunityPopular
rlaope
model-onboarding

Onboard a new model generation or sibling into oh-my-hermes: probe router recognition, research the official contract, write trait-to-counter calibration, place routing in both lanes, price from documented list only, gate machine config on a served route, prove with the gates, close with a benchmark pair. Use when a model family ships a new generation (for example "Fable 5.1 is out", "GLM 5.4 shipped", "onboard new model", "add model to chains").

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill namemodel-onboarding
Stars
3.6K
Forks
269
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Model Onboarding AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/rlaope/oh-my-hermes/tree/main/.claude/skills/model-onboarding
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/.claude/skills/model-onboarding .claude/skills/model-onboarding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Model Onboarding in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Model Onboarding on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Model Onboarding is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Model onboarding

The procedure lives in docs/MODEL-ONBOARDING.md. Read that file and follow it in order.

It is kept there rather than here because Codex, Hermes handoffs, and generic executor profiles run the same loop, and AGENTS.md requires that no single executor own a shared surface. This file exists so the loop is reachable as /model-onboarding; it holds no rules of its own.

Start by reading, in this order:

sh
cat docs/MODEL-ONBOARDING.md
cat MODEL_OPTI.md

Nine things the loop gets wrong most often:

  • Recognition before research. Probe every served id and every bare chat name with omh coding model-route first; a model_family of unknown means the calibration never attaches. Probe the dated snapshot form too (<id>-YYYY-MM-DD): some providers serve only that spelling, and it must resolve the base's contract, chain position, price, and HUD label through dated_snapshot_base() rather than falling to generic.
  • The chain names the id the vendor's API serves, not the model card's spelling. Read the Hermes provider profile for the family before choosing the alias — DeepSeek serves deepseek-flash and 400s deepseek-v4.1-flash; the versioned contract sits behind the pointer as a declared projection and in EXACT_CONTRACT_POINTER_ALIASES.
  • Research runs as four parallel read-only lanes (official docs, Hermes runtime source, the vendor's own harness, community harnesses), each a labeled dossier under .omc/research/; the Hermes lane is the one that shows what the wire carries (id folding, effort overrides, passback).
  • Chains move as a set. The Hermes-lane table, its plugin mirror, the Maestro-lane table, the model-setup skill text, seven public doc surfaces, the release budget note, and the pinned-chain tests all name the old id; grep src/ tests/ docs/ README* site/ for it and move every site in the same commit. Superseded generations leave the shipped chains (owner decision, 2026-09-11) and join the recognition-only list; pins hide in fixtures, so start the full suite in the background at the chain edit.
  • Machine config stays provider-neutral. Chains name aliases; the provider row is a separate concern in model-providers.json. Place the id with omh model-chains set, never by hand-editing the JSON; an operator may keep an older generation behind it there, the shipped table does not.
  • Served is not released. Prove the route with one hermes --oneshot call and read the usage file (model, provider, cost_status) before any measurement or placement; gateways want the vendor-prefixed id.
  • The override is measured against the block it replaced, on cost. Run the family arm next to baseline and optimized; expect pass rate to tie and read the paired token delta, tool calls, and turns. Same pass with more tokens on the tasks it fails is a sentence that pushes — cut it.
  • A routing signal is only as good as the tier's chain head. Measure the head on the request class it will receive before shipping the signal, and name the head in every routing claim.
  • A separate reviewer lane reads the diff before the commit. The DeepSeek round's reviewer caught a reverse projection that let -pro / -fast / -flex aliases label their base id; self-review did not.

Arguments pass through verbatim: the model ids as served (for example claude-fable-5-1 claude-mythos-5-1).

Frequently asked questions

What does the Model Onboarding AI skill do?

Onboard a new model generation or sibling into oh-my-hermes: probe router recognition, research the official contract, write trait-to-counter calibration, place routing in both lanes, price from documented list only, gate machine config on a served route, prove with the gates, close with a benchmark pair. Use when a model family ships a new generation (for example "Fable 5.1 is out", "GLM 5.4 shipped", "onboard new model", "add model to chains").

Why use Model Onboarding on TypingMind?

Because you install it once and use it with any model. Model Onboarding is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Model Onboarding in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/.claude/skills/model-onboarding. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Model Onboarding?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Model Onboarding?

As many as you like. As long as a model supports skills, you can use Model Onboarding with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Model Onboarding AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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