Runway Dev Model Routers logo

Runway Dev Model Routers

Organization
runwayml
runway-dev-model-routers

Build, modify, debug, or verify Runway Model Routers: inspect live config and eligible models via MCP, manage approved settings, integrate routed SDK calls, and inspect routing results. Use with +runway-dev. Not for direct per-model endpoints or agent REST CLI.

Overview

Publisherrunwayml
Repositoryskills
Skill namerunway-dev-model-routers
Stars
68
Forks
17
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 runwayml on GitHub. Read the source before you install it.

Installation

Install the Runway Dev Model Routers 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.
  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/runwayml/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/runway-dev-model-routers .claude/skills/runway-dev-model-routers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Runway Dev Model Routers 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 Runway Dev Model Routers 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 Runway Dev Model Routers 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.

Runway Dev — Model Routers

Companion: Use +runway-dev for shared guidance when available. If it is not installed, inspect the workspace, probe RUNWAYML_API_SECRET without printing it, and read current docs. Encourage connecting Dev MCP to inspect or manage router configs. If the user declines or cannot connect, continue integration from current docs and an existing configId.

Goal

Keep a Model Router and its application integration correct. Verify changes with one routed SDK call (client.generate.{video|image|audio}.create({ configId, input })) when safe.

MCP tools

  • list_model_routers / get_model_router — find a router by configId, then inspect it using the returned record UUID.
  • list_models — inspect eligible models and capabilities.
  • create_model_router / update_model_router / delete_model_router — manage routers after user approval; updates replace the full configuration.
  • get_credit_balance — check budget before proposing credit ceilings or testing.
  • get_task_routing — explain which model handled an existing routed task.

New router

  1. list_models across relevant endpoints — do not guess eligible models or capabilities.
  2. get_credit_balance — understand budget before proposing credit ceilings.
  3. Propose: name, immutable configId slug, description, routing preference, model-list policy, capacity fallback, optional credit caps. Wait for user approval unless they supplied all fields.
  4. create_model_router, then update_model_router for settings if needed.
  5. Validate the intended payload with HTTP dryRun: true before a billable generation; the SDK does not currently support dry runs.
  6. When billable verification is appropriate, make one routed SDK call with the wait helper chained directly from create(), then use get_task_routing to explain which model ran.

Existing router

  1. Use list_model_routers to resolve the application's configId slug to a router record, then call get_model_router with its UUID.
  2. Clarify integration goal (wire into app vs test call).
  3. Implement the routed generate call behind the application's server boundary per https://docs.dev.runwayml.com/model-routers.md, chaining the SDK wait helper directly from create().
  4. Validate the same payload with HTTP dryRun: true before any billable verification.
  5. After an approved live test, use get_task_routing to confirm which model ran.

Terminology

  • Router record id (UUID) ≠ configId (immutable slug used in SDK configId field).

Docs

Frequently asked questions

What does the Runway Dev Model Routers AI skill do?

Build, modify, debug, or verify Runway Model Routers: inspect live config and eligible models via MCP, manage approved settings, integrate routed SDK calls, and inspect routing results. Use with +runway-dev. Not for direct per-model endpoints or agent REST CLI.

Why use Runway Dev Model Routers on TypingMind?

Because you install it once and use it with any model. Runway Dev Model Routers 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 Runway Dev Model Routers in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runwayml/skills/tree/main/skills/runway-dev-model-routers. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Runway Dev Model Routers?

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 Runway Dev Model Routers?

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

Is the Runway Dev Model Routers AI skill free?

Yes. It is published on GitHub by runwayml 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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