Runway Dev

Organization

runwayml

runway-dev

Foundation for building, modifying, debugging, or verifying Runway Dev Platform integrations in an application: connect Dev MCP, use llms.txt to find current resources, resolve project context, use SDK wait helpers, and handle errors. Load with relevant runway-dev-* surface skills. Not for direct media generation scripts or REST CLI shortcuts.

Overview

Publisher	runwayml
Repository	`skills`
Skill name	`runway-dev`
Stars	68
Forks	17
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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 .claude/skills/runway-dev

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Runway Dev 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 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 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 Platform

Workflow for integrating Runway Dev products into an application. Use MCP for live account state and management, the SDK for application code, and current docs for API contracts.

When to use: Building, modifying, debugging, or verifying a Runway integration, including work started from Dev Portal.

Do not use for: one-off media generation from the agent or direct REST CLI actions.

Start with the work

Make useful progress before explaining setup. Inspect the workspace and existing configuration without narrating each check. Ask a question only when missing information blocks the next change.

Existing project: find its server boundary and user-facing integration point. Add a server route or function before integrating a frontend-only project.
Empty workspace: ask what the user wants to build. You may recommend a small web app with visible inputs and output as a Runway starting point, but do not claim the user requested one.
Before giving credential setup instructions, test whether RUNWAYML_API_SECRET is present without printing its value, for example with test -n "${RUNWAYML_API_SECRET:-}". If it is present, skip dotenv instructions.
Use the official SDK. Install @runwayml/sdk for Node or runwayml for Python only if the project needs it and does not already have it.
Keep updates short. Do not narrate a long setup sequence or checklist.

Current contracts

Installed skill prose is workflow guidance, not the canonical API schema. Resolve current contracts in this order:

Fetch https://docs.dev.runwayml.com/llms.txt.
Fetch only the exact linked documentation subset relevant to the task.
If that subset does not define the contract, read https://docs.dev.runwayml.com/api.md.
If machine-readable detail is still needed, use https://docs.dev.runwayml.com/openapi.json.

Do not invent endpoints, field names, or model constraints.

MCP policy

Encourage connecting Dev MCP as the happy path for live account context and management. Connect https://dev.runwayml.com/mcp with Runway OAuth. Never put an API key in MCP config or automate browser OAuth.

If the user declines or the connection fails, never block account-independent work. Continue with live docs, existing application config, or environment configuration. SDK and API integration code remain allowed. Stop only when the next requested step requires live account discovery, account or resource mutations, or billable verification.

Never imitate an unavailable MCP account-management or resource-management tool with a REST call. Explain the MCP dependency only when it blocks the requested action.

Call MCP tools only when the result affects the next step:

whoami when identity or access is uncertain.
list_projects when a live projectId must be selected or verified. Never guess one.
list_models when model access, selection, or current constraints matter.
get_credit_balance immediately before an approved billable verification.

API key (SDK only)

MCP uses OAuth. SDK calls use an organization-scoped API key from Developer Portal settings. Probe RUNWAYML_API_SECRET without printing it. If missing when a live SDK call is imminent, ask the user to store the key in a server-side environment file or secret manager. Never expose it client-side, in chat, or in source control; ensure local environment files are ignored.

SDK requests

Build one valid SDK request from the current API docs and, when needed, MCP list_models constraints.
Chain the wait helper directly from the create call: await client.<operation>.create({...}).waitForTaskOutput() in Node or client.<operation>.create(...).wait_for_task_output() in Python. Do not await create() before calling the helper.
Catch the SDK's TaskFailedError and surface its task details. Submit once; do not add a manual polling loop or auto-resubmit.
Use MCP get_task only to inspect or debug an existing task outside the application's SDK flow.
Wire successful output into the application's intended UI or consumer. Persist outputs if the app needs them after signed URLs expire (~24–48h).

Terminology

UI / quickstart	MCP / API
Characters	avatars (`list_avatars`, `get_avatar`)
Character ID	avatar UUID
Model Router config ID	immutable slug (`configId`)
live Session	`POST /v1/realtime_sessions`

Errors

Validation error → show message, fix field from MCP constraints or docs, retry once.
Auth/permission → stop; ask user to authenticate or pick accessible project.
Rate limit → honor retry interval.
FAILED task → report failure details; do not auto-resubmit.
Missing MCP tool → continue account-independent implementation; stop only when live account state or management is required.

Surface skills

Skill	When
`+runway-dev-models`	Model generation integration
`+runway-dev-model-routers`	Model Router setup and routed calls
`+runway-dev-characters`	Characters / realtime sessions
`+runway-dev-recipes`	Recipe pipelines
`+runway-dev-workflows`	Runway app workflows → API endpoints

Use +runway-dev with the relevant surface skill or skills when both are installed. Surface skills repeat their minimum setup so they remain useful when installed alone. Usually one surface matches the user's goal; load more when the task crosses surfaces.

Frequently asked questions

What does the Runway Dev AI skill do?

Why use Runway Dev on TypingMind?

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

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

Which AI models can use Runway Dev?

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?

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

Is the Runway Dev 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Runway Dev Platform

Start with the work

Current contracts

MCP policy

API key (SDK only)

SDK requests

Terminology

Errors

Surface skills

Frequently asked questions

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