runway-workflows
This skill enables the agent to build, inspect, and execute Runway editor workflow graphs through an MCP interface.
Identity proven by GitHub's verified organisation badge (runwayml)
- Agents
- Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode, claude.ai and API
- License
- MIT (permissive)
- Pin
- f56e77apinned Oct 9, 2026
- Context cost
- about 360 tokenswhen the skill loads
- GitHub stars
- 2on runwayml/runway-mcp-plugin
- Scripts
- None found
What it does
“Build, inspect, validate, and run Runway editor workflow graphs via MCP. Use when the user asks to create, edit, list, or run Runway workflows, design multi-step generation pipelines, or work with workflow graphs, nodes, and dynamic_workflow tasks.”
Generated summary from the skill's own text
This skill enables the agent to build, inspect, and execute Runway editor workflow graphs through an MCP interface. It handles multi-step generation pipelines by creating workflows from templates, validating graph structures, and running them to completion. The agent reads domain resources for current node types and model capabilities, then constructs JSON graphs with proper UUIDs rather than inventing configurations.
When to use it
- User requests creating or editing Runway workflow pipelines
- Need to design multi-step generation sequences with connected nodes
- Running complex workflows that require validation before execution
- Working with existing workflow templates as starting points
When not to
- Simple one-shot image or video generation tasks
- User wants to build workflows directly in the Runway editor interface
- Working with published Workflow Apps
Install
These commands install from the publisher's repository and need no account.
skills CLI
npx skills add runwayml/runway-mcp-plugin --skill runway-workflowsInstalls from the repository's default branch, which may have changed since the commit we checked.
GitHub CLI 2.90 or later
gh skill install runwayml/runway-mcp-plugin runway-workflows@f56e77a713796c0667f266aa4b183f1e226230eaInstalls the commit we checked (f56e77a).
Copy by hand
Download the folder at f56e77a and put it in .claude/skills/runway-workflows for Claude Code and in .agents/skills/runway-workflows for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.
Claude Code plugin
/plugin install runway-api@claude-plugins-officialThe publisher's plugin in Claude's official marketplace. It may hold more than this skill, and we have not pinned it.
Copy for your agent
A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.
Prompt
Install the agent skill "runway-workflows" by Runway. Source, pinned to commit f56e77a713796c0667f266aa4b183f1e226230ea: https://github.com/runwayml/runway-mcp-plugin/tree/f56e77a713796c0667f266aa4b183f1e226230ea/skills/runway-workflows 1. Download that folder at exactly this commit. Do not use the default branch. 2. Copy every file unchanged, keeping the folder structure, into .claude/skills/runway-workflows/ for Claude Code and .agents/skills/runway-workflows/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist. 3. Do not edit the files, and do not run any script in the folder unless I ask you to. 4. When you are done, list the files you copied and where you put them. Listing and checks: https://www.agentnative.dev/skills/runway/runway-workflows
SKILL.md at the pin
---
name: runway-workflows
description: Build, inspect, validate, and run Runway editor workflow graphs via MCP. Use when the user asks to create, edit, list, or run Runway workflows, design multi-step generation pipelines, or work with workflow graphs, nodes, and dynamic_workflow tasks.
---
# Runway MCP workflows
> Not for: one-shot `generate_image` / `generate_video` — use the `runway-media` skill. Not for: published Workflow Apps.
Domain knowledge (port types, node catalog, model modes, worked examples) lives in the server's MCP resources, which are always current. Do not copy tables into this skill.
## Before you start
1. Runway MCP connected, workspace selected.
2. Read the resources: `runway://docs/workflows/overview`, then `authoring`, `models`, `catalog`, and `examples`.
3. `list_workflows` → `get_workflow` with `include_graph: true` on a similar template. **Copy** that graph shape; do not invent `{ type, config, inputs }` or human-readable ids like `input_photo`.
4. Sketch the pipeline as a mermaid `flowchart LR` and show it to the user with inputs, node count, and estimated credits before writing JSON.
## Author and run
- New workflow → `create_workflow`. Edit an existing one → `save_workflow_version` with the API graph JSON copied from `get_workflow` (graphs must use UUID ids).
- `validate_workflow_graph` before saving when unsure; the save still succeeds with `errorCount > 0`, so surface errors to the user.
- Run with `run_workflow`. It returns a task id; say generation is in progress. The inline viewer polls where available — do not `sleep` + `get_task` in a loop unless the authoring guide says to.
- Never tell the user to build the graph themselves in the editor when the tools can do it.
From runwayml/runway-mcp-plugin at f56e77a, shown as plain text under its license: MIT (permissive). The license text is in LICENSE.
View SKILL.md on GitHub at f56e77aFiles
- SKILL.md1.7 KB · fc22dde23990
1 file, 1.7 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.
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