How do I install the Runway Dev Models skill?

Run npx skillmds add runwayml/runway-dev-models in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Runway Dev Models safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Runway Dev Models?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Runway Dev Models free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Runway Dev Models?

runwayml (@runwayml) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Runway Dev Models by runwayml — Agent Skill

Name: Runway Dev Models
Rating: 4.2 (5 reviews)
Author: runwayml

Runway Dev Models

Build, modify, debug, or verify Runway model generation in an application: discover accessible models and constraints with MCP, implement SDK calls, and wire inputs and outputs into the product UI. Use with +runway-dev. Not for Model Routers, Characters, recipes, or agent-side generate scripts.

runwayml 162ddac 3.0 KB Updated Sep 9, 2026

File contents

Runway Dev — Models

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 for live model access and constraints. If the user declines or cannot connect, continue from current docs and existing model configuration.

Goal

Help the user choose and integrate a model endpoint (/v1/text_to_video, /v1/text_to_image, etc.) across their application, including its backend call and existing UI. Verify changes with one working SDK call when safe.

MCP tools

list_models — discover models the selected project can call and read each model's inputConstraints.
get_credit_balance — check budget before billable verification.
get_task — inspect or debug an existing task, not replace SDK wait helpers in application code.

Workflow

Inspect the existing application. Confirm the user experience, target modality, and where generation inputs and outputs belong.
If the task requires model selection, access verification, or current constraints, call list_models with { projectId, endpoint }. If existing code pins a model, proceed from current docs unless live access must be verified.
Follow the endpoint docs linked by llms.txt; do not infer request fields from another model.
Keep RUNWAYML_API_SECRET behind the application's server boundary.
Implement or update the SDK call by chaining .waitForTaskOutput() in Node or .wait_for_task_output() in Python directly from the create call.
If the application has a UI, wire its controls to the backend and render loading, error, and generated-output states.
When verification is appropriate, submit one test generation. Present the result and offer to persist output before its signed URL expires.

Input media

Follow the current input docs linked by llms.txt: use a public HTTPS URL, a small data URI, or an ephemeral upload.
Send browser-selected files to the application's server, then use the SDK upload helper and pass its runway:// URI to generation. Local filesystem paths cannot be API inputs.
Do not accept arbitrary remote URLs from clients. Prefer uploads or allowlisted origins.
Ephemeral inputs and generated output URLs expire; persist anything the application must retain.

Do not

Guess model ids, ratios, or durations from memory or other models.
Put API keys in frontend bundles.
Add manual polling when the SDK wait helper fits, or resubmit on transient read errors.

Docs

Index: https://docs.dev.runwayml.com/llms.txt
Setup: https://docs.dev.runwayml.com/guides/setup/

---name: runway-dev-modelsdescription: Build, modify, debug, or verify Runway model generation in an application: discover accessible models and constraints with MCP, implement SDK calls, and wire inputs and outputs into the product UI. Use with +runway-dev. Not for Model Routers, Characters, recipes, or agent-side generate scripts.--- # Runway Dev — Models > **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 for live model access and constraints. If the user declines or cannot connect, continue from current docs and existing model configuration. ## Goal Help the user choose and integrate a model endpoint (`/v1/text_to_video`, `/v1/text_to_image`, etc.) across their application, including its backend call and existing UI. Verify changes with one working SDK call when safe. ## MCP tools - `list_models` — discover models the selected project can call and read each model's `inputConstraints`. - `get_credit_balance` — check budget before billable verification. - `get_task` — inspect or debug an existing task, not replace SDK wait helpers in application code. ## Workflow 1. Inspect the existing application. Confirm the user experience, target modality, and where generation inputs and outputs belong. 2. If the task requires model selection, access verification, or current constraints, call `list_models` with `{ projectId, endpoint }`. If existing code pins a model, proceed from current docs unless live access must be verified. 3. Follow the endpoint docs linked by `llms.txt`; do not infer request fields from another model. 4. Keep `RUNWAYML_API_SECRET` behind the application's server boundary. 5. Implement or update the SDK call by chaining `.waitForTaskOutput()` in Node or `.wait_for_task_output()` in Python directly from the create call. 6. If the application has a UI, wire its controls to the backend and render loading, error, and generated-output states. 7. When verification is appropriate, submit one test generation. Present the result and offer to persist output before its signed URL expires. ## Input media - Follow the current input docs linked by `llms.txt`: use a public HTTPS URL, a small data URI, or an ephemeral upload. - Send browser-selected files to the application's server, then use the SDK upload helper and pass its `runway://` URI to generation. Local filesystem paths cannot be API inputs. - Do not accept arbitrary remote URLs from clients. Prefer uploads or allowlisted origins. - Ephemeral inputs and generated output URLs expire; persist anything the application must retain. ## Do not - Guess model ids, ratios, or durations from memory or other models. - Put API keys in frontend bundles. - Add manual polling when the SDK wait helper fits, or resubmit on transient read errors. ## Docs - Index: https://docs.dev.runwayml.com/llms.txt - Setup: https://docs.dev.runwayml.com/guides/setup/

Frequently asked questions