From runway-api
Guides setup of Runway ML API key including account creation, Node.js/Python SDK installs, env var config, .gitignore updates. Use before Runway API integration in server-side projects.
npx claudepluginhub runwayml/skillsThis skill is limited to using the following tools:
Guide the user through obtaining a Runway API key, installing the SDK, and configuring their project for API access.
Guides full Runway API setup in Node.js/Python projects: checks server-side compatibility, configures API key/SDK install, integrates video/image/audio generation endpoints.
Installs Runway ML SDK for Python or Node.js and configures API key authentication, enabling AI video generation and creative workflows.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Share bugs, ideas, or general feedback.
Guide the user through obtaining a Runway API key, installing the SDK, and configuring their project for API access.
PREREQUISITE: Run
+rw-check-compatibilityfirst to ensure the project has server-side capability.
Direct the user to:
Important warnings to tell the user:
npm install @runwayml/sdk
Requires Node.js 18+. The SDK includes TypeScript type definitions.
pip install runwayml
Requires Python 3.8+. Includes MyPy type annotations.
The SDK automatically reads the API key from the RUNWAYML_API_SECRET environment variable.
.env file (recommended for development)Check if the project already has a .env file. If so, append to it. If not, create one.
RUNWAYML_API_SECRET=your_api_key_here
For Node.js projects: Ensure the project loads .env files:
.env support, no extra setup neededdotenv:
npm install dotenv
Add to the entry point:
import 'dotenv/config';
For Python projects: Ensure python-dotenv is installed if not using a framework with built-in support:
pip install python-dotenv
Add to the entry point:
from dotenv import load_dotenv
load_dotenv()
export RUNWAYML_API_SECRET=your_api_key_here
// Node.js
const client = new RunwayML({ apiKey: 'your_api_key_here' });
# Python
client = RunwayML(api_key='your_api_key_here')
Warn the user: Never hardcode keys in source code. Use environment variables or a secrets manager.
Ensure .env is in .gitignore to prevent accidentally committing the API key:
.env
.env.local
.env.*.local
Check the existing .gitignore and add the entry if it's missing.
Suggest the user run a quick verification:
import RunwayML from '@runwayml/sdk';
const client = new RunwayML();
// If no error is thrown, the API key is configured correctly
console.log('Runway SDK initialized successfully');
from runwayml import RunwayML
client = RunwayML()
# If no error is thrown, the API key is configured correctly
print('Runway SDK initialized successfully')
Remind the user:
// Node.js - check organization info
const response = await fetch('https://api.dev.runwayml.com/v1/organization', {
headers: {
'Authorization': `Bearer ${process.env.RUNWAYML_API_SECRET}`,
'X-Runway-Version': '2024-11-06'
}
});
const org = await response.json();
console.log('Credits:', org.creditBalance);
Before moving on, verify:
.env file is in .gitignoreOnce the API key is configured, the user can proceed with integration:
+rw-integrate-video — Video generation (text-to-video, image-to-video)+rw-integrate-image — Image generation+rw-integrate-audio — Audio generation (TTS, sound effects, voice)+rw-integrate-uploads — File upload for models that require image/video input