CopilotKit helps developers integrate AI capabilities into their applications by routing tasks to appropriate sub-skills. It's designed for developers looking to enhance their projects with AI-driven features.
$ npx skills add https://github.com/copilotkit/skills --skill copilotkitCopilotKit Skills is a collection of reference material and agent instructions built on the open Agent Skills standard, designed to teach AI coding agents like Claude Code and Cursor how to develop with CopilotKit. The skills cover project setup, feature development, integration wiring, debugging, version migration, and open-source contribution workflows targeting the v2 API surface. Skills are maintained in the main CopilotKit monorepo, ensuring reference material stays synchronized with code releases. Developers can install the latest skills directly from the monorepo using a single command, eliminating version drift. This approach works across multiple AI coding agents, providing consistent guidance for building AI-powered features with CopilotKit.
Install using the provided command and access specific sub-skills as needed.
Add CopilotKit to existing projects
Diagnose CopilotKit issues
Build custom agent backends
Develop AI-powered features with CopilotKit v2
$ npx skills add https://github.com/copilotkit/skills --skill copilotkitgit clone https://github.com/copilotkit/skillsCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
I want to integrate AI capabilities into my [COMPANY]'s [INDUSTRY] application using CopilotKit. Here's the current setup: [DESCRIBE CURRENT APP ARCHITECTURE]. How can I use CopilotKit to route specific tasks like [TASK_1], [TASK_2], and [TASK_3] to the appropriate AI sub-skills? Provide a step-by-step implementation plan with code snippets where applicable.
# CopilotKit Integration Plan for Acme Corp's E-Commerce Platform
## Current Architecture
- Frontend: React with TypeScript
- Backend: Node.js with Express
- Database: PostgreSQL
## Implementation Steps
### 1. Install CopilotKit SDK
```bash
npm install @copilotkit/core @copilotkit/react-core
```
### 2. Configure CopilotKit in Express Backend
```javascript
import { CopilotKit } from '@copilotkit/backend-core';
const copilotKit = new CopilotKit({
apiKey: process.env.COPILOTKIT_API_KEY,
actions: {
generateProductDescription: async ({ productName, features }) => {
return `Create a compelling product description for ${productName} with these features: ${features.join(', ')}`;
},
analyzeCustomerSentiment: async (reviewText) => {
const sentiment = await analyzeSentiment(reviewText);
return { sentiment, confidence: 0.92 };
}
}
});
```
### 3. Expose CopilotKit Endpoints
```javascript
app.post('/copilot/generate-description', copilotKit.handleRequest('generateProductDescription'));
app.post('/copilot/analyze-sentiment', copilotKit.handleRequest('analyzeCustomerSentiment'));
```
### 4. Integrate with React Frontend
```javascript
import { CopilotKit } from '@copilotkit/react-core';
function ProductPage() {
return (
<CopilotKit>
<ProductDescriptionGenerator />
<CustomerReviewAnalyzer />
</CopilotKit>
);
}
```
## Expected Outcomes
- Product descriptions generated in 2-3 seconds
- Customer sentiment analysis with 85%+ accuracy
- 40% reduction in manual content creation time
## Next Steps
1. Test the integration with sample products
2. Monitor performance metrics
3. Scale to additional AI capabilities like chatbots and recommendation engines
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