Exposes the same production-tested engine behind Copilot CLI with support for streaming responses, custom tool definitions, and multi-turn conversations
Supports four language runtimes (Node.js 18+, Python 3.8+, Go 1.21+, .NET 8.0+) with consistent APIs across all platforms
Enables integration with MCP servers for pre-built tools, custom agent personas with sp
Fetches copilot-sdk from github/awesome-copilot and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/copilot-sdk
Restart Cursor to activate copilot-sdk. Access via /copilot-sdk in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Embed Copilot's agentic workflows in any application using Python, TypeScript, Go, or .NET.
Overview
The GitHub Copilot SDK exposes the same engine behind Copilot CLI: a production-tested agent runtime you can invoke programmatically. No need to build your own orchestration - you define agent behavior, Copilot handles planning, tool invocation, file edits, and more.
usingGitHub.Copilot.SDK;awaitusingvar client =newCopilotClient();awaitusingvar session =await client.CreateSessionAsync(newSessionConfig{ OnPermissionRequest = PermissionHandler.ApproveAll, Model ="gpt-4.1",});var response =await session.SendAndWaitAsync(newMessageOptions{ Prompt ="What is 2 + 2?"});Console.WriteLine(response?.Data.Content);
Run: dotnet run
Streaming Responses
Enable real-time output for better UX:
TypeScript
import{ CopilotClient, approveAll, SessionEvent }from"@github/copilot-sdk";const client =newCopilotClient();const session =await client.createSession({ onPermissionRequest: approveAll, model:"gpt-4.1", streaming:true,});session.on((event: SessionEvent)=>{if(event.type ==="assistant.message_delta"){ process.stdout.write(event.data.deltaContent);}if(event.type ==="session.idle"){console.log();// New line when done}});await session.sendAndWait({ prompt:"Tell me a short joke"});await client.stop();process.exit(0);
Python
import asyncio
import sys
from copilot import CopilotClient, PermissionHandler
from copilot.generated.session_events import SessionEventType
asyncdefmain(): client = CopilotClient()await client.start()
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
