copilot-sdk

Q: How do I install copilot-sdk?

Run npx skills add https://github.com/github/awesome-copilot --skill copilot-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/copilot-sdk/ (personal) or .claude/skills/copilot-sdk/ (project).

by github OfficialAI & Agents

Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.

Installs: 9.4K
Last 24h: —
Trending rank: —
Security: Warn

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/github/awesome-copilot --skill copilot-sdk

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads copilot-sdk automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·This skill provides a guide and examples for using the GitHub Copilot SDK. It introduces potential security surfaces related to automated agent actions and indirect prompt injection from untrusted inputs, but uses official vendor resources.

Socket PassNo alerts

Snyk PassLOW ·Risk: LOW · No issues

Runlayer WarnMEDIUM ·1/1 file flagged

ZeroLeaks PassNONE ·Score: 93/100 · 2 sections analyzed

SKILL.md

The instructions your agent reads when the skill is triggered.

GitHub Copilot SDK

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.

Prerequisites

GitHub Copilot access and an authenticated environment
Language runtime: Node.js ^20.19.0 or >=22.12.0, Python 3.11+, Go 1.24+, or a .NET Standard 2.0-compatible implementation
Go: GitHub Copilot CLI installed and authenticated (Installation guide)

The TypeScript, Python, and .NET packages use a bundled Copilot runtime by default, so they do not need a separate CLI installation.

Installation

Node.js/TypeScript

mkdir copilot-demo && cd copilot-demo
npm init -y --init-type module
npm install @github/copilot-sdk tsx

Python

pip install github-copilot-sdk

# Optional: pre-download the bundled runtime instead of downloading it on first use
python -m copilot download-runtime

Published Python wheels include a pinned runtime version. The pre-download command caches that runtime locally; if skipped, the SDK attempts to download it automatically on first use.

Go

mkdir copilot-demo && cd copilot-demo
go mod init copilot-demo
go get github.com/github/copilot-sdk/go

.NET

dotnet new console -n CopilotDemo && cd CopilotDemo
dotnet add package GitHub.Copilot.SDK

Quick Start

TypeScript

import { CopilotClient, approveAll } from "@github/copilot-sdk";

const client = new CopilotClient();
const session = await client.createSession({
    onPermissionRequest: approveAll,
    model: "gpt-4.1",
});

const response = await session.sendAndWait({ prompt: "What is 2 + 2?" });
console.log(response?.data.content);

await client.stop();
process.exit(0);

Run: npx tsx index.ts

Python

import asyncio
from copilot import CopilotClient, PermissionHandler

async def main():
    async with CopilotClient() as client:
        async with await client.create_session(
            on_permission_request=PermissionHandler.approve_all,
            model="gpt-4.1",
        ) as session:
            response = await session.send_and_wait("What is 2 + 2?")
            print(response.data.content)

asyncio.run(main())

Go

package main

import (
    "fmt"
    "log"
    "os"
    copilot "github.com/github/copilot-sdk/go"
)

func main() {
    client := copilot.NewClient(nil)
    if err := client.Start(); err != nil {
        log.Fatal(err)
    }
    defer client.Stop()

    session, err := client.CreateSession(&copilot.SessionConfig{
        OnPermissionRequest: copilot.PermissionHandler.ApproveAll,
        Model:               "gpt-4.1",
    })
    if err != nil {
        log.Fatal(err)
    }

    response, err := session.SendAndWait(copilot.MessageOptions{Prompt: "What is 2 + 2?"}, 0)
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(*response.Data.Content)
    os.Exit(0)
}

.NET (C#)

using GitHub.Copilot.SDK;

await using var client = new CopilotClient();
await using var session = await client.CreateSessionAsync(new SessionConfig
{
    OnPermissionRequest = PermissionHandler.ApproveAll,
    Model = "gpt-4.1",
});

var response = await session.SendAndWaitAsync(new MessageOptions { 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 = new CopilotClient();
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

async def main():
    async with CopilotClient() as client:
        async with await client.create_session(
            on_permission_request=PermissionHandler.approve_all,
            model="gpt-4.1",
            streaming=True,
        ) as session:
            def handle_event(event):
                if event.type == SessionEventType.ASSISTANT_MESSAGE_DELTA:
                    sys.stdout.write(event.data.delta_content)
                    sys.stdout.flush()
                if event.type == SessionEventType.SESSION_IDLE:
                    print()

            session.on(handle_event)
            await session.send_and_wait("Tell me a short joke")

asyncio.run(main())

Go

session, err := client.CreateSession(&copilot.SessionConfig{
	OnPermissionRequest: copilot.PermissionHandler.ApproveAll,
    Model:     "gpt-4.1",
    Streaming: true,
})

session.On(func(event copilot.SessionEvent) {
    if event.Type == "assistant.message_delta" {
        fmt.Print(*event.Data.DeltaContent)
    }
    if event.Type == "session.idle" {
        fmt.Println()
    }
})

_, err = session.SendAndWait(copilot.MessageOptions{Prompt: "Tell me a short joke"}, 0)

.NET

await using var session = await client.CreateSessionAsync(new SessionConfig
{
    OnPermissionRequest = PermissionHandler.ApproveAll,
    Model = "gpt-4.1",
    Streaming = true,
});

session.On(ev =>
{
    if (ev is AssistantMessageDeltaEvent deltaEvent)
        Console.Write(deltaEvent.Data.DeltaContent);
    if (ev is SessionIdleEvent)
        Console.WriteLine();
});

await session.SendAndWaitAsync(new MessageOptions { Prompt = "Tell me a short joke" });

Custom Tools

Define tools that Copilot can invoke during reasoning. When you define a tool, you tell Copilot:

What the tool does (description)
What parameters it needs (schema)
What code to run (handler)

TypeScript (JSON Schema)

import { CopilotClient, approveAll, defineTool, SessionEvent } from "@github/copilot-sdk";

const getWeather = defineTool("get_weather", {
    description: "Get the current weather for a city",
    parameters: {
        type: "object",
        properties: {
            city: { type: "string", description: "The city name" },
        },
        required: ["city"],
    },
    handler: async (args: { city: string }) => {
        const { city } = args;
        // In a real app, call a weather API here
        const conditions = ["sunny", "cloudy", "rainy", "partly cloudy"];
        const temp = Math.floor(Math.random() * 30) + 50;
        const condition = conditions[Math.floor(Math.random() * conditions.length)];
        return { city, temperature: `${temp}°F`, condition };
    },
});

const client = new CopilotClient();
const session = await client.createSession({
    onPermissionRequest: approveAll,
    model: "gpt-4.1",
    streaming: true,
    tools: [getWeather],
});

session.on((event: SessionEvent) => {
    if (event.type === "assistant.message_delta") {
        process.stdout.write(event.data.deltaContent);
    }
});

await session.sendAndWait({
    prompt: "What's the weather like in Seattle and Tokyo?",
});

await client.stop();
process.exit(0);

Python (Pydantic)

import asyncio
import random
import sys
from copilot import CopilotClient, PermissionHandler
from copilot.tools import define_tool
from copilot.generated.session_events import SessionEventType
from pydantic import BaseModel, Field

class GetWeatherParams(BaseModel):
    city: str = Field(description="The name of the city to get weather for")

@define_tool(description="Get the current weather for a city")
async def get_weather(params: GetWeatherParams) -> dict:
    city = params.city
    conditions = ["sunny", "cloudy", "rainy", "partly cloudy"]
    temp = random.randint(50, 80)
    condition = random.choice(conditions)
    return {"city": city, "temperature": f"{temp}°F", "condition": condition}

async def main():
    async with CopilotClient() as client:
        async with await client.create_session(
            on_permission_request=PermissionHandler.approve_all,
            model="gpt-4.1",
            streaming=True,
            tools=[get_weather],
        ) as session:
            def handle_event(event):
                if event.type == SessionEventType.ASSISTANT_MESSAGE_DELTA:
                    sys.stdout.write(event.data.delta_content)
                    sys.stdout.flush()

            session.on(handle_event)
            await session.send_and_wait(
                "What's the weather like in Seattle and Tokyo?"
            )

asyncio.run(main())

Go

type WeatherParams struct {
    City string `json:"city" jsonschema:"The city name"`
}

type WeatherResult struct {
    City        string `json:"city"`
    Temperature string `json:"temperature"`
    Condition   string `json:"condition"`
}

getWeather := copilot.DefineTool(
    "get_weather",
    "Get the current weather for a city",
    func(params WeatherParams, inv copilot.ToolInvocation) (WeatherResult, error) {
        conditions := []string{"sunny", "cloudy", "rainy", "partly cloudy"}
        temp := rand.Intn(30) + 50
        condition := conditions[rand.Intn(len(conditions))]
        return WeatherResult{
            City:        params.City,
            Temperature: fmt.Sprintf("%d°F", temp),
            Condition:   condition,
        }, nil
    },
)

session, _ := client.CreateSession(&copilot.SessionConfig{
	OnPermissionRequest: copilot.PermissionHandler.ApproveAll,
    Model:     "gpt-4.1",
    Streaming: true,
    Tools:     []copilot.Tool{getWeather},
})

.NET (Microsoft.Extensions.AI)

using GitHub.Copilot.SDK;
using Microsoft.Extensions.AI;
using System.ComponentModel;

var getWeather = AIFunctionFactory.Create(
    ([Description("The city name")] string city) =>
    {
        var conditions = new[] { "sunny", "cloudy", "rainy", "partly cloudy" };
        var temp = Random.Shared.Next(50, 80);
        var condition = conditions[Random.Shared.Next(conditions.Length)];
        return new { city, temperature = $"{temp}°F", condition };
    },
    "get_weather",
    "Get the current weather for a city"
);

await using var session = await client.CreateSessionAsync(new SessionConfig
{
    OnPermissionRequest = PermissionHandler.ApproveAll,
    Model = "gpt-4.1",
    Streaming = true,
    Tools = [getWeather],
});

How Tools Work

When Copilot decides to call your tool:

Copilot sends a tool call request with the parameters
The SDK runs your handler function
The result is sent back to Copilot
Copilot incorporates the result into its response

Copilot decides when to call your tool based on the user's question and your tool's description.

Interactive CLI Assistant

Build a complete interactive assistant:

TypeScript

import { CopilotClient, approveAll, defineTool, SessionEvent } from "@github/copilot-sdk";
import * as readline from "readline";

const getWeather = defineTool("get_weather", {
    description: "Get the current weather for a city",
    parameters: {
        type: "object",
        properties: {
            city: { type: "string", description: "The city name" },
        },
        required: ["city"],
    },
    handler: async ({ city }) => {
        const conditions = ["sunny", "cloudy", "rainy", "partly cloudy"];
        const temp = Math.floor(Math.random() * 30) + 50;
        const condition = conditions[Math.floor(Math.random() * conditions.length)];
        return { city, temperature: `${temp}°F`, condition };
    },
});

const client = new CopilotClient();
const session = await client.createSession({
    onPermissionRequest: approveAll,
    model: "gpt-4.1",
    streaming: true,
    tools: [getWeather],
});

session.on((event: SessionEvent) => {
    if (event.type === "assistant.message_delta") {
        process.stdout.write(event.data.deltaContent);
    }
});

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
});

console.log("Weather Assistant (type 'exit' to quit)");
console.log("Try: 'What's the weather in Paris?'\n");

const prompt = () => {
    rl.question("You: ", async (input) => {
        if (input.toLowerCase() === "exit") {
            await client.stop();
            rl.close();
            return;
        }

        process.stdout.write("Assistant: ");
        await session.sendAndWait({ prompt: input });
        console.log("\n");
        prompt();
    });
};

prompt();

Python

import asyncio
import random
import sys
from copilot import CopilotClient, PermissionHandler
from copilot.tools import define_tool
from copilot.generated.session_events import SessionEventType
from pydantic import BaseModel, Field

class GetWeatherParams(BaseModel):
    city: str = Field(description="The name of the city to get weather for")

@define_tool(description="Get the current weather for a city")
async def get_weather(params: GetWeatherParams) -> dict:
    conditions = ["sunny", "cloudy", "rainy", "partly cloudy"]
    temp = random.randint(50, 80)
    condition = random.choice(conditions)
    return {"city": params.city, "temperature": f"{temp}°F", "condition": condition}

async def main():
    async with CopilotClient() as client:
        async with await client.create_session(
            on_permission_request=PermissionHandler.approve_all,
            model="gpt-4.1",
            streaming=True,
            tools=[get_weather],
        ) as session:
            def handle_event(event):
                if event.type == SessionEventType.ASSISTANT_MESSAGE_DELTA:
                    sys.stdout.write(event.data.delta_content)
                    sys.stdout.flush()

            session.on(handle_event)

            print("Weather Assistant (type 'exit' to quit)")
            print("Try: 'What's the weather in Paris?'\n")

            while True:
                try:
                    user_input = input("You: ")
                except EOFError:
                    break

                if user_input.lower() == "exit":
                    break

                sys.stdout.write("Assistant: ")
                await session.send_and_wait(user_input)
                print("\n")

asyncio.run(main())

MCP Server Integration

Connect to MCP (Model Context Protocol) servers for pre-built tools. Connect to GitHub's MCP server for repository, issue, and PR access:

TypeScript

const session = await client.createSession({
    onPermissionRequest: approveAll,
    model: "gpt-4.1",
    mcpServers: {
        github: {
            type: "http",
            url: "https://api.githubcopilot.com/mcp/",
        },
    },
});

Python

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-4.1",
    mcp_servers={
        "github": {
            "type": "http",
            "url": "https://api.githubcopilot.com/mcp/",
        },
    },
) as session:
    ...

Go

session, _ := client.CreateSession(&copilot.SessionConfig{
	OnPermissionRequest: copilot.PermissionHandler.ApproveAll,
    Model: "gpt-4.1",
    MCPServers: map[string]copilot.MCPServerConfig{
        "github": {
            "type": "http",
            "url": "https://api.githubcopilot.com/mcp/",
        },
    },
})

.NET

await using var session = await client.CreateSessionAsync(new SessionConfig
{
    OnPermissionRequest = PermissionHandler.ApproveAll,
    Model = "gpt-4.1",
    McpServers = new Dictionary<string, McpServerConfig>
    {
        ["github"] = new McpServerConfig
        {
            Type = "http",
            Url = "https://api.githubcopilot.com/mcp/",
        },
    },
});

Custom Agents

Define specialized AI personas for specific tasks:

TypeScript

const session = await client.createSession({
    onPermissionRequest: approveAll,
    model: "gpt-4.1",
    customAgents: [{
        name: "pr-reviewer",
        displayName: "PR Reviewer",
        description: "Reviews pull requests for best practices",
        prompt: "You are an expert code reviewer. Focus on security, performance, and maintainability.",
    }],
});

Python

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-4.1",
    custom_agents=[{
        "name": "pr-reviewer",
        "display_name": "PR Reviewer",
        "description": "Reviews pull requests for best practices",
        "prompt": "You are an expert code reviewer. Focus on security, performance, and maintainability.",
    }],
) as session:
    ...

System Message

Customize the AI's behavior and personality:

TypeScript

const session = await client.createSession({
    onPermissionRequest: approveAll,
    model: "gpt-4.1",
    systemMessage: {
        content: "You are a helpful assistant for our engineering team. Always be concise.",
    },
});

Python

async with await client.create_session(
    on_permission_request=PermissionHandler.approve_all,
    model="gpt-4.1",
    system_message={
        "content": "You are a helpful assistant for our engineering team. Always be concise.",
    },
) as session:
    ...

External CLI Server

Run the CLI in server mode separately and connect the SDK to it. Useful for debugging, resource sharing, or custom environments.

Start CLI in Server Mode

copilot --server --port 4321

Connect SDK to External Server

TypeScript

const client = new CopilotClient({
    cliUrl: "localhost:4321"
});

const session = await client.createSession({
    onPermissionRequest: approveAll,
    model: "gpt-4.1",
});

Python

from copilot import CopilotClient, PermissionHandler, RuntimeConnection

async with CopilotClient(
    connection=RuntimeConnection.for_uri("localhost:4321")
) as client:
    async with await client.create_session(
        on_permission_request=PermissionHandler.approve_all,
        model="gpt-4.1",
    ) as session:
        ...

Go

client := copilot.NewClient(&copilot.ClientOptions{
    CLIUrl: "localhost:4321",
})

if err := client.Start(); err != nil {
    log.Fatal(err)
}

session, _ := client.CreateSession(&copilot.SessionConfig{
	OnPermissionRequest: copilot.PermissionHandler.ApproveAll,
	Model:               "gpt-4.1",
})

.NET

using var client = new CopilotClient(new CopilotClientOptions
{
    CliUrl = "localhost:4321"
});

await using var session = await client.CreateSessionAsync(new SessionConfig
{
    OnPermissionRequest = PermissionHandler.ApproveAll,
    Model = "gpt-4.1",
});

Note: When configured to use an external server, the SDK manages only its connection and does not manage the external process.

Event Types

Event	Description
`user.message`	User input added
`assistant.message`	Complete model response
`assistant.message_delta`	Streaming response chunk
`assistant.reasoning`	Model reasoning (model-dependent)
`assistant.reasoning_delta`	Streaming reasoning chunk
`tool.execution_start`	Tool invocation started
`tool.execution_complete`	Tool execution finished
`session.idle`	No active processing
`session.error`	Error occurred

Client Configuration

Option	Description	Default
`cliPath`	Path to Copilot CLI executable	System PATH
`cliUrl`	Connect to existing server (e.g., "localhost:4321")	None
`port`	Server communication port	Random
`useStdio`	Use stdio transport instead of TCP	true
`logLevel`	Logging verbosity	"info"
`autoStart`	Launch server automatically	true
`autoRestart`	Restart on crashes	true
`cwd`	Working directory for CLI process	Inherited

Session Configuration

Option	Description
`model`	LLM to use ("gpt-4.1", "claude-sonnet-4.5", etc.)
`sessionId`	Custom session identifier
`tools`	Custom tool definitions
`mcpServers`	MCP server connections
`customAgents`	Custom agent personas
`systemMessage`	Override default system prompt
`streaming`	Enable incremental response chunks
`availableTools`	Whitelist of permitted tools
`excludedTools`	Blacklist of disabled tools

Session Persistence

Save and resume conversations across restarts:

Create with Custom ID

const session = await client.createSession({
    onPermissionRequest: approveAll,
    sessionId: "user-123-conversation",
    model: "gpt-4.1"
});

Resume Session

const session = await client.resumeSession("user-123-conversation", { onPermissionRequest: approveAll });
await session.send({ prompt: "What did we discuss earlier?" });

List and Delete Sessions

const sessions = await client.listSessions();
await client.deleteSession("old-session-id");

Error Handling

try {
    const client = new CopilotClient();
    const session = await client.createSession({
        onPermissionRequest: approveAll,
        model: "gpt-4.1",
    });
    const response = await session.sendAndWait(
        { prompt: "Hello!" },
        30000 // timeout in ms
    );
} catch (error) {
    if (error.code === "ENOENT") {
        console.error("Copilot CLI not installed");
    } else if (error.code === "ECONNREFUSED") {
        console.error("Cannot connect to Copilot server");
    } else {
        console.error("Error:", error.message);
    }
} finally {
    await client.stop();
}

Graceful Shutdown

process.on("SIGINT", async () => {
    console.log("Shutting down...");
    await client.stop();
    process.exit(0);
});

Common Patterns

Multi-turn Conversation

const session = await client.createSession({
    onPermissionRequest: approveAll,
    model: "gpt-4.1",
});

await session.sendAndWait({ prompt: "My name is Alice" });
await session.sendAndWait({ prompt: "What's my name?" });
// Response: "Your name is Alice"

File Attachments

await session.send({
    prompt: "Analyze this file",
    attachments: [{
        type: "file",
        path: "./data.csv",
        displayName: "Sales Data"
    }]
});

Abort Long Operations

const timeoutId = setTimeout(() => {
    session.abort();
}, 60000);

session.on((event) => {
    if (event.type === "session.idle") {
        clearTimeout(timeoutId);
    }
});

Available Models

Query available models at runtime:

const models = await client.getModels();
// Returns: ["gpt-4.1", "gpt-4o", "claude-sonnet-4.5", ...]

Best Practices

Always clean up: Use language-native context managers or disposal, or explicitly disconnect sessions and stop clients
Set timeouts: Use sendAndWait with timeout for long operations
Handle events: Subscribe to error events for robust error handling
Use streaming: Enable streaming for better UX on long responses
Persist sessions: Use custom session IDs for multi-turn conversations
Define clear tools: Write descriptive tool names and descriptions

Architecture

Your Application
       |
  SDK Client
       | JSON-RPC
  Copilot CLI (server mode)
       |
  GitHub (models, auth)

The SDK manages the CLI process lifecycle automatically. All communication happens via JSON-RPC over stdio or TCP.

Resources

GitHub Repository: https://github.com/github/copilot-sdk
Getting Started Tutorial: https://github.com/github/copilot-sdk/blob/main/docs/tutorials/first-app.md
GitHub MCP Server: https://github.com/github/github-mcp-server
MCP Servers Directory: https://github.com/modelcontextprotocol/servers
Cookbook: https://github.com/github/copilot-sdk/tree/main/cookbook
Samples: https://github.com/github/copilot-sdk/tree/main/samples

Status

This SDK is in Technical Preview and may have breaking changes. Not recommended for production use yet.

FAQ

What does the copilot-sdk skill do?

How do I install copilot-sdk?

Run npx skills add https://github.com/github/awesome-copilot --skill copilot-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/copilot-sdk/ (personal) or .claude/skills/copilot-sdk/ (project).

Which AI agents can use copilot-sdk?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is copilot-sdk safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains copilot-sdk?

It is published by github in github/awesome-copilot, and it is an official skill from the maker of the technology it covers. It has 9,396 recorded installs.

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excalidraw-diagram-generator

github/awesome-copilot

Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.

OfficialDocs & Writing31.9K installs

multi-stage-dockerfile

github/awesome-copilot

Create optimized multi-stage Dockerfiles for any language or framework

Official29.8K installs

documentation-writer

github/awesome-copilot

Diátaxis Documentation Expert. An expert technical writer specializing in creating high-quality software documentation, guided by the principles and structure of the Diátaxis technical documentation authoring framework.

OfficialDocs & Writing27.9K installs

prd

github/awesome-copilot

Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.

Official24.1K installs

refactor

github/awesome-copilot

Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.

OfficialCode Quality & Workflow22.4K installs

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google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs

copilot-sdk

Install

Install

Describe the task

Review the result

Security audits

SKILL.md

GitHub Copilot SDK

Overview

Prerequisites

Installation

Node.js/TypeScript

Python

Go

.NET

Quick Start

TypeScript

Python

Go

.NET (C#)

Streaming Responses

TypeScript

Python

Go

.NET

Custom Tools

TypeScript (JSON Schema)

Python (Pydantic)

Go

.NET (Microsoft.Extensions.AI)

How Tools Work

Interactive CLI Assistant

TypeScript

Python

MCP Server Integration

TypeScript

Python

Go

.NET

Custom Agents

TypeScript

Python

System Message

TypeScript

Python

External CLI Server

Start CLI in Server Mode

Connect SDK to External Server

TypeScript

Python

Go

.NET

Event Types

Client Configuration

Session Configuration

Session Persistence

Create with Custom ID

Resume Session

List and Delete Sessions

Error Handling

Graceful Shutdown

Common Patterns

Multi-turn Conversation

File Attachments

Abort Long Operations

Available Models

Best Practices

Architecture

Resources

Status

FAQ

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