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teams-sdk-projectteams SDK project 命令行

Agent Skill

teams-sdk-project 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

267

周安装

11

GitHub Stars

公开资料未说明

下载量

87
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:teams-sdk-project(teams SDK project 命令行)
来源仓库:https://github.com/heyitsaamir/teams.ts-skills
仓库路径:skills/teams-sdk-project
安装命令:
npx skills add https://github.com/heyitsaamir/teams.ts-skills --skill teams-sdk-project
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/heyitsaamir/teams.ts-skills --skill teams-sdk-project

简介

teams-sdk-project 用于处理 GitHub 仓库、Issue 和 Pull Request。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态进行协作整理。
  • 通过 npx skills add 命令从指定仓库安装并使用。
  • 安装前需确认权限范围、维护状态及是否触发联网或文件读写。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

Teams SDK Project Scaffolding

This skill walks developers through the full end-to-end process of creating a Teams bot project in TypeScript — from dev tunnel setup through bot registration to a running, message-handling bot.

Overview

There are three phases to getting a Teams bot running:

  1. Dev Tunnel — Create a public tunnel so Teams can reach your local server
  2. Bot Registration — Use the teams2 CLI to register an Azure AD app, create a bot, and get credentials
  3. Project Scaffolding — Generate the TypeScript project files and wire everything together

Phase 1: Dev Tunnel

The bot needs a publicly accessible HTTPS endpoint. Teams sends messages to this URL. During development, a tunnel forwards traffic from a public URL to localhost:3978.

Ask the user which tunnel provider they prefer, then follow the appropriate section.

Option A: Microsoft Dev Tunnels (devtunnel)

Check if devtunnel is installed by running devtunnel --version. If not installed:

brew install microsoft/dev-tunnels/devtunnel

Check if the user is logged in with devtunnel user show. If not:

devtunnel user login

Create the tunnel — run these commands to create a named, persistent tunnel. Use the project name as the tunnel name:

devtunnel create <tunnel-name> -a
devtunnel port create <tunnel-name> -p 3978

Get the tunnel URL — run this to retrieve the endpoint URL (available immediately after creation, before hosting):

devtunnel show <tunnel-name>

Parse the "Connect via browser" URL from the output. It will look like https://<tunnel-name>-3978.<region>.devtunnels.ms. Save this — it's used as the bot endpoint in Phase 2.

Option B: ngrok

Tell the user to run ngrok in a separate terminal (long-running process):

brew install ngrok   # if not installed
ngrok http 3978

The user needs to share the forwarding URL (e.g., https://abc123.ngrok-free.app) back before proceeding.

Phase 2: Bot Registration with teams2 CLI

Check if teams2 is installed by running teams2 --version. If not installed:

npm install -g https://github.com/heyitsaamir/teamscli/releases/latest/download/teamscli.tgz

Check if the user is logged in with teams2 status. If not, run:

teams2 login

This opens a device-code authentication flow — the user needs to complete the browser authentication step.

Create the bot — run this using the tunnel URL from Phase 1. Write the .env file into the project directory:

teams2 app create -n "<project-name>" -e https://<tunnel-url>/api/messages --env <project-dir>/.env

This command automatically:

  1. Creates an Azure AD app registration
  2. Generates a client secret
  3. Registers the bot in the Teams Developer Portal
  4. Imports the app package with manifest
  5. Writes CLIENT_ID and CLIENT_SECRET to the .env file

After creation, read the CLIENT_ID from the .env file and show the Teams install link:

teams2 app view <CLIENT_ID> --web

Share this install link with the user so they can install the bot in Teams later.

Phase 3: Project Scaffolding

Ask the user which template they want:

  • Echo — Simple bot that echoes back messages. Good starting point.
  • AI — Bot with OpenAI/Azure OpenAI integration, streaming, and conversation memory. For building intelligent agents.

Then scaffold the project by creating the files described in the appropriate reference:

  • Echo template: read references/echo-template.md
  • AI template: read references/ai-template.md

Install dependencies after scaffolding:

cd <project-dir> && npm install

For the AI template, the user also needs to add their OpenAI/Azure OpenAI credentials to .env.

Getting it Running

Once everything is scaffolded, tell the user to run two commands in separate terminals:

  1. Host the tunnel (if using devtunnel): devtunnel host <tunnel-name>
  2. Start the bot: npm run dev

The bot is now live. The install link was already shown after bot registration in Phase 2 — remind the user to open it in Teams to start chatting with the bot.

Project Structure

Both templates produce this structure:

<project-name>/
├── src/
│   └── index.ts          # Bot entry point
├── .env                   # Credentials (from teams2)
├── package.json
├── tsconfig.json
└── tsup.config.js

Environment Variables Reference

Echo bot

VariableDescriptionSource
CLIENT_IDAzure AD app client IDteams2 app create
CLIENT_SECRETAzure AD app client secretteams2 app create
PORTServer port (default: 3978)Optional

AI bot (additional)

VariableDescriptionSource
OPENAI_API_KEYOpenAI API keyUser provides
AZURE_OPENAI_API_KEYAzure OpenAI API key (alternative)User provides
AZURE_OPENAI_ENDPOINTAzure OpenAI endpoint URLUser provides
AZURE_OPENAI_API_VERSIONAzure OpenAI API versionUser provides
AZURE_OPENAI_MODEL_DEPLOYMENT_NAMEAzure OpenAI deployment nameUser provides

Key SDK Concepts

The App class

The App from @microsoft/teams.apps is the main entry point. It handles HTTP server setup, authentication, and activity routing.

Event handlers

Register handlers with app.on(eventName, handler):

  • 'message' — User sends a message
  • 'install.add' — Bot is installed
  • 'message.submit.feedback' — User gives feedback on a message

Handler parameters: {send, reply, stream, activity, next, log}

  • send(activity) — Send a new message
  • reply(activity) — Reply to the current message
  • stream.emit(chunk) — Stream a response chunk
  • activity — The incoming activity/message
  • next() — Pass to the next handler (middleware pattern)
  • log — Logger instance

AI capabilities (AI template)

  • ChatPrompt from @microsoft/teams.ai — Manages LLM conversations with system instructions and message history
  • OpenAIChatModel from @microsoft/teams.openai — Connects to OpenAI or Azure OpenAI
  • .addAiGenerated() on MessageActivity — Marks response as AI-generated in Teams UI
  • Streaming via onChunk callback and stream.emit()

Plugins

  • DevtoolsPlugin from @microsoft/teams.dev — Adds development tooling and debugging support

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.41%
按下载量换算29

Claude

30.87%
按下载量换算27

Cursor

20.37%
按下载量换算18

Gemini CLI

9.38%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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