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github/typespec-create-api-plugin

为 Microsoft 365 Copilot 生成包含 REST 操作、身份验证和自适应卡片的 TypeSpec API 插件

typespec-create-api-plugin 是什么?

此技能用于快速生成 TypeSpec API 插件,专为 Microsoft 365 Copilot 设计。它帮助开发者定义 REST 操作、配置身份验证机制(如 OAuth2 或 API Key),并生成自适应卡片(Adaptive Cards)来增强 Copilot 的交互体验。支持主流 AI 开发平台(如 Claude Code、Cursor 等),通过自动化模板和最佳实践减少手动配置错误,加速从 API 设计到 Copilot 集成的流程。对于希望将企业数据或服务安全接入 Microsoft 365 Copilot 的开发者而言,这是一个高效的脚手架工具。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/typespec-create-api-plugin

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文档

typespec-create-api-plugin 是做什么的?

为 Microsoft 365 Copilot 生成包含 REST 操作、身份验证和自适应卡片的 TypeSpec API 插件。

此技能可自动创建 TypeSpec 定义,包括 REST API 操作、身份验证配置(如 OAuth2 或 API Key)以及自适应卡片模板,以便将您的服务快速集成到 Microsoft 365 Copilot 中。它遵循官方最佳实践,适用于各种 AI 开发环境。

使用场景

  • 为 Microsoft 365 Copilot 创建新的 API 插件
  • 快速原型设计 REST 操作和身份验证
  • 生成自适应卡片以增强 Copilot 对话体验

前提条件

  • 安装 TypeSpec 编译器 (tsp) 和 Node.js 18+
  • 了解 Microsoft 365 Copilot 插件概念

使用方法

在 AI 终端(如 Claude Code、Cursor 或 Codex)中运行以下命令:

npx @typespec/create-api-plugin my-copilot-plugin

此命令将生成包含以下内容的项目结构:

  • main.tsp:主 TypeSpec 文件,定义 REST 操作和模型
  • authentication.tsp:身份验证配置
  • adaptive-cards/:自适应卡片模板目录
  • tspconfig.yaml:TypeSpec 编译器配置

示例输出

生成的 main.tsp 文件示例:

import "@typespec/http";
import "@typespec/rest";

@service({
  title: "My Copilot Plugin",
})
namespace MyApi {
  model TodoItem {
    id: string;
    title: string;
    completed?: boolean;
  }

  @route("/todos")
  interface Todos {
    @get list(): TodoItem[];
    @post create(@body item: TodoItem): TodoItem;
  }
}

身份验证配置

生成的 authentication.tsp 示例:

import "@typespec/http";

@useAuth(AuthorizationCodeFlow<CredentialScopes>)
namespace AuthenticatedApi {
  model CredentialScopes {
    scopes: ["https://api.example.com/.default"];
  }
}

自适应卡片

生成的 adaptive-cards/todo-card.json 示例:

{
  "type": "AdaptiveCard",
  "version": "1.5",
  "body": [
    {
      "type": "TextBlock",
      "text": "${title}",
      "size": "Medium"
    }
  ]
}

相关资源

支持平台

  • Claude Code
  • Cursor
  • Codex
  • 任何支持 TypeScript/Node.js 的 AI 开发终端

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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