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github/typespec-create-api-plugin

生成一個包含 REST 操作、驗證機制及適用於 Microsoft 365 Copilot 的 Adaptive Cards 的 TypeSpec API 外掛程式

typespec-create-api-plugin 是什麼?

此技能可用於產生一個完整的 TypeSpec API 外掛程式,專為 Microsoft 365 Copilot 設計。它會自動建立 REST API 操作(如 GET、POST)、支援多種驗證方式(例如 API 金鑰、OAuth2),並整合 Adaptive Cards 以在 Copilot 對話中呈現豐富的互動式卡片。此技能主要支援開發者使用 TypeSpec 語言快速定義 API 規格,並一鍵生成對應的外掛程式程式碼,大幅縮短從設計到部署的流程。適用於 Visual Studio Code 及命令行環境,並可與 Azure 服務等後端整合。透過此技能,開發者能專注於業務邏輯,而無需手動處理重複性的 API 外掛程式架構設定。

相容平台~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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說明文件

typespec-create-api-plugin 是做什麼的?

生成一個 TypeSpec API 外掛程式,其中包含 REST 操作、驗證機制及適用於 Microsoft 365 Copilot 的 Adaptive Cards。

功能

  • 自動產生 REST API 操作(GET、POST、PUT、DELETE)
  • 支援多種驗證方式(API 金鑰、OAuth2、Bearer Token)
  • 整合 Adaptive Cards,讓 Copilot 對話更豐富
  • 使用 TypeSpec 定義 API 規格
  • 一鍵生成外掛程式程式碼

需求

  • Node.js (16.0.0 或更新版本)
  • TypeSpec CLI
  • 有效的 Microsoft 365 Copilot 開發環境

安裝

npm install -g @typespec/compiler

使用方法

  1. 建立新的 TypeSpec 專案:
mkdir my-api-plugin
cd my-api-plugin
tsp init
  1. 執行此技能:
# 使用 Cursor 或 Codex 等工具呼叫
  1. 遵循提示指定 API 名稱、端點及驗證方式。

  2. 產生外掛程式檔案。

輸出

技能會產生以下檔案:

  • main.tsp - TypeSpec 規格定義
  • plugin.json - Copilot 外掛程式設定
  • adaptiveCards/ - Adaptive Cards 範本
  • operations/ - REST 操作實作

驗證支援

方式說明
API 金鑰透過 x-api-key 標頭傳遞
OAuth2支援授權碼流程
Bearer TokenJWT 或自訂 Token

自訂 Adaptive Cards

可在 adaptiveCards/ 目錄中修改範本以符合您的使用案例。如需詳細資訊,請參閱 Adaptive Cards 文件。

疑難排解

  • 確保 TypeSpec CLI 已正確安裝:tsp --version
  • 驗證 Node.js 版本:node --version
  • 檢查目標端點是否可存取

授權

MIT

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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