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github/typespec-create-agent

Microsoft 365 Copilot용 완전한 TypeSpec 선언적 에이전트를 지침, 기능, 대화 시작점과 함께 생성합니다.

typespec-create-agent란 무엇인가요?

이 스킬은 개발자가 Microsoft 365 Copilot용 선언적 에이전트를 신속하게 생성할 수 있도록 TypeSpec 템플릿을 제공합니다. TypeSpec은 에이전트의 동작, 지침, 기능 및 대화 시작점을 명확하고 구조화된 방식으로 정의하는 데 사용되는 선언적 언어입니다. 이 스킬을 사용하면 Copilot이 특정 작업을 수행하거나 도메인 지식을 제공하는 맞춤형 에이전트를 정의할 수 있으며, JSON 또는 YAML보다 더 간결하고 오류 가능성이 적은 TypeSpec 문법을 활용합니다. 주로 Microsoft 365 Copilot 환경에서 작동하며, 에이전트 개발 프로세스를 간소화하고 표준화하는 데 중점을 둡니다. TypeSpec 파일을 생성한 후에는 이를 Copilot Studio 또는 다른 배포 파이프라인에 통합하여 실제 Copilot 인스턴스에 배포할 수 있습니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/typespec-create-agent

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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

typespec-create-agent은(는) 무엇을 하나요?

Microsoft 365 Copilot용 완전한 TypeSpec 선언적 에이전트를 지침, 기능, 대화 시작점과 함께 생성합니다.

개요

이 스킬은 TypeSpec을 사용하여 Microsoft 365 Copilot용 선언적 에이전트를 생성하는 프로세스를 간소화합니다. TypeSpec을 사용하면 에이전트의 동작, 기능 및 대화 상호 작용을 선언적 방식으로 정의할 수 있으므로 수동 구성보다 더 쉽게 유지 관리하고 확장할 수 있습니다.

시작하기

필수 조건

  • TypeSpec CLI 설치 (npm install -g @typespec/compiler)
  • Microsoft 365 Copilot Studio 액세스 (선택 사항, 배포용)

사용법

  1. 새 TypeSpec 프로젝트를 만듭니다:

    tsp init
    
  2. 선언적 에이전트 템플릿을 생성합니다:

    tsp generate github/typespec-create-agent
    
  3. 생성된 agent.tsp 파일을 편집하여 에이전트를 사용자 지정합니다.

TypeSpec 선언적 에이전트 구조

생성된 템플릿은 다음 섹션을 포함합니다:

  • 패키지 및 모델 가져오기
  • 에이전트 지침 (model Instruction)
  • 기능 정의 (model Capability)
  • 대화 시작점 (model ConversationStarter)
  • 에이전트 구성 (model AgentConfig)

예제

import "@typespec/openapi";

model CustomerSupportAgent {
  name: "Customer Support Agent";
  instructions: "고객 문의에 친절하고 효율적으로 응답합니다.";
  capabilities: ["ticket-management", "faq-lookup", "escalation-handling"];
  conversationStarters: [
    "주문 상태를 확인할 수 있나요?",
    "환불 정책이 어떻게 되나요?"
  ];
}

지원 플랫폼

  • Microsoft 365 Copilot Studio – TypeSpec 파일을 가져와서 Copilot 에이전트를 생성합니다.
  • Azure AI Foundry – TypeSpec 구성을 지원하는 AI 에이전트 배포 플랫폼.

추가 자료

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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