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github/power-platform-mcp-connector-suite

Power Platform 사용자 지정 커넥터를 MCP 통합 및 Copilot Studio와 함께 완전히 생성합니다. 여기에는 스키마 생성, 문제 해결 및 유효성 검사가 포함됩니다.

power-platform-mcp-connector-suite란 무엇인가요?

이 스킬은 Power Platform 사용자 지정 커넥터를 MCP(Model Context Protocol) 통합과 함께 생성하는 완전한 워크플로우를 제공합니다. Copilot Studio와의 원활한 연동을 위해 설계되었으며, 커넥터 정의를 위한 OpenAPI 스키마 생성, 문제 해결 가이드라인, 그리고 생성된 커넥터의 유효성 검사를 포함합니다. 주로 Power Platform 및 Copilot Studio 환경을 사용하는 개발자와 AI 에이전트 사용자를 대상으로 하며, 사용자 지정 API를 Power Platform에 통합하는 프로세스를 간소화합니다. 이 스킬은 효율성을 높이고 수동 구성 오류를 줄임으로써 MCP 기반 도구를 Power Platform 생태계에 연결하는 데 유용합니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/power-platform-mcp-connector-suite

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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

power-platform-mcp-connector-suite은(는) 무엇을 하나요?

Power Platform 사용자 지정 커넥터를 MCP 통합 및 Copilot Studio와 함께 완전히 생성합니다. 여기에는 스키마 생성, 문제 해결 및 유효성 검사가 포함됩니다.

개요

이 스킬은 Power Platform 사용자 지정 커넥터를 설정하고 MCP(Model Context Protocol)와 통합하며 Copilot Studio에서 사용할 수 있도록 하는 종합적인 솔루션을 제공합니다.

주요 기능

  • 스키마 생성: API 정의를 기반으로 OpenAPI 스키마를 자동 생성합니다.
  • MCP 통합: Model Context Protocol을 지원하여 AI 에이전트와의 상호 작용을 활성화합니다.
  • Copilot Studio 지원: 생성된 커넥터를 Copilot Studio에 원활하게 통합합니다.
  • 문제 해결: 일반적인 오류 및 통합 문제에 대한 가이드라인을 제공합니다.
  • 유효성 검사: 생성된 커넥터가 필요한 표준을 충족하는지 확인합니다.

사용 방법

  1. API 엔드포인트 및 인증 세부 정보를 준비합니다.
  2. 스키마 생성 프로세스를 실행하여 OpenAPI 명세서를 생성합니다.
  3. MCP 통합 구성을 적용합니다.
  4. Power Platform 환경에서 커넥터를 테스트하고 유효성을 검사합니다.
  5. Copilot Studio에 커넥터를 배포합니다.

지원 플랫폼

  • Power Platform
  • Copilot Studio
  • MCP 지원 AI 에이전트 (예: Claude Code, Cursor)

참고 사항

  • AZURE_OPENAI_API_KEY와 같은 환경 변수가 필요한 경우 적절히 설정하십시오.
  • 생성된 커넥터를 배포하기 전에 항상 유효성 검사를 수행하는 것이 좋습니다.
  • 문제 해결이 필요한 경우 공식 문서를 참조하십시오.

문제 해결

  • 오류 401: 인증 토큰이 올바르게 설정되었는지 확인합니다.
  • 스키마 생성 실패: API 응답 형식이 OpenAPI 표준을 따르는지 확인합니다.
  • MCP 연결 문제: 네트워크 구성 및 MCP 엔드포인트 URL을 확인합니다.

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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