FlowStudio MCP 서버를 사용하여 Power Automate 클라우드 플로우를 구축, 스캐폴딩 및 배포합니다. 에이전트가 포털을 열지 않고도 플로우 정의 작성, 연결 연결, 배포 및 테스트를 모두 MCP를 통해 수행합니다.
이 스킬은 개발자와 AI 에이전트가 FlowStudio MCP 서버를 통해 Power Automate 클라우드 플로우를 완전히 코드 기반으로 생성, 수정, 배포할 수 있도록 지원합니다. 사용자는 포털에 접속하지 않고도 플로우 정의를 처음부터 작성하거나, 기존 플로우의 작업을 업데이트하거나, 연결을 연결하고 배포를 테스트할 수 있습니다. 주요 사용 사례로는 새 플로우 생성, 워크플로우 JSON 조립, 기존 정의 수정, 또는 특정 작업 추가 등이 있으며, 이 모든 작업은 MCP 프로토콜을 통해 이루어집니다. 이 스킬을 사용하려면 FlowStudio MCP 구독이 필요하며, 자세한 내용은 https://mcp.flowstudio.app에서 확인할 수 있습니다.
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-buildInstalled? Explore more 생산성 & 협업 skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
FlowStudio MCP 서버를 사용하여 Power Automate 클라우드 플로우를 구축, 스캐폴딩 및 배포합니다. 에이전트가 포털을 열지 않고도 플로우 정의를 작성하고, 연결을 연결하며, 배포하고, 테스트합니다. 다음 요청이 있을 때 이 스킬을 로드하세요: 플로우 생성, 새 플로우 빌드, 플로우 정의 배포, Power Automate 워크플로우 스캐폴딩, 플로우 JSON 구성, 기존 플로우의 작업 업데이트, 플로우 정의 패치, 플로우에 작업 추가, 연결 연결, 또는 처음부터 워크플로우 정의 생성. FlowStudio MCP 구독이 필요합니다 — https://mcp.flowstudio.app 참조
This repo contains 20 individual skills — each has its own dedicated page.
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.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
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)
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
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"
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.
Instrument a webapp to send useful telemetry data to Azure App Insights
Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.
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.
Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.