FlowStudio MCP 서버를 사용하여 실패하는 Power Automate 클라우드 흐름을 디버그합니다. Graph API는 상위 상태 코드만 표시하지만, 이 스킬은 에이전트가 작업 수준의 입력 및 출력을 확인하여 실제 근본 원인을 찾을 수 있도록 합니다.
이 스킬은 FlowStudio MCP 서버를 기반으로 Power Automate 클라우드 흐름의 실패 원인을 작업 단위로 진단할 수 있는 AI 에이전트 도구입니다. Microsoft Graph API는 흐름 실행 시 상위 수준의 상태 코드(예: 실패/성공)만 제공하지만, 실제 오류는 개별 작업의 입력/출력 값, 커넥터 인증 오류, 표현식 실패 등에 숨어 있습니다. 이 스킬은 `actionOutputs` 및 `actionInputs` 같은 특정 도구를 호출하여 각 작업의 세부 데이터를 가져오고, `DynamicOperationRequestFailure`나 타임아웃 같은 구체적인 오류 메시지를 추적할 수 있게 해줍니다. FlowStudio MCP 구독이 필요하며, Claude Code, Cursor, Codex 등 MCP를 지원하는 모든 플랫폼에서 사용할 수 있습니다. 개발자는 '이 흐름이 왜 실패했는지', '액션 출력을 검사해줘', '표현식 오류를 찾아줘' 같은 자연어 명령으로 에이전트에게 디버그를 요청할 수 있습니다.
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-debugInstalled? Explore more 생산성 & 협업 skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
FlowStudio MCP 서버를 사용하여 실패하는 Power Automate 클라우드 흐름을 디버그합니다. Graph API는 상위 상태 코드만 표시하지만, 이 스킬은 에이전트가 작업 수준의 입력 및 출력을 확인하여 실제 근본 원인을 찾을 수 있도록 합니다. 다음 요청을 받았을 때 이 스킬을 로드하세요: 흐름 디버그, 실패한 실행 조사, 흐름이 실패하는 이유, 작업 출력 검사, 흐름 오류의 근본 원인 찾기, 고장난 Power Automate 흐름 수정, 타임아웃 진단, DynamicOperationRequestFailure 추적, 커넥터 인증 오류 확인, 실행에서 오류 세부 정보 읽기, 표현식 실패 문제 해결. FlowStudio MCP 구독이 필요합니다 — 자세한 내용은 https://mcp.flowstudio.app 를 참조하세요.
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.