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github/azure-devops-cli

Azure DevOps 리소스를 CLI로 관리합니다: 프로젝트, 리포지토리, 파이프라인, 빌드, PR, 작업 항목, 아티팩트, 서비스 엔드포인트 등. Azure DevOps, az 명령어, DevOps 자동화, CI/CD 작업 시 사용하세요.

azure-devops-cli란 무엇인가요?

이 스킬은 Azure DevOps CLI(`az devops` 명령어 세트)를 통해 Azure DevOps 리소스를 직접 관리할 수 있게 해줍니다. 프로젝트, 리포지토리, 파이프라인, 빌드, 풀 리퀘스트, 작업 항목, 아티팩트, 서비스 엔드포인트 등 주요 리소스를 모두 다룰 수 있어 DevOps 자동화와 CI/CD 파이프라인 구축에 유용합니다. Azure CLI가 설치된 환경에서 동작하며, `az login`을 통해 인증이 완료된 상태여야 합니다. 이 스킬은 사용자의 요청을 적절한 `az devops` 명령어로 변환하여 실행하고, 결과를 요약하여 보여줌으로써 개발자가 Azure DevOps를 더 효율적으로 운영할 수 있도록 지원합니다. Cursor, Claude Code, Codex 등 다양한 AI 코딩 에이전트와 호환됩니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/azure-devops-cli

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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

azure-devops-cli은(는) 무엇을 하나요?

Azure DevOps 리소스를 CLI로 관리합니다: 프로젝트, 리포지토리, 파이프라인, 빌드, 풀 리퀘스트, 작업 항목, 아티팩트, 서비스 엔드포인트. Azure DevOps, az 명령어, DevOps 자동화, CI/CD 관련 작업 또는 사용자가 Azure DevOps CLI를 언급할 때 사용하세요.

필수 조건

  • Azure CLI가 설치되어 있어야 합니다.
  • az login을 통해 인증이 완료되어 있어야 합니다.
  • Azure DevOps 확장(az extension add --name azure-devops)이 설치되어 있어야 합니다.
  • 기본 조직이 설정되어 있어야 합니다 (az devops configure --defaults organization=https://dev.azure.com/your-org).

사용법

다음은 주요 사용 예시입니다.

프로젝트 목록 조회

az devops project list --output table

리포지토리 생성

ez repos create --name my-repo --project my-project

파이프라인 실행

az pipelines run --name my-pipeline --project my-project

풀 리퀘스트 생성

az repos pr create --source-branch feature-branch --target-branch main --title "새 기능 추가"

빌드 결과 보기

az pipelines build show --id 12345 --project my-project

작업 항목 쿼리

ez boards query --wiql "SELECT [System.Title] FROM WorkItems WHERE [System.WorkItemType] = 'Task'"

아티팩트 게시

az pipelines artifact publish --path ./drop --artifact-name drop

서비스 엔드포인트 생성

az devops service-endpoint create --service-endpoint-configuration <config-file.json>

지원되는 명령어 그룹

  • az devops - DevOps 관련 기본 설정 및 조직 관리
  • az boards - 작업 항목 및 보드 관리
  • az repos - 리포지토리 및 풀 리퀘스트 관리
  • az pipelines - 파이프라인 및 빌드 관리
  • az artifacts - 패키지 아티팩트 관리
  • az devops service-endpoint - 서비스 연결 관리

참고 사항

  • 모든 명령어는 Azure DevOps 조직 내에서 실행됩니다. 기본 조직이 설정되지 않은 경우 --organization 플래그를 추가로 지정해야 할 수 있습니다.
  • 출력 형식은 --output 플래그로 제어할 수 있습니다 (예: json, table, tsv).
  • 자세한 도움말은 az devops -h 또는 각 명령어에 -h 플래그를 추가하여 확인할 수 있습니다.

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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