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github/breakdown-plan

이슈 계획 및 자동화 프롬프트로, 에픽 > 기능 > 스토리/인에이블러 > 테스트 계층 구조, 종속성, 우선순위 및 자동 추적이 포함된 포괄적인 프로젝트 계획을 생성합니다.

breakdown-plan란 무엇인가요?

github/breakdown-plan은 개발 팀이 프로젝트 계획을 체계적으로 수립하고 자동화할 수 있도록 설계된 AI 에이전트 스킬입니다. 이 스킬은 에픽(Epic), 기능(Feature), 스토리/인에이블러(Story/Enabler), 테스트(Test)로 구성된 계층 구조를 기반으로 프로젝트를 세분화하여, 각 작업 간의 종속성과 우선순위를 명확히 정의합니다. GitHub Issues, Projects, Jira 등 다양한 이슈 트래킹 플랫폼과 통합되어 작동하며, CLI 명령어를 통해 AI 에이전트(예: Claude Code, Cursor, Codex)가 직접 프로젝트 계획을 생성하고 관리할 수 있습니다. 이를 통해 팀은 복잡한 프로젝트를 더 쉽게 시각화하고, 작업 할당 및 진행 상황을 자동으로 추적하여 생산성을 크게 향상시킬 수 있습니다. 또한, 자동화 워크플로우를 지원하여 새로운 이슈 생성, 상태 업데이트, 리마인더 설정과 같은 반복 작업을 간소화합니다.

지원 대상~Claude Code~Codex CLI~Cursor
Part ofAgent Workflows
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/breakdown-plan

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원본 보기→모든 스킬 둘러보기

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

문서

breakdown-plan은(는) 무엇을 하나요?

이슈 계획 및 자동화 프롬프트로, 에픽 > 기능 > 스토리/인에이블러 > 테스트 계층 구조, 종속성, 우선순위 및 자동 추적이 포함된 포괄적인 프로젝트 계획을 생성합니다.

개요

github/breakdown-plan은 개발 팀이 프로젝트를 체계적으로 분해하고 관리할 수 있도록 돕는 AI 에이전트 스킬입니다. 이 스킬은 에픽(Epic), 기능(Feature), 스토리/인에이블러(Story/Enabler), 테스트(Test)로 구성된 계층 구조를 자동으로 생성하여 프로젝트의 모든 측면을 명확하게 정의합니다.

주요 기능

  • 계층적 이슈 분해: 복잡한 프로젝트를 관리 가능한 단위로 분해합니다.
  • 종속성 추적: 작업 간의 관계를 자동으로 식별하고 시각화합니다.
  • 우선순위 설정: 각 작업에 우선순위를 할당하여 중요한 작업에 집중할 수 있습니다.
  • 자동 워크플로우: 새로운 이슈 생성, 상태 업데이트, 리마인더 설정 등을 자동화합니다.
  • 플랫폼 통합: GitHub Issues, Projects, Jira 등과 원활하게 통합됩니다.

사용 방법

  1. AI 에이전트(예: Claude Code, Cursor, Codex)에서 이 스킬을 활성화합니다.
  2. 프로젝트 설명이나 목표를 제공합니다.
  3. 스킬이 자동으로 계획을 생성하고 이슈를 설정합니다.
  4. 필요에 따라 계획을 검토하고 조정합니다.

예시 CLI 명령어

# 프로젝트 계획 생성
github/breakdown-plan create --name "새로운 기능 개발" --description "사용자 인증 시스템 구축"

# 기존 이슈 기반으로 계획 확장
github/breakdown-plan extend --issue-id 42

구성 요소

  • 에픽(Epic): 대규모 작업 단위로, 여러 기능을 포함함.
  • 기능(Feature): 특정 기능을 구현하기 위한 작업 그룹.
  • 스토리(Story): 사용자 관점에서의 요구 사항.
  • 인에이블러(Enabler): 기술적 작업이나 연구 활동.
  • 테스트(Test): 품질 보증을 위한 테스트 케이스.

환경 변수

  • GITHUB_TOKEN: GitHub API 접근을 위한 토큰
  • JIRA_API_TOKEN: Jira API 접근을 위한 토큰

지원 및 기여

이 스킬에 대한 문의나 기여는 GitHub 저장소에서 확인하세요.

라이선스

MIT 라이선스

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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