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github/create-architectural-decision-record

AI 최적화 의사 결정 문서화를 위한 아키텍처 결정 기록(ADR) 문서를 생성합니다.

create-architectural-decision-record란 무엇인가요?

이 스킬은 개발자나 AI 에이전트가 중요한 아키텍처 결정 사항을 구조화된 ADR(Architectural Decision Record) 형식으로 문서화할 수 있도록 도와줍니다. ADR은 프로젝트의 기술적 의사 결정을 추적하고, 이후 참여하는 개발자 또는 AI 에이전트가 맥락을 이해하는 데 필수적인 도구입니다. 이 스킬은 GitHub 기반 워크플로우에서 사용되며, 특히 AI 어시스턴트(예: Claude Code)가 프로젝트 저장소 내에서 ADR을 생성하고 관리할 수 있게 합니다. 결정의 배경, 옵션, 최종 선택 및 결과를 포함한 템플릿을 제공하여 일관성과 가독성을 높입니다.

지원 대상~Claude Code~Codex CLI~Cursor
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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

create-architectural-decision-record은(는) 무엇을 하나요?

AI 최적화 의사 결정 문서화를 위한 아키텍처 결정 기록(ADR) 문서를 생성합니다.

개요

이 스킬을 사용하면 프로젝트 내에서 중요한 아키텍처 결정 사항을 표준화된 형식으로 기록할 수 있습니다. ADR은 결정의 맥락, 대안, 최종 선택, 그리고 그 결과를 명확하게 문서화하여 팀과 AI 시스템이 과거 결정을 이해하고 향후 결정을 내리는 데 도움을 줍니다.

사용 방법

  1. 명령 실행: github/create-architectural-decision-record 스킬을 호출합니다.
  2. 결정 내용 입력: 문서화할 아키텍처 결정에 대한 정보를 제공합니다.
  3. ADR 생성: 템플릿을 기반으로 구조화된 ADR 문서가 생성되어 저장소에 추가됩니다.

지원 플랫폼

  • GitHub 저장소
  • Claude Code와 같은 AI 어시스턴트

템플릿 구조

생성되는 ADR 문서는 일반적으로 다음 섹션을 포함합니다:

  • 제목: 결정의 요약
  • 상태: 제안됨, 승인됨, 대체됨 등
  • 맥락: 결정을 내리게 된 배경과 동기
  • 결정: 내려진 구체적인 결정 사항
  • 대안: 고려되었지만 채택되지 않은 옵션들
  • 결과: 결정으로 인한 예상 효과와 영향

예시

# ADR-001: 마이크로서비스 아키텍처 채택

상태: 승인됨

맥락: 모놀리식 애플리케이션이 확장성과 유지보수에 문제를 겪음.

결정: 마이크로서비스 아키텍처로 전환하여 각 서비스를 독립적으로 배포 및 확장.

대안: 서비스 지향 아키텍처(SOA) 유지, 서버리스 전환.

결과: 배포 자유도 향상, 팀 자율성 증가, 초기 복잡성 증가.

주의 사항

  • ADR은 간결하고 명확하게 작성되어야 합니다.
  • 각 결정은 고유한 번호를 가져야 합니다.
  • 필요시 결정을 업데이트하거나 대체할 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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