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github/create-architectural-decision-record

アーキテクチャ上の意思決定を記録するためのADR(アーキテクチャ決定記録)文書を作成します。

create-architectural-decision-record とは?

このスキルは、ソフトウェアプロジェクトにおけるアーキテクチャ上の重要な意思決定を文書化するためのADR(Architectural Decision Record)を作成します。ADRは、決定内容、その背景、トレードオフ、結果を構造化された形式で記録することで、チーム内のコミュニケーションを向上させ、後からプロジェクトに参加した開発者やAIエージェントが意思決定の経緯を理解しやすくします。このスキルはGitHub上の操作に対応しており、AIエージェント(Claude CodeやCodexなど)がプロジェクトのコンテキストに基づいてADRを自動生成することを支援します。作成される文書は、AIが読み取りやすいように最適化されており、マークダウン形式で出力されるため、バージョン管理とレビューが容易です。

対応~Claude Code~Codex CLI~Cursor
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このエージェントスキルを事前に読み込んだ状態で新しいチャットを開きます。

ドキュメント

create-architectural-decision-record は何をしますか?

AI に最適化された意思決定文書を作成するためのアーキテクチャ決定記録 (ADR) 文書を作成します。

概要

このスキルを使用すると、プロジェクト内の重要なアーキテクチャ上の決定を記録する ADR 文書を生成できます。ADR は、決定の内容、背景、結果、および検討された代替案を明確かつ簡潔に文書化するための構造化された形式を提供します。

使用法

このスキルを AI エージェントと一緒に使用するには、次のように呼び出します。

/claude create-architectural-decision-record "決定のタイトルをここに記述"

出力形式

生成された ADR 文書はマークダウン形式で出力され、次のセクションが含まれます。

  • タイトル: 決定の簡潔な名前
  • ステータス: 草案 (draft)、提案中 (proposed)、承認済み (accepted)、非推奨 (deprecated)、置き換え済み (superseded) のいずれか
  • コンテキスト: 決定が行われた背景と状況
  • 決定: 行われた具体的なアーキテクチャ上の選択
  • 結果: 決定のプラスとマイナスの影響
  • 代替案: 検討された他のオプションとそれらが却下された理由

例

# ADR-001: データベースに PostgreSQL を採用する

## ステータス

承認済み

## コンテキスト

現在、アプリケーションは SQLite を使用していますが、同時接続数の増加とデータ整合性の要件により、より堅牢なデータベースソリューションが必要です。

## 決定

主要なデータベースとして PostgreSQL 13 を採用します。

## 結果

- プラス: 同時実行性の向上、より強力な整合性保証、拡張性に優れた機能
- マイナス: 運用オーバーヘッドの増加、追加のインフラストラクチャコスト

## 代替案

- MySQL: 同様の機能を提供しますが、PostgreSQL の拡張性に劣ります
- MongoDB: ドキュメント指向モデルは現在のリレーショナルデータモデルと一致しません

ヒント

  • 決定は現在形で記述してください (「〜することにした」ではなく「〜を採用する」)
  • 決定の背後にある「なぜ」に焦点を当ててください
  • 各 ADR に一意の ID を付けてください
  • 今参照できるように関連する決定を相互参照してください

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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