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Turn descriptions or code analysis into Mermaid diagrams for workflows, system architecture, and database relationships. Check diagram syntax and export SVG, PNG, or text-based ASCII output, with themes and batch rendering options.
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Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.
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Design and review system architecture, compare databases and structural approaches, and document technical decisions. Included tools generate architecture diagrams, inspect dependencies, and assess project structure to identify circular relationships, excessive coupling, and opportunities for improvement.
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Turn written descriptions into editable Excalidraw diagrams, including flowcharts, mind maps, system architecture, and database relationships. Generate a .excalidraw JSON file with connected shapes, labels, and a consistent sketch or clean visual style.
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Architecture Decision Framework workflow skill. Use this skill when the user needs Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Make system architecture decisions by clarifying requirements and constraints, comparing benefits and drawbacks, and considering simpler alternatives. Record the reasoning in ADRs, architecture decision records, so significant choices and their rationale are documented.
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Design Azure infrastructure from plain language or visualize existing resources as interactive diagrams. Refine the architecture through conversation, then generate Bicep infrastructure code, review it, and validate changes before deployment.
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Design a Google Cloud system for data analysis or machine learning with AI agents. Turn requirements into product choices, an architecture diagram, infrastructure configuration, deployment instructions, and validation steps, with user reviews between phases.
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Architecture Decision Framework workflow skill. Use this skill when the user needs Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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コードベースのアーキテクチャを 6 つの専門エージェントチーム（Claude 5体 + Codex 1体）で 多角的に分析し、リスク・デグレしやすい箇所・改善ポイントを洗い出すスキル。 パフォーマンス、スケーラビリティ、信頼性、セキュリティ、運用、DX、データ整合性、依存関係の 8 観点を 5 グループに分担し、さらに Codex がクロスカットレビュアーとして横断分析する。 エージェント同士が反論・補足・自由な議論を行い、合意形成した結果をレポートとして出力する。 どのプロジェクトでも使える汎用スキル。 Use when: アーキテクチャを分析したい、リファクタリング前にリスクを把握したい、 デグレしやすい箇所を知りたい、コードの健全性を確認したい、大きめの実装前に注意点を洗い出したい、 技術的負債を可視化したい、コードベースの品質を監査したい。 Triggers: "arch review", "アーキテクチャ分析", "アーキテクチャレビュー", "リスク分析", "コード監査", "codebase audit", "技術的負債", "デグレしやすい", "architecture analysis", "risk analysis", "コードの健全性", "品質チェック", "実装前の確認"
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Understand software architecture, trace request flows, and assess the impact of code changes using Ontoly’s graph of software relationships. Answers include supporting graph evidence, confidence, and relevant limitations.
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Organize existing code components into business areas to prepare for service separation. Produce domain maps, component assignment tables, and refactoring plans that align namespaces, the naming structure used to organize code, with business responsibilities.
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Make AI truly understand large codebases: for multi-repository, multi-microservice projects that have evolved over years, run architecture reverse-engineering + a Graph RAG graph + multi-language AST to compress a huge codebase into a structured knowledge base, where every conclusion traces back to a code line and every relation carries a confidence label. Suited to projects with 10+ repositories or microservices that AI cannot understand globally by reading the code directly. Triggers: architecture analysis, architecture reverse-engineering, codebase knowledge base, code-to-knowledge, architecture wiki, large multi-repo codebase. Loaded on demand by the teamai discovery stub.
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Perform codebase analysis and architecture mapping as the first phase of a security assessment. Explores the tech stack, frameworks, entry points, data flows, and trust boundaries. Outputs sast/architecture.md. Run this before any vulnerability detection skill. Use when asked to analyze a codebase for security or when sast/architecture.md does not yet exist.
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Use this skill for the architecture phases of an MBSE workflow in MATLAB, when writing idempotent buildXxx.m scripts that produce a three-layer RFLPV architecture (Functional, Logical, Physical) with interface dictionaries, stereotype profiles, allocation sets, and requirements Implement links. Trigger for defining stereotype properties, functional-to-logical / logical-to-physical allocation, mapping requirements to components via slreq Implement links, or running quantitative roll-up analysis on the architecture. Do NOT trigger for ad-hoc structural edits to an already-built System Composer model (adding one component, rewiring a port) — use `building-simulink-models` with `model_edit` for that. Works alongside the `system-composer` skill for detailed SC API patterns.
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This skill should be used when the user asks to "design system architecture", "evaluate microservices vs monolith", "create architecture diagrams", "analyze dependencies", "choose a database", "plan for scalability", "make technical decisions", or "review system design". Use for architecture decision records (ADRs), tech stack evaluation, system design reviews, dependency analysis, and generating architecture diagrams in Mermaid, PlantUML, or ASCII format.
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Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.
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Specialized software engineering prompt methodology for Claude. Use when building prompts for system design, code review, incident response, security analysis, API design, architecture decisions, RFCs, ADRs, runbooks, or technical leadership. Trigger on: "build a prompt for code review," "system design prompt," "SRE prompt," "security review prompt," "incident response prompt," "API design prompt," "architecture decision prompt," "RFC prompt," "runbook prompt." Provides 11 tested approaches across identity, reasoning, and output for engineering analysis. Do NOT use for general coding help, writing code, or debugging — this builds prompts that shape engineering analysis, not code itself. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available).
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Comprehensive software architecture skill for designing scalable, maintainable systems using ReactJS, NextJS, NodeJS, Express, React Native, Swift, Kotlin, Flutter, Postgres, GraphQL, Go, Python. Includes architecture diagram generation, system design patterns, tech stack decision frameworks, and dependency analysis. Use when designing system architecture, making technical decisions, creating architecture diagrams, evaluating trade-offs, or defining integration patterns.
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Analyze project code, configuration, and documentation to build a graph for interactive architecture exploration. It maps components and dependencies, supports Japanese descriptions, and can update the analysis based on changes since the previous analyzed commit.
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Audit a codebase across nine areas, including architecture, tests, security, and documentation. Produces TECH_DEBT_AUDIT.md with source locations, severity ratings, estimated repair effort, and prioritized recommendations to help plan maintenance work.
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Create task-oriented READMEs, API references, architecture documentation, and security reports. Organize information from quick starts to deeper explanations, and turn completed investigations into formal Markdown reports with supporting evidence, reproducible steps, and appropriately masked sensitive information.
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Make system design decisions by clarifying requirements, identifying constraints, and comparing the benefits and drawbacks of different approaches. Record the reasoning in architecture decision records, or ADRs, while considering simpler alternatives and the team's experience.
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Explore code structure and history using an index of code relationships and meaning-based search. Trace calls, assess change impact, find likely bug locations, generate architecture reports, and review pull requests for risk and conflicts.
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