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概要と使いどころ
Expert guide for React Native 0.79+ and Expo SDK 53+ development. Covers cross-platform mobile architecture, Expo Router v4, New Architecture, OTA updates, and native modules / Panduan ahli pengembangan React Native 0.79+ dan Expo SDK 53+ untuk aplikasi mobile.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for Astro 5+ framework — Content Collections, Islands Architecture, View Transitions, partial hydration, and MDX integration / Panduan ahli framework Astro 5+ — Content Collections, Islands Architecture, View Transitions, partial hydration, dan integrasi MDX.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for Composable MACH Architecture (Microservices, API-first, Cloud-native, Headless) — dynamic UI composition, federated API mesh, Backend-for-Frontend patterns, plugin/extension architectures, event-driven composition, and composable AI routing / Panduan ahli Arsitektur MACH Komposabel — komposisi UI dinamis, API mesh federasi, pola Backend-for-Frontend, arsitektur plugin/ekstensi, komposisi event-driven, dan routing AI komposabel.
日本語の概要は準備中です。原文の説明を表示しています。
Generates CQRS Commands with Handlers, Validators, and Request DTOs following Clean Architecture patterns. Commands represent actions that modify state and return Result types for proper error handling.
日本語の概要は準備中です。原文の説明を表示しています。
Generates RESTful API Controllers with proper routing, versioning, authorization, and MediatR integration. Follows REST conventions and Clean Architecture patterns.
日本語の概要は準備中です。原文の説明を表示しています。
architecture で選定したデータ基盤（Microsoft Fabric / Azure Databricks / Foundry IQ）を、事前確認 → plan と hash 承認 → デプロイ → セマンティック層 → runtime 検証 → 計算停止まで API で構築し、MCP 接続情報を Cowork / Copilot Studio / Foundry へ引き渡す。Dataverse は dataverse スキル、データ投入は data-migration スキルへ委譲する。
Software design principles, patterns, and architecture from SOLID through distributed systems. Covers the five SOLID principles with violations and fixes, DRY/KISS/YAGNI heuristics, separation of concerns, 12 GoF design patterns organized by intent (creational, structural, behavioral), architectural patterns (MVC, MVP, MVVM, layered, hexagonal, microservices, event-driven), coupling and cohesion metrics, dependency injection, and the design decision framework for choosing between competing approaches. Use when making design decisions, reviewing architecture, refactoring code, or teaching software engineering principles.
日本語の概要は準備中です。原文の説明を表示しています。
Electrical power distribution design for infrastructure: load calculations (NEC 220), conductor sizing (NEC 310.16), transformer/UPS/PDU selection, redundancy architectures (N through 2N+1), DC distribution, and voltage classes 120V-480V. Activates for electrical load calculations, conductor sizing, power equipment selection, data center power design, and redundancy architecture planning.
日本語の概要は準備中です。原文の説明を表示しています。
Backend API design, database architecture, microservices patterns, and test-driven development. Use for designing APIs, database schemas, or backend system architecture.
日本語の概要は準備中です。原文の説明を表示しています。
Use for architecture review, dependency graphs, coupling analysis, or circular dependency detection. Establishes project module namespaces and traces imports plus Claude plugin hook, skill, script, PEP 723, and MCP boundaries. Emits partitioned Mermaid graphs with cycle, coupling, and ownership signals; verifies contract and consequence before assigning severity. Registers diagrams as codebase-analysis artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
日本語の概要は準備中です。原文の説明を表示しています。
Deterministic Software Graph analysis via Ontoly CLI and MCP. Use when: (1) Codebase architecture, dependency, route, service, module, configuration, or impact questions need graph evidence, (2) A repository should be analyzed before source search, (3) You need a persistent SoftwareGraph.json for validation, MCP, or agent workflows. Triggers: "build software graph", "trace route", "impact analysis", "architecture summary", "dependency graph", "what uses this", "Ontoly".
日本語の概要は準備中です。原文の説明を表示しています。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
日本語の概要は準備中です。原文の説明を表示しています。
Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which package owns this", "is this the right layer"). Explains subsystem architecture, runtime flow, onboarding mental models. Can critique architecture. Use why for motivation.
日本語の概要は準備中です。原文の説明を表示しています。
Review touched subsystems for layering, crate boundaries, single-source-of-truth rules, registry-driven behavior, and compiler/runtime separation. Use when a broad review needs a dedicated architecture pass or when the user asks for architectural review specifically.
日本語の概要は準備中です。原文の説明を表示しています。
Icarus Figures produces publication-grade figures for scientific papers — reproducible matplotlib/seaborn data figures and original TikZ method/framework/architecture figures, held to a four-axis quality bar (depth · elegance · unimpeachable · visible-gap) and exported as editable PDF+PNG+SVG with a runnable critique gate. Use this whenever a figure is destined for a paper, journal, conference submission, thesis, or report — making a plot/chart publication- or journal-ready, building a method/architecture/framework diagram or 技术路线图 (the hero figure a paper opens with), a schematic or workflow figure, or cleaning up a matplotlib/seaborn/TikZ figure that currently looks like a homework plot. Trigger even when the user never says "publication": if the figure goes into a written scientific document, this skill applies.
日本語の概要は準備中です。原文の説明を表示しています。
Build and query architecture-aware code search, dependency graphs, call flows, impact analysis, and belief maps for Python, TypeScript, TSX, Rust, C#, Java, Go, Ruby, and Ruby on Rails repositories. Use before non-trivial code changes to find module boundaries, blast radius, architecture violations, and the minimal source files to read.
日本語の概要は準備中です。原文の説明を表示しています。
Create and manage Architecture Decision Records (ADRs). Use when making architectural changes (refactoring, API versioning, new domain layers, schema changes), when reviewing recent commits for documentation needs, or when the user mentions ADR, architecture decisions, or documenting technical decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Generate C4 architecture diagrams from system notes
日本語の概要は準備中です。原文の説明を表示しています。
Create and edit Obsidian Canvas files for architecture visualization
日本語の概要は準備中です。原文の説明を表示しています。
Evaluate an architecture artifact (ADR, HLD, LLD) against the vault's established architecture principles
日本語の概要は準備中です。原文の説明を表示しています。
Generate architecture diagrams using Python diagrams library
日本語の概要は準備中です。原文の説明を表示しています。
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
日本語の概要は準備中です。原文の説明を表示しています。
CTO-level architectural advisor for AI-native development. Use this skill whenever you encounter code design decisions, architecture discussions, system resilience questions, or any work touching: "architecture", "design", "scale", "dependencies", "state", "failure", "blast radius", "refactor", "migrate", "optimize", "resilience", "consistency", "observability", "bottleneck", "coupling", "monolith", "microservices", "distributed", "concurrency", "data flow", "system design", or any prompt suggesting code-first thinking when design-first thinking is needed. This skill integrates with Claude Code to review generated code for architectural soundness, define design systems via design.md, and guide teams toward CTO-level thinking. Trigger aggressively on architectural questions—this is where AI adds the most leverage.
日本語の概要は準備中です。原文の説明を表示しています。