Blockchain

3層開示モデル（メタデータ→本文→リソース）による段階的な情報提供で、トークン効率と知識スケーラビリティを両立。スキル発動信頼性を最大化し、必要な時に必要な知識だけをロードします。Anchors:• The Pragmatic Programmer (Andrew Hunt, David Thomas) / 適用: 段階的な情報開示と実践的改善 / 目的: トークン効率を維持しながら深い知識を提供• Progressive Disclosure (Jakob Nielsen) / 適用: 認知負荷の最小化 / 目的: UX設計原則のスキルメタデータへの応用• Information Architecture (Louis Rosenfeld) / 適用: 階層的知識組織化 / 目的: 遅延読み込みとインデックス駆動設計Trigger:Use when designing skill metadata, optimizing token usage, implementing progressive disclosure patterns, improving skill activation reliability, organizing knowledge hierarchically, reducing context window consumption, or creating scalable documentation structures.

Pytest testing patterns, fixtures, mocking, and coverage for GMailArchiver. Use when writing unit tests, integration tests, creating fixtures, mocking Gmail API, or checking coverage. Triggers on: test, pytest, fixture, mock, coverage, conftest, assert, unit test, integration test.

Create, update, validate, and submit Homebrew Casks. Use when the user mentions Homebrew cask/cask, Homebrew/homebrew-cask, adding a new cask, updating a cask, cask token naming, sha256, url verified:, livecheck, zap/uninstall, or when asked to run brew style/audit for a cask.

This skill optimizes prompts for Large Language Models (LLMs) to reduce token usage, lower costs, and improve performance. It analyzes the prompt, identifies areas for simplification and redundancy removal, and rewrites the prompt to be more concise and effective. It is used when the user wants to reduce LLM costs, improve response speed, or enhance the quality of LLM outputs by optimizing the prompt. Trigger terms include "optimize prompt", "reduce LLM cost", "improve prompt performance", "rewrite prompt", "prompt optimization".

Design REST and GraphQL APIs. Use when creating backend APIs, defining API contracts, or integrating third-party services. Covers endpoint design, authentication, versioning, documentation, and best practices.

ドメイン駆動設計（DDD）のビルディングブロックを活用したドメインモデリングを専門とするスキル。Entity、Value Object、Aggregate、Repository Patternを適用し、ビジネスロジックを中心に据えた堅牢なドメイン層を設計する。Anchors:• Domain-Driven Design (Eric Evans) / 適用: 戦術的パターン / 目的: ドメインモデル構築• Implementing DDD (Vaughn Vernon) / 適用: 集約設計 / 目的: トランザクション境界定義• Clean Architecture (Robert C. Martin) / 適用: 依存関係逆転 / 目的: ドメイン層の独立性確保Trigger:Use when designing domain models, defining entities and value objects, establishing aggregate boundaries, designing repository interfaces, or applying DDD tactical patterns.domain driven design, DDD, entity design, value object, aggregate, repository pattern, domain model, bounded context

Generate production-ready Kubernetes manifests for AgentStack. Use for creating Deployments, Services, ConfigMaps, Secrets, RBAC, and other K8s resources. Triggers on "create deployment", "k8s manifest", "kubernetes yaml", "pod spec", "service definition", "configmap", "RBAC", or when deploying components to Kubernetes.

MCPのリソース指向API設計パターンを提供。URIスキーム設計、リソースモデル定義、プロバイダー実装、キャッシュ戦略、リソース変換パターンを網羅する。Anchors:• RESTful Web APIs (Leonard Richardson) / 適用: リソース設計・URI設計 / 目的: REST原則の適用• MCP Resource Protocol / 適用: リソースプロバイダー実装 / 目的: MCP仕様準拠Trigger:Use when designing MCP resources, implementing resource providers, or defining URI schemes.MCP resource, resource provider, URI scheme, リソース定義, リソースモデル, API設計

SLO（Service Level Objective）とSLI（Service Level Indicator）の設計、エラーバジェット管理、信頼性目標の策定を支援するスキル。Googleの SRE プラクティスに基づき、適切な信頼性目標を設計する。Anchors:• Site Reliability Engineering (Google) / 適用: SLO/SLI設計原則 / 目的: 信頼性目標の最適化• The Site Reliability Workbook (Google) / 適用: 実践的なSLO実装 / 目的: 運用可能なSLO設計• Implementing Service Level Objectives (Hidalgo) / 適用: SLO成熟度モデル / 目的: 段階的導入Trigger:Use when designing SLOs, defining SLIs, calculating error budgets, or establishing reliability targets.SLO design, SLI definition, error budget, reliability target, service level objective

This skill identifies the locations of known transcription factor (TF) binding motifs within genomic regions such as ChIP-seq or ATAC-seq peaks. It utilizes HOMER to search for specific sequence motifs defined by position-specific scoring matrices (PSSMs) from known motif databases. Use this skill when you need to detect the presence and precise genomic coordinates of known TF binding motifs within experimentally defined regions such as ChIP-seq or ATAC-seq peaks.

Process large document corpora (1000+ docs, millions of tokens) through knowledge graph construction and stateful multi-hop reasoning. Use when (1) User provides a large corpus exceeding context limits, (2) Questions require connections across multiple documents, (3) Multi-hop reasoning needed for complex queries, (4) User wants persistent queryable knowledge from documents. Replaces brute-force document stuffing with intelligent graph traversal.

Provides Tenzir design system tokens and component specifications. Use when building UI components, styling with CSS/Tailwind, choosing colors, typography, spacing, or implementing buttons, inputs, tags/badges, toasts, and other Tenzir UI elements.

コマンドのパフォーマンス最適化（トークン効率化/並列実行/モデル選択/速度改善）を整理し、性能改善の判断と適用を支援するスキル。計測観点、最適化手順、テンプレート運用を一貫して整理する。Anchors:• High Performance Browser Networking (Ilya Grigorik) / 適用: パフォーマンス測定 / 目的: レイテンシー削減• Design of Computer Programs (Peter Norvig) / 適用: 最適化設計 / 目的: 実行速度向上• Programming Pearls (Jon Bentley) / 適用: トークン効率化 / 目的: リソース削減Trigger:Use when optimizing command performance, reducing token usage, or designing parallel execution flows and model selection.command performance, token optimization, parallel execution, model selection

This skill creates Ansible playbooks for automating configuration management tasks. It generates production-ready, multi-platform playbooks based on user-defined requirements, incorporating best practices and a security-first approach. Use this skill when you need to automate server configurations, software deployments, or infrastructure management using Ansible. Trigger this skill by requesting "Ansible playbook," specifying configuration details, or asking for automation of a particular setup.

Uses the mcp postgresql to read and write crm relevant data to the crm database: Its about: sales_opportunity (Verkaufschancen) person (Kontaktperson im Unternehmen) company_site (Ein Standort eines Unternehmens) event (Aktivität, TODO, ... insgesamt bilden die Aktivitäten die Historie und die Zukunft von company_site, person und sales_opportunity ab) data_files (Dateien: PDFs, Bilder, E-Mail-Anhänge, Office-Dokumente - verknüpft mit CRM-Entitäten)

Package language-specific subsets of claudefiles

マルチエージェントシステム設計を専門とするスキル。複数のエージェント間の効果的な協調、ハンドオフプロトコルの設計、情報受け渡しメカニズムにより、スケーラブルで保守性の高い分散システムを構築する。Anchors:• Building Microservices (Sam Newman) / 適用: サービス間の協調設計 / 目的: 疎結合で信頼性の高いエージェント連携• Patterns of Enterprise Application Architecture (Martin Fowler) / 適用: ハンドオフパターン / 目的: 明確なプロトコル設計• Working Effectively with Legacy Code (Michael Feathers) / 適用: 既存システムとの統合 / 目的: 段階的なエージェント導入Trigger:Use when designing multi-agent collaboration, defining handoff protocols, optimizing inter-agent communication, or managing agent dependencies.multi-agent, agent collaboration, handoff protocol, delegation, chaining, parallel agents, feedback loop, orchestration

カール・ウィーガーズの要求工学とIEEE 830に基づき、ステークホルダーニーズを抽出し、検証可能な要件に落とし込むための体系的なスキル。スコープ定義、要件抽出、仕様化、品質検証、合意形成までの一貫プロセスを提供する。Anchors:• 『Software Requirements』（Karl Wiegers） / 適用: 要件工学 / 目的: 品質要件の明確化• IEEE 830 / 適用: 要件仕様の構造 / 目的: 一貫したドキュメント化• ISO/IEC 25010 / 適用: 非機能要件分類 / 目的: 品質特性の網羅Trigger:Use when defining system requirements, eliciting stakeholder needs, validating requirement quality, or drafting requirements documents.

Run shell commands where long (many tokens) output is expected. Use when you need to

Converts Figma/design specifications into production-ready UI components with accurate spacing, typography, color tokens, responsive rules, and interaction states (hover, focus, disabled, active). Generates Tailwind/shadcn code with design system tokens mapping. Use when translating "Figma to code", "design specs to components", or "implement design system".