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概要と使いどころ
Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.
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。
AI-powered contractor matching and selection for construction projects. Analyze contractor capabilities, past performance, certifications, and project requirements to recommend optimal matches.
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。
Databricks documentation reference via llms.txt index. Use when other skills do not cover a topic, looking up unfamiliar Databricks features, or needing authoritative docs on APIs, configurations, or platform capabilities.
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 Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
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。
Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities. This skill MUST be invoked when the user mentions: "DBSQL", "Databricks SQL", "SQL warehouse", "SQL scripting", "stored procedure", "CALL procedure", "materialized view", "CREATE MATERIALIZED VIEW", "pipe syntax", "|>", "geospatial", "H3", "ST_", "spatial SQL", "collation", "COLLATE", "ai_query", "ai_classify", "ai_extract", "ai_gen", "AI function", "http_request", "remote_query", "read_files", "Lakehouse Federation", "recursive CTE", "WITH RECURSIVE", "multi-statement transaction", "temp table", "temporary view", "pipe operator". SHOULD also invoke when the user asks about SQL best practices, data modeling patterns, or advanced SQL features on Databricks.
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。
Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
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。
The 100th skill! Your intelligent guide to all 99 other skills. Recommends the perfect skill for any task, creates skill combinations, and helps you discover capabilities you didn't know you had.
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。
Turn an RFP and a folder of your past proposals, case studies, and capability docs into a requirement-by-requirement compliance matrix and a drafted response. Flags disqualifiers and unanswerable requirements before you burn a week writing.
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。
Fact lookup: Apple Human Interface Guidelines corpus (greppable markdown). Query on demand for accessibility and ergonomic minimums (hit-target sizes, Dynamic Type), platform terminology, system component capabilities, and App-Review-relevant conventions. Not design direction.
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。
Applies microservices for independent deployment and per-service scaling. Use when teams need autonomous release cycles with distinct capability scaling needs.
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 Electricity Maps contrib parser development, capacity updates, zone/exchange configuration maintenance, and focused repo validation workflows.
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。
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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。
Implements BYOD privacy compliance frameworks for personal device use in the workplace. Covers personal vs corporate data separation, MDM capabilities and limitations, employee consent requirements, data wiping boundaries, and monitoring restrictions on personal devices. Keywords: BYOD, mobile device management, MDM, personal device, data separation, containerisation, remote wipe, employee privacy.
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。
Manages deletion requests for children's personal data. Covers parental-initiated versus child-initiated requests, age of capacity assessment, identity verification, scope determination, third-party notification obligations, and regulatory timelines under GDPR Art. 17, COPPA Section 312.6, and UK AADC Standard 15. Keywords: deletion, children, right to erasure, parental request, data deletion, COPPA.
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。
Designs and executes tabletop breach simulation exercises for testing organizational breach response capabilities. Covers scenario creation with realistic inject timelines, participant role assignment, communication testing across internal and external channels, decision-point evaluation, and after-action report generation. Keywords: tabletop exercise, breach simulation, incident response testing, scenario design, after-action report.
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。
Framework for evaluating and selecting Consent Management Platforms (CMPs). Covers TCF v2.2 certification requirements, Global Privacy Control support, multi-regulation compliance (GDPR, CCPA, LGPD), A/B testing capabilities, API integration options, reporting features, and a structured vendor comparison methodology.
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。
Implements AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated decision.
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。
Implements technical breach detection capabilities including SIEM integration, DLP alert configuration, anomaly detection rules, and insider threat monitoring. Provides a breach classification taxonomy across confidentiality, integrity, and availability dimensions. Covers detection tool selection, alert tuning, and integration with privacy incident response workflows. Keywords: breach detection, SIEM, DLP, anomaly detection, insider threat, classification.
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 when exposing Spring Boot 3 application capabilities through Model Context Protocol tools, resources, or prompts. Covers Spring AI 1.x tool callback registration, transports, schemas, errors, security, and standalone MCP Java SDK compatibility.
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 when exposing Spring Boot 4 application capabilities through Model Context Protocol tools, resources, or prompts. Covers Spring AI 2.0 MCP annotations, transport selection, schemas, errors, security, and the standalone MCP Java SDK 2.x.
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。
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
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 repository architecture using Ontoly Software Graph and MCP capabilities. Use when asked to explain architecture, module boundaries, package topology, service ownership, or architectural risk.
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。
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
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。
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
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。