2,988 件 ・ 関連度順
概要と使いどころ
Provides implementation patterns for Clean Architecture, Domain-Driven Design (DDD), and Hexagonal Architecture (Ports & Adapters) in NestJS/TypeScript applications. Use when structuring complex backend systems, designing domain layers with entities/value objects/aggregates, implementing ports and adapters, creating use cases, or refactoring from anemic models to rich domain models with dependency inversion.
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。
Provides comprehensive Tailwind CSS utility-first styling patterns including responsive design, layout utilities, flexbox, grid, spacing, typography, colors, and modern CSS best practices. Use when styling React/Vue/Svelte components, building responsive layouts, implementing design systems, or optimizing CSS workflow.
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。
Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.
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。
Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
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。
Provides Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.
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。
Update or create DOCS.md files for Shift subsystems. Use this skill whenever the user asks to update docs, refresh documentation, create a DOCS.md, write module documentation, or says "update docs for X". Also trigger after completing a large feature when Claude.md says to update docs — check if any DOCS.md in the affected subsystem needs refreshing.
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 custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom protocol), build a Spark connector or plugin in Python, implement a DataSourceReader or DataSourceWriter, pull data from or push data to a system via Spark, or work with the PySpark DataSource API in any way. Even if they just say "read from X in Spark" or "write DataFrame to Y" and there's no native connector, 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。
Security review checklist for construction software systems. Use when building integrations, APIs, data pipelines, or dashboards for construction projects.
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 automated ETL (Extract-Transform-Load) pipelines for construction data. Process PDFs, Excel, BIM exports. Generate reports, dashboards, and integrate with other systems. Orchestrate with Airflow or n8n.
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 multi-agent AI systems for construction estimation. Use CrewAI/LangGraph to orchestrate specialized agents: QTO agent, pricing agent, validation agent. Automate complex estimation 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。
Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.
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。
Extract and analyze data from construction ERP systems. Pull project data for analytics, reporting, and integration.
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。
Comprehensive audit of all construction data sources and systems. Map data flows, identify silos, assess quality, and create integration roadmap.
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。
Read and parse XML from construction systems - P6 schedules, BSDD exports, IFC-XML, COBie-XML. Convert to pandas DataFrames.
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。
Classify BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.
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。
Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.
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。
Automate construction permit tracking and management. Monitor application status, track renewal deadlines, manage document requirements, and integrate with municipal systems.
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。
Answers business questions about the user's own spreadsheet or data export (CSV, TSV, XLSX from a CRM, Shopify, Stripe, QuickBooks, ad platforms, HR or payroll systems) correctly and auditably. Profiles every file first (grain, duplicate keys, embedded Grand Total rows, text-stored numbers, mixed currencies, UTC timestamps, Excel serial dates, hidden rows), states the metric definition used, computes every number in SQL or pandas instead of mental math, checks joins for fan-out, reconciles to a known total, and returns the query, filters, row counts, and rows behind the answer. Use this whenever the user says "analyze this spreadsheet", "how many...", "what's the total in this export", "which customers...", "pivot this", "revenue by month", "top 10", "what's our churn/MRR/AOV", or asks any question about their data, CSV, or Excel file, even a quick one. Also use when they ask to change values in a data file.
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。
Provides templates and lifecycle patterns for storage and documentation systems. Use when organizing knowledge storage, config lifecycle, or naming conventions.
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。
Provides weighted scoring, rubrics, and decision-threshold patterns. Use when designing quality gates, evaluation systems, or decision frameworks.
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 layered n-tier architecture with enforced boundaries. Use when designing moderate systems needing clear presentation, domain, and persistence layers.
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。
Applies hexagonal architecture isolating domain from infrastructure. Use when designing systems where testability and port/adapter separation are priorities.
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 event-driven async messaging to decouple producers and consumers. Use when designing real-time or multi-subscriber systems needing loose coupling.
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。