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Patterns and best practices for integrating ROS2 systems with web technologies including REST APIs, WebSocket bridges, and browser-based robot interfaces. Use this skill when building web dashboards for robots, streaming camera feeds to browsers, exposing ROS2 services as REST endpoints, or implementing bidirectional WebSocket communication between web UIs and ROS2 nodes. Trigger whenever the user mentions rosbridge, rosbridge_suite, roslibjs, FastAPI with ROS2, Flask with rclpy, WebSocket for robot telemetry, MJPEG streaming, WebRTC for robots, REST API wrapping ROS2 services, web-based robot control, browser robot interface, robot dashboard, CORS configuration for robots, or any web-to-ROS2 bridge pattern. Also trigger for authentication on robot web interfaces, rate limiting sensor streams, video streaming from robot cameras to browsers, or running async web frameworks alongside the ROS2 executor. Covers rosbridge_suite, FastAPI, Flask, WebSocket, and WebRTC approaches.

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Comprehensive best practices, design patterns, and common pitfalls for ROS2 (Robot Operating System 2) development. Use this skill when building ROS2 nodes, packages, launch files, components, or debugging ROS2 systems. Trigger whenever the user mentions ROS2, colcon, rclpy, rclcpp, DDS, QoS, lifecycle nodes, managed nodes, ROS2 launch, ROS2 parameters, ROS2 actions, nav2, MoveIt2, micro-ROS, or any ROS2-era robotics middleware. Also trigger for ROS2 workspace setup, DDS tuning, intra-process communication, ROS2 security, or deploying ROS2 in production. Also trigger for colcon build issues, ament_cmake, ament_python, CMakeLists.txt for ROS2, package.xml dependencies, rosdep, workspace overlays, custom message generation, or ROS2 build troubleshooting. Covers Humble, Iron, Jazzy, and Rolling distributions.

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Operational knowledge for the daily-brief digest pipeline (this project). RSS/API fetchers, pluggable LLM enrichment (default claude CLI on Max; also anthropic/openai/deepseek/minimax API), trading section, HTML rendering, cross-platform scheduler integration (Windows Task Scheduler / macOS launchd / Linux cron). Load when the user asks about running daily / regenerating sections / debugging a failed run / adding or disabling sources / LLM quota / scheduler / why a tab shows wrong data / why a source failed / switching LLM backend. Always prefer the documented npm commands over re-implementing logic. Diagnose by reading logs/daily-*.log first, then logs/llm-calls.jsonl for LLM-side issues.

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Background legal and regulatory context for the shinkoku tax filing plugin. Contains the standard disclaimer about the scope of tax information provided, the relationship to the Tax Accountant Act (税理士法), and tool limitations. This skill is not user-invocable — Claude loads it automatically when generating tax-related responses that require a disclaimer.

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Provides AWS CloudFormation patterns for ECS clusters, task definitions, services, container definitions, auto scaling, blue/green deployments, CodeDeploy integration, ALB integration, service discovery, monitoring, logging, template structure, parameters, outputs, and cross-stack references. Use when creating ECS clusters with CloudFormation, configuring Fargate and EC2 launch types, implementing blue/green deployments, managing auto scaling, integrating with ALB and NLB, and implementing ECS best practices.

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Provides patterns to deploy ECS tasks and services with GitHub Actions CI/CD. Use when building Docker images, pushing to ECR, updating ECS task definitions, deploying ECS services, integrating with CloudFormation stacks, configuring AWS OIDC authentication for GitHub Actions, and implementing production-ready container deployment pipelines. Supports ECS deployments with proper security (OIDC or IAM keys), multi-environment support, blue/green deployments, ECR private repositories with image scanning, and CloudFormation infrastructure updates.

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Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.

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Provides AWS CloudFormation patterns for Lambda functions, layers, API Gateway integration, event sources, cold start optimization, monitoring, logging, template validation, and deployment workflows. Use when creating Lambda functions with CloudFormation, configuring event sources, implementing cold start optimization, managing layers, integrating with API Gateway, and deploying Lambda infrastructure.

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Provides comprehensive NestJS framework patterns with Drizzle ORM integration for building scalable server-side applications. Generates REST/GraphQL APIs, implements authentication guards, creates database schemas, and sets up microservices. Use when building NestJS applications, setting up APIs, implementing authentication, working with databases, or integrating Drizzle ORM.

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Provides Better Auth integration patterns for NestJS backend and Next.js frontend with Drizzle ORM and PostgreSQL. Use when setting up Better Auth with NestJS backend, integrating Next.js App Router frontend, configuring Drizzle ORM schema, implementing social login (GitHub, Google), adding plugins (2FA, Organization, SSO, Magic Link, Passkey), implementing email/password authentication with session management, or creating protected routes and middleware.

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Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".

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 patterns and code examples for building Next.js 16+ applications with App Router architecture. Use when creating projects with App Router, implementing Server Components and Client Components ("use client"), creating Server Actions for forms, building Route Handlers (route.ts), configuring caching with "use cache" directive (cacheLife, cacheTag), setting up parallel routes (`@slot`) or intercepting routes, migrating to proxy.ts, or working with App Router file conventions (layout.tsx, page.tsx, loading.tsx, error.tsx).

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Provides comprehensive Nx monorepo management guidance for TypeScript/JavaScript projects. Use when creating Nx workspaces, generating apps/libraries/components, running affected commands, setting up CI/CD, configuring Module Federation, or implementing NestJS backends within Nx

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Provides comprehensive NestJS best practices including modular architecture, dependency injection scoping, exception filters, DTO validation with class-validator, and Drizzle ORM integration. Use when designing NestJS modules, implementing providers, creating exception filters, validating DTOs, or integrating Drizzle ORM within NestJS applications.

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Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.

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 Amazon DynamoDB patterns using AWS SDK for Java 2.x. Use when creating, querying, scanning, or performing CRUD operations on DynamoDB tables, working with indexes, batch operations, transactions, or integrating with Spring Boot applications.

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 AWS Lambda patterns using AWS SDK for Java 2.x. Use when invoking Lambda functions, creating/updating functions, managing function configurations, working with Lambda layers, or integrating Lambda with Spring Boot applications.

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Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.

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Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.

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Provides implementation patterns for Clean Architecture, Hexagonal Architecture (Ports & Adapters), and Domain-Driven Design in Java 21+ Spring Boot 3.5+ applications. Use when structuring layered architectures, separating domain logic from frameworks, implementing ports and adapters, creating entities/value objects/aggregates, or refactoring monolithic codebases for testability and maintainability.

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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.

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Serverless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invoke BEFORE writing a job spec. For migrating existing classic workloads, use databricks-serverless-migration.

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Use when querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does not grant access to a Unity Gateway service. Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that.

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Databricks CLI operations and the parent/entry-point skill for Databricks CLI use: authentication, profile selection, and bundles. Load this first for CLI, auth, profile, and bundle tasks, then load the matching product skill. For finding or exploring data, answering questions about the data, or generating SQL, load the databricks-data-discovery skill (it routes to Genie One). Contains up-to-date guidelines for Databricks-related CLI tasks.

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。