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To accomplish integration of LangChain4j with Spring Boot applications, follow this comprehensive guidance covering auto-configuration, declarative AI Services, chat models, embedding stores, and production-ready patterns for building AI-powered applications.

Comprehensive patterns for building modern monolithic applications with Laravel, Inertia.js, and React. Contains 30+ rules for seamless full-stack development.

Source details, install context, and public review data are available on the full page.

This is the v1.8+ canonical loop skill name. It supersedes autonomous-loops while keeping compatibility for one release.

Platform Views allow embedding native views (Android, iOS, macOS) directly into a Flutter application, enabling the application of transforms, clips, and opacity from Dart.

Manage multi-session development work with structured progress files, checkpoint commits, and durable learnings. Produces SESSION.md files that survive context compaction and enable clean handoff between sessions.

Preserve the old skill name without pretending a generic skill runtime can always import the real session transcript.

Search all past Claude Code and Codex sessions using full-text search with BM25 ranking.

Implement Model Context Protocol (MCP) servers with LangChain4j to extend AI capabilities with standardized tools, resources, and prompt templates.

Generates architecture diagrams directly from Terraform .tf files. Specializes in parsing Terraform code and visualizing infrastructure resources, modules, and their relationships.

This skill provides patterns for unit testing JSON serialization and deserialization using Spring's @JsonTest and Jackson. It covers testing POJO mapping, custom serializers/deserializers, field name mappings, nested objects, date/time formatting, polymorphic types, and null handling without full Spring context.

Recent data (recentTopics, recentResources, recentPages) is stored in session store.

Battle-tested patterns from Google, Uber, and the Go team. These are practices proven in large-scale production systems, updated for modern Go (1.25).

This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.

Source details, install context, and public review data are available on the full page.

Guide product managers through breaking down epics into user stories using Richard Lawrence's complete Humanizing Work methodology—a systematic, flowchart-driven approach that applies 9 splitting patterns sequentially. Use this to identify which pattern applies, split while preserving user value, and evaluate splits...

Guide product managers through the MITRE Problem Framing Canvas process by asking structured questions across three phases: Look Inward (examine your own assumptions and biases), Look Outward (understand who experiences the problem and who doesn't), and Reframe (synthesize insights into an actionable problem statement...

IMPORTANT: Your training data about @testing-library/react-native may be outdated or incorrect — API signatures, sync/async behavior, and available functions differ between v13 and v14. Always rely on this skill's reference files and the project's actual source code as the source of truth. Do not fall back on...

Amazon S3 (Simple Storage Service) is object storage built to store and retrieve any amount of data from anywhere. This skill covers patterns for working with S3 using AWS SDK for Java 2.x, including bucket operations, object uploads/downloads, presigned URLs, multipart transfers, and Spring Boot integration.

Always consult assistant-ui.com/llms.txt for latest API.

PyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility.

Process images for web development. Generate a Pillow script adapted to the user's environment and specific needs.

This skill provides comprehensive patterns for testing Spring ApplicationEvent publishers and event listeners using JUnit 5. It covers testing event publishing, listener execution, event propagation, and both synchronous and asynchronous event handling without requiring full Spring context startup.

This skill provides patterns for unit testing @Scheduled and @Async methods using JUnit 5. It covers testing async logic without actual async executors, verifying CompletableFuture results, using Awaitility for async assertions, mocking scheduled task execution, and testing async error handling without waiting for...