Agent Skills: langchain4j-tool-function-calling-patterns
Provides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.
ID: giuseppe-trisciuoglio/developer-kit/langchain4j-tool-function-calling-patterns
34340
Install this agent skill to your local
Skill Files
The files that make up langchain4j-tool-function-calling-patterns.
More agent skills
- shadcn-uiProvides complete shadcn/ui component library patterns including installation, configuration, and implementation of accessible React components. Use when setting up shadcn/ui, installing components, building forms with React Hook Form and Zod, customizing themes with Tailwind CSS, or implementing UI patterns like buttons, dialogs, dropdowns, tables, and complex form layouts.
- add-iconAdd or update supported language, framework, library, tool, or platform icons in Socialify. Use when a request involves adding a new icon/logo, mapping a display name to a Simple Icons entry or a custom icon, and updating the language options list used in the UI.
- data-client-graphql-setupSet up @data-client/graphql for GraphQL APIs. Configures GQLEndpoint with auth and custom options. Use after data-client-setup detects GraphQL patterns.
- paperjsxGenerate PPTX presentations, DOCX documents, XLSX spreadsheets, and PDF reports from structured JSON input using PaperJSX.
- code-reviewSkill for handling PR code reviews. Use when triggered by a PR review comment, review request, or when asked to review code changes. Provides workflow for reading review comments, understanding feedback, and iterating on changes.
- mlx-devWrite correct, idiomatic Apple MLX code for Apple Silicon ML. Use when working with MLX arrays, neural networks, training loops, lazy evaluation, unified memory, mx.eval, mx.compile, Metal GPU, memory optimization, quantization, or Apple Silicon performance. Covers critical API differences from PyTorch/NumPy, array indexing gotchas (lists must be mx.array, slices create copies), NHWC format for Conv2d, __call__ not forward(), float64 CPU-only, mlx-lm integration, and debugging patterns.