Skill UI

This advanced workflow allows developers to clone live websites directly into structured React or HTML code using the Anima platform. It goes beyond simple cloning by featuring post-generation customization rules (e.g., updating utility classes or imports) and a dedicated design token mapper. This ensures the generated code is immediately usable and adheres strictly to the project's existing design system tokens, while also organizing the final component structure.

This guide details how to install and configure the Anima SDK for robust Figma-to-code generation. It is essential for developers setting up design-to-code automation pipelines, requiring configuration of both Anima and Figma API tokens. The SDK enables converting designs into production-ready React, Vue, or HTML components with Tailwind, MUI, or AntD styling.

This guide details production-ready patterns for integrating the Anima SDK into automated design-to-code pipelines. It covers best practices such as implementing a singleton client, managing generation cache, normalizing output for project conventions (e.g., TSX structure, utility classes), and incorporating retry logic for API resilience. Use it when building robust, reusable Anima client wrappers for enterprise applications.

This skill automatically converts structured data specifications (such as CSV, JSON schemas, SQL DDL, or Protobuf definitions) into functional, production-ready code. It generates complete models, validation logic, full CRUD API endpoints, and unit tests in multiple languages (Python, TypeScript, Go, etc.), significantly reducing boilerplate coding time and ensuring type safety.

This setup implements a pre-execution hook to safeguard code repositories by intercepting and blocking destructive Git operations (e.g., `git push`, `git reset --hard`, `git clean`). It prevents accidental or unauthorized data loss during automated code generation or tool use within AI environments, ensuring robust version control safety.

Master the complexities of building robust Retrieval Augmented Generation (RAG) pipelines using LangChain. This guide addresses common pitfalls in vector search, such as score semantics differences (L2 vs. Cosine), embedding dimension mismatches, and chunking issues for code/Markdown. Learn best practices for selecting between FAISS, Pinecone, Chroma, and PGVector, normalizing scores, and implementing hybrid keyword+vector search for maximum retrieval quality.

This skill provides implementation patterns and an SDK pattern for utilizing the RunwayML API. It allows users to perform advanced video generation, image-to-video tasks, and creative AI workflows directly within their code. Use cases include generating cinematic content, editing media, and managing API credits for complex creative projects.

This skill synthesizes a comprehensive Product Requirements Document (PRD) from the current conversation context and understanding of the codebase. It guides the user through structuring the problem statement, defining detailed user stories, outlining implementation decisions, and suggesting test strategies. The final PRD is formatted and submitted directly as a GitHub issue, ensuring a structured handover from discussion to development specification.

This guide details how to extend the Webiny CLI by adding custom commands using the `CliCommandFactory` pattern. Developers can define command structures, handle typed parameters, and implement custom logic for various tasks, including data migration, code generation, deployment scripting, or system health checks. It covers command definition, parameter handling, and formatted terminal output using the `Ui` service.

This utility serves as a Command Line Interface (CLI) alias for specialized AI code models, specifically mapping to the Claude Code `/pua:yes` command structure. It facilitates structured code generation or interaction by adopting a specific 'yes skill' protocol. It ensures that while switching to an encouragement-first narration, the system maintains strict verification red lines and handles configuration file updates while preserving unknown fields. Ideal for developers needing consistent, protocol-driven AI code assistance.

This skill provides a generic entry point for delegating self-contained, complex tasks to various LLM-powered sub-agents (Codex, Claude Code, Pi). It is designed to be used in meta-skill DAG steps for tasks like feature creation, PR reviews, code refactoring, or document generation, providing full tool surface access. It favors non-interactive CLI modes for reliable background execution.

This skill operates on two layers: the THINK layer enforces rigorous coding discipline (simplicity, surgical changes, goal-driven execution), and the SPEAK layer compresses prose output (caveman/classical modes) for maximum token efficiency. Use it when brevity is explicitly requested or when disciplined, high-precision code generation is required.