Skill UI

Leverages the Anima SDK to convert visual designs from Figma into production-ready code across multiple modern frameworks (React, Vue, HTML). It supports clean TypeScript integration and Tailwind CSS styling, making it ideal for developers building automated design-to-code pipelines or rapidly prototyping complex UI components directly from design mockups.

Implements an iterative development workflow using the Anima SDK. This tool allows developers to quickly generate, compare, and preview code outputs from Figma designs across multiple frameworks (React, Vue, HTML/CSS) in a local environment. Ideal for rapid prototyping, cross-framework comparison, and refining design-to-code implementation.

This guide provides a reference architecture for automating the design-to-code workflow. It outlines the complete pipeline—from Figma design input to structured React/Vue output—detailing components like Figma API integration, code generation, and crucial post-processing steps (token mapping, normalization). Ideal for establishing scalable design systems and managing large-scale design handoffs in a professional environment.

This guide assists developers in upgrading the Anima SDK versions and managing associated API changes. It covers migrating from manual Figma plugin workflows to automated SDK-based code generation, ensuring seamless integration of the latest design system features into development projects.

This skill demonstrates how to create an event-driven pipeline using Figma's Webhooks. It listens for design file changes and automatically triggers Anima to regenerate production-ready code components (e.g., React/TypeScript/Tailwind). Ideal for integrating continuous design updates into CI/CD workflows.

A comprehensive tool for managing annotation datasets, supporting full CRUD operations, label management, and advanced export functionality. It specifically handles COCO format export and facilitates uploading datasets to platforms like Kaggle, streamlining the data pipeline for AI model development.

A comprehensive guide for developers encountering complex issues with the Claude API, including context window overflow management, debugging tool use failures, handling streaming corruption, and diagnosing unexpected stop reasons. Provides Python code snippets and diagnostic commands to ensure robust AI integration.

A guide detailing four validated architectural patterns for integrating Claude APIs, catering to diverse scale and use cases. Choose between Serverless, Streaming Microservice, Queue-Based Pipelines, or complex Orchestrators to optimize performance, cost, and latency for your application, whether it's a real-time chatbot or large-scale batch processing.

Master building robust AI workflows with Anthropic's core APIs. This guide details two powerful patterns: real-time streaming via SSE events for interactive chat UIs, and the cost-efficient Message Batches API for asynchronously processing up to 100,000 requests. Includes comprehensive code examples for Python and TypeScript.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.

A comprehensive guide providing minimal, working examples for integrating with the Anthropic Claude Messages API. Covers core functionalities including basic text completion, advanced vision capabilities for image analysis, and real-time streaming responses. Ideal for developers starting a new Claude integration or testing core API patterns.

A comprehensive guide detailing common pitfalls and anti-patterns encountered when integrating with the Anthropic Claude API. It highlights crucial differences from other models (e.g., OpenAI), such as the mandatory nature of `max_tokens`, correct handling of system prompts, and precise response parsing techniques. Essential for developers debugging subtle API integration issues and ensuring best practices.