Skill UI

This comprehensive geospatial science skill covers remote sensing, GIS, spatial analysis, and machine learning for Earth observation. It supports satellite imagery processing, vector and raster data operations, spatial statistics, and point cloud processing. Users can utilize cloud-native workflows and 8 programming languages with 500+ code examples. Ideal for terrain analysis, hydrological modeling, and marine spatial analysis tasks.

PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining flexibility. It automates training workflows, supports multi-GPU/TPU orchestration, and implements best practices for neural network training. Features include LightningModules, Trainers, data pipelines, callbacks, logging (W&B, TensorBoard), and distributed training strategies like DDP and FSDP for scalable training.

This specialized skill automates the process of converting visual designs (from Stitch) into high-quality, modular React and Vite components. It adheres to modern frontend best practices, enforcing logic isolation via custom hooks, data decoupling using mock data files, and ensuring type safety with TypeScript interfaces. The workflow includes automated asset fetching, style mapping (Tailwind), and rigorous validation cycles to produce production-ready, maintainable code.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.

This skill provides actionable strategies and code patterns to significantly reduce Anima API costs. It focuses on optimizing usage through generation caching, detecting component changes for incremental generation, and implementing smart policies to minimize unnecessary API calls. Ideal for managing large-scale design-to-code workflows and controlling API budget.

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 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.

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.

A comprehensive resource detailing common anti-patterns and critical mistakes when integrating with the Canva Connect API. This guide covers essential best practices, including robust token management, handling rate limits, implementing asynchronous workflows, and securing webhooks. Ideal for developers conducting code reviews, onboarding new team members, or auditing existing Canva-related integrations.