Skill UI

This guide provides comprehensive troubleshooting for common issues encountered when converting Figma designs to code using the Anima SDK. It covers authentication failures (Figma/Anima tokens), specific node/file lookup errors, code rate limits, and output quality problems, helping developers ensure accurate and robust code generation.

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.

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.

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.

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.

This guide provides a comprehensive setup for a fast and reliable local development loop for applications powered by the Anthropic Claude API. It covers environment management, implementing hot reloading, tracking API usage costs, and utilizing mock responses to ensure a smooth, rapid iteration cycle for AI developers.

Provides comprehensive security guidelines for integrating Anthropic Claude API. Covers essential best practices including secure API key management (rotation, isolation), defending against prompt injection attacks by separating system instructions from user input, robust input sanitization, and output validation to prevent accidental data leakage. Essential for building production-grade, secure AI applications.

A comprehensive diagnostic reference for resolving common technical failures encountered when running web scraping and automation tasks on the Apify platform. Covers issues such as Actor run failures, API rate limits (429), authentication errors (401), anti-bot blocks, and memory allocation issues, providing structured fixes and best practices for robust scraping pipelines.

Provides comprehensive programmatic control over Apify's core data infrastructure, including Datasets, Key-Value Stores, and Request Queues. This skill allows users to perform CRUD operations on various storage types, manage data persistence, and orchestrate complex, multi-stage data pipelines (e.g., scrape -> transform -> export). Ideal for building robust, end-to-end web scraping and data processing automation workflows.

This skill provides a comprehensive guide and implementation for deploying a containerized AppFolio property management integration service using Docker. It covers building a production-ready image, configuring environment variables for multi-property setups, implementing health checks for API connectivity, and executing zero-downtime rolling updates across your property portfolio.

This comprehensive guide addresses common and complex errors encountered when automating Apple Notes on macOS using JXA or osascript. It covers three major areas: TCC permission denials, AppleEvent communication failures, and iCloud synchronization issues. The document provides a detailed error reference table, root cause analysis, tested fixes, and diagnostic scripts to help developers successfully automate Notes tasks.