Skill UI

This guide provides a deep dive into migrating applications from OpenAI's GPT models to Anthropic's Claude API. It covers crucial API differences, including endpoint changes, response structure updates, handling the system prompt, tool use restructuring, and necessary SDK code swaps (e.g., TypeScript examples). Essential resource for developers transitioning AI backends.

A comprehensive guide to the three primary architecture patterns for integrating with Figma: CLI scripts, Webhook services, and dedicated plugins. This resource helps developers select the optimal pattern (REST vs. Plugin API) based on whether the use case requires real-time event handling, local automation, or in-editor component interaction. Ideal for planning robust Figma-connected applications like design token synchronization or linting.

A comprehensive guide for diagnosing and resolving common errors encountered when integrating with the Figma REST API or Figma Plugin API. It covers status codes (403, 429, 404), token management, rate limiting, and plugin-specific runtime issues, ensuring robust and stable API development.

This skill provides a comprehensive workflow to export images, icons, and assets directly from Figma files using the REST API. It enables users to render specific frames or components as high-quality PNG, SVG, JPG, or PDF files. Ideal for building automated asset pipelines, generating documentation previews, or batch exporting large icon libraries.

A comprehensive diagnostic tool designed to collect all necessary evidence for troubleshooting persistent Figma REST API issues or preparing detailed support tickets. It automatically tests connectivity, checks rate limit status, captures environment information, and redacts sensitive data, providing a clean, actionable archive for developers and QA teams.

A comprehensive runbook for rapid incident response to Figma REST API failures. It guides users through quick triage (checking status, authentication, rate limits), decision-making based on error codes (403, 429, 500), applying immediate mitigation strategies (token rotation, fallback modes), and completing thorough postmortem documentation.

This comprehensive guide details the ten most common mistakes and anti-patterns encountered when integrating with the Figma REST and Plugin APIs. It covers critical topics such as preventing oversized data fetches, managing rate limits, securing tokens, handling color format conversions, and optimizing data sync using webhooks instead of polling. Essential for code reviews and onboarding. Use when reviewing Figma code, onboarding new developers, or auditing an existing Figma integration.

A comprehensive guide to optimizing performance when integrating with the Figma REST API. Techniques cover aggressive response caching (LRU), reducing payload size using depth parameters, batching multiple node fetches, and implementing webhook-driven cache invalidation. Essential for handling large, complex Figma files efficiently.

A collection of production-ready patterns for interacting with the Figma REST API and Plugin API. It provides type safety, robust error handling, and reusable abstractions for tasks like building client wrappers, extracting design tokens, and traversing complex node trees. Essential for building scalable, enterprise-grade Figma integrations.

This guide manages critical migrations for the Figma REST API, addressing scope deprecations (e.g., migrating 'files:read' to granular scopes) and upgrading from Webhooks V1 to V2. It is essential for developers updating existing integrations to ensure compatibility with the latest API standards and security features.

This guide provides a minimal working example for integrating with the Flexport REST API v2. It demonstrates core logistics operations, including listing shipments, retrieving detailed shipment milestones, and listing associated containers. It is ideal for developers starting a new Flexport integration, testing API connectivity, or learning the basic API patterns.

This skill creates and synchronizes prompt-based AI agents directly into Azure AI Foundry using the Agent Service REST API. By defining agents in a local JSON manifest, the skill registers them server-side, making them instantly available for evaluation and invocation within the Foundry ecosystem. It is essential for deploying, updating, or managing AI agents at scale within the Azure cloud environment.