Skill UI

This skill provides comprehensive instructions and code examples for deploying Figma-powered applications, such as webhook receivers and design token APIs, to various production platforms including Vercel, Google Cloud Run, and Fly.io. It covers best practices for secret management, setting up robust webhooks, and implementing health checks to ensure reliable operation in a production environment.

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 skill provides a comprehensive guide for deploying a containerized Finta integration service using Docker. It connects to the Finta API to manage critical fundraising data, including tracking fundraising rounds, investor pipelines, and deal flow analytics. The implementation covers environment configuration, health checks, and rolling update strategies, ensuring zero-downtime operation during active fundraising campaigns.

This comprehensive guide serves as a quick reference for diagnosing and fixing common Flexport API v2 errors. It covers standard HTTP status codes (e.g., 401, 403, 422, 429, 5xx), detailing the causes and providing actionable fixes, including cURL examples and validation best practices. Essential for developers integrating supply chain solutions with Flexport.

This skill provides instructions for deploying Flexport logistics integrations across various cloud platforms, including Vercel, Fly.io, and Google Cloud Run. It is essential for setting up reliable, always-on webhook receivers, shipment tracking dashboards, and background workers necessary for comprehensive supply chain automation services in a production environment.

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 guide serves as a diagnostic reference for common Framer errors, covering issues across plugins, components, and Server APIs. It provides detailed causes and actionable fixes for problems like component rendering failures, incorrect API usage, and connection errors, helping developers efficiently debug and resolve integration issues.

This guide provides comprehensive instructions for setting up and authenticating Framer development environments. It covers two primary surfaces: the Plugin SDK (for UI running inside the editor) and the Server API (for headless CMS synchronization or CI/CD). Users will learn how to initialize projects, configure environment variables, and connect both client-side and server-side components.

This guide provides advanced techniques to boost Framer API performance. It covers critical optimization areas including batch CMS syncing to prevent timeouts, memoizing expensive React components for better render performance, establishing persistent WebSocket connections, and optimizing image URLs before CMS synchronization. Use this when aiming to improve overall speed and stability of Framer-integrated applications.

Provides robust patterns for handling API rate limits, throttling, and connection errors when integrating with the Framer API. It covers implementing exponential backoff, retry mechanisms, batching large CMS writes, and debouncing plugin operations to ensure reliable and high-throughput API calls.

This skill provides a comprehensive guide for analyzing, planning, and executing upgrades of the Framer SDK, plugin API, and migrating between different Framer platform versions. It helps developers detect breaking changes, follow version checks, and manage common migration patterns required when updating the Framer environment.

GateGuard is a powerful pre-tool-use hook designed to enhance the reliability of LLM-generated code. Instead of relying on mere self-evaluation, it forces the model to conduct deep, verifiable investigations (such as listing imports, analyzing data schemas, and generating rollback plans) before any code edit, file write, or destructive bash command is executed. This significantly improves code quality and minimizes unexpected bugs in complex codebases.