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 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 detailed guide outlines how to configure the Flexport API for isolated development, staging, and production environments. It enforces best practices for data separation using distinct API keys, webhook secrets, and implements critical safety guards to prevent accidental data access or modification across environments.

A comprehensive checklist for pre-deployment and go-live procedures for Flexport logistics integrations. This guide covers critical areas such as API authentication setup, robust webhook handling (including idempotency and dead-letter queuing), data integrity validation, continuous monitoring setup, and safe rollback strategies when deploying to a production environment.

A comprehensive checklist designed to guide users through deploying Framer integrations to a live production environment. It covers best practices for plugins, code components, server API synchronization (CMS), and final site publishing, ensuring a thorough and reliable go-live process.

A collection of production-ready patterns for integrating frontend applications with backend APIs. It solves complex asynchronous issues such as race conditions, stale data, and duplicate requests. Patterns include centralized API layering, request cancellation (AbortController), exponential backoff retries, debouncing, and request deduplication, ensuring highly resilient and reliable user experiences.

This skill provides a complete solution for deploying Glean custom connectors as scheduled jobs on container platforms (e.g., Cloud Run). It covers building a production-ready Docker image to manage document ingestion, custom data source connectivity, and search query execution. Includes robust health checks and rolling update strategies to ensure continuous, high-availability indexing for enterprise search environments.

A comprehensive checklist designed to validate the deployment, robustness, and security of an enterprise search system powered by Glean. It covers critical areas such as API integration, secrets management, error handling, monitoring, and permissions, ensuring a stable and reliable user experience in a production setting.

This module provides production-ready patterns for interacting with the Glean enterprise search API. It features a typed client, implementing singleton patterns and robust mechanisms for search and indexing. Key patterns include batch pagination, comprehensive error handling (e.g., retries for rate limits), and a fluent request builder for executing complex, reliable API calls.

Skills this deployment guide covers building, configuring, and deploying a containerized Grammarly writing assistant service. It provides detailed instructions for production setup, including Docker configuration, environment variable management (for OAuth credentials), health checks, and zero-downtime rolling updates when integrating Grammarly's Text API for grammar, clarity, and tone checking.

This comprehensive checklist guides developers through the necessary steps to deploy a robust and reliable Grammarly API integration in a production environment. It covers critical aspects such as secure credential storage, rate limit handling, implementing text chunking, designing error resilience patterns (circuit breakers, retries), and establishing comprehensive monitoring and alerting systems to ensure high content quality and system uptime.