Skill UI

This comprehensive guide provides step-by-step instructions for integrating Langfuse LLM observability into various cloud and serverless environments. It covers best practices for deployment on platforms such as Vercel, AWS Lambda, Google Cloud Run, and self-hosted Docker, ensuring robust tracing and monitoring of entire AI workflows.

A comprehensive guide for deploying the application layer that interacts with Lindy AI agents. This includes setting up webhook receivers, managing callback handlers, and ensuring production readiness across multiple platforms like Docker, Vercel, and AWS. It covers prerequisites, multi-step deployment instructions, post-deployment verification scripts, and rollback plans for robust MLOps practices.

This skill integrates Continuous Integration/Continuous Deployment (CI/CD) pipelines with Linear issue tracking. It automatically detects deployment URLs (e.g., Vercel, Railway), adds detailed deployment comments to relevant Linear issues, and manages state transitions (e.g., moving an issue to 'Review' or 'Done') based on the deployment environment (staging or production). It ensures a seamless link between code commits, deployment status, and project management tickets.

This skill provides a comprehensive guide for integrating Lokalise into CI/CD pipelines. It outlines how to automate the download of the latest, reviewed translations during the build stage, ensuring that production environments always ship the most current localized content. Ideal for setting up deployment workflows on platforms like Vercel, Netlify, and GitHub Actions.

This comprehensive guide details the deployment process for Mistral AI-powered applications across various cloud and container platforms, including Vercel, Docker, Google Cloud Run, and self-hosted vLLM setups. It covers secure API key management, platform-specific secret configuration, and the full steps required to establish robust, scalable production pipelines for your AI model.

A comprehensive guide to building modern, high-performance full-stack applications using Next.js 14+ and the App Router. Covers advanced concepts including React Server Components (RSC), Server Actions, optimized data fetching with caching, implementing robust SEO via generateMetadata, and best practices for deployment and performance tuning (Vercel).

This guide provides comprehensive instructions for deploying applications powered by the Perplexity Sonar API. It covers setup for major cloud platforms like Vercel, Google Cloud Run, and Docker. Users can deploy production-ready endpoints, manage required secrets, implement edge functions, and utilize caching mechanisms (e.g., Redis) to ensure high availability and performance in production environments.

A comprehensive guide for deploying PostHog analytics platform across diverse production environments. Covers advanced configurations for Next.js/Vercel (including reverse proxy for ad blocker bypass and edge functions for server-side capture), self-hosted setups using Docker, and deployment patterns for Google Cloud Run. Ensures robust, reliable user behavior tracking regardless of the hosting platform.

Diagnoses and fixes Prisma connection pool exhaustion in serverless deployments (Vercel, Lambda, Netlify) by routing through pooling services, limiting client connections, and tuning timeouts to stop P2024/too many connection errors under load.

A comprehensive optimization guide maintained by Vercel, covering 45 rules for React and Next.js. Use this resource when building new components, implementing complex data fetching (client or server-side), refactoring code, or systematically improving bundle size and overall load times for high-performance web applications.

A comprehensive guide for migrating existing applications from platforms like Heroku, Railway, Vercel, and local development environments to Replit. It covers critical steps such as converting configuration files (Procfile to .replit), adapting to Replit's Nix-based system, and performing database migrations (PostgreSQL, MongoDB). Essential for developers upgrading or moving their deployment stack.

This guide outlines the end-to-end process for deploying Speak language learning features to modern cloud environments, including Vercel, Cloud Run, or containerized setups. It covers initial configuration, implementing the core logic using TypeScript, and performing production verification. Use this skill when setting up a new Speak integration in a production environment or managing the platform's deployment lifecycle.