Skill UI

A comprehensive guide for deploying Exa-powered search APIs to production environments. This skill covers best practices for integrating Exa into Vercel Edge Functions, Docker containers, and Google Cloud Run. It includes detailed steps for secret management, implementing Redis caching for performance, and setting up health check endpoints, ensuring a robust and scalable production search solution.

A comprehensive guide for deploying web scraping applications powered by Firecrawl to various production environments. Covers Vercel serverless functions, Google Cloud Run, and self-hosted Docker setups. Learn how to configure platform-specific secrets, implement API routes, and handle asynchronous crawl results via webhooks for scalable, reliable production service.

A comprehensive guide for deploying Fireflies.ai integrations into production environments. This skill covers setting up the GraphQL client, implementing secure webhook receivers with signature verification, and managing multi-platform deployments across Vercel, Docker, and Google Cloud Run. Ideal for integrating AI-powered meetings data into production services.

Manages Firebase/Firestore CRUD, queries, batch jobs, and security for applications while extending to A2A agent messaging, MCP server integration, and Cloud Run coordination for advanced workflows.

Guides building production-ready serverless apps on GCP with Cloud Run services and Functions, covering containerized APIs, event-driven Pub/Sub workflows, cold-start tuning, and deployment automation.

Sets up production-grade Genkit infrastructure via Terraform, covering Firebase Functions, Cloud Run, GKE, AI monitoring, and CI/CD wiring so deployments run securely at scale.

Comprehensive guide for deploying applications powered by the Groq inference API. Covers best practices and specific setup instructions for multiple production environments, including Vercel Edge Functions, Google Cloud Run, Docker containers, and other containerized platforms. Essential for setting up robust, low-latency, production-ready AI services.

Automates Google Cloud IAM binding tasks by guiding users through best practices, generating production-ready code and configs, and validating outputs across compute, storage, BigQuery, Vertex AI, Cloud Run, Firebase, and other GCP services whenever iam binding creator is mentioned.

This comprehensive guide provides a multi-platform deployment solution for Ideogram-powered image generation APIs. It includes detailed code and setup steps for Vercel, Google Cloud Run, and self-hosted Docker containers. The solution addresses key production concerns, including secure secret management (API keys), handling generation timeouts, and ensuring generated images are persisted to durable cloud storage (like S3) with CDN integration for reliable service operation. Use this when migrating Ideogram prototypes to a production environment.

This guide provides comprehensive instructions for setting up robust CI/CD pipelines for Instantly.ai integrations using GitHub Actions. It covers unit and integration testing against both a mock server and the live API, includes API scope validation scripts, and details the deployment process for webhook receivers (e.g., to Cloud Run). Use this when ensuring continuous validation and reliable deployment of your Instantly API code.

This skill guide provides comprehensive instructions for deploying Instantly.ai's API v2 webhook receivers and automation services to major cloud platforms. It covers setting up resilient, public HTTPS endpoints using Vercel serverless functions, Google Cloud Run containers, or Fly.io services. Use this when moving from local development to production or setting up robust webhook endpoints that handle real-time event processing.

This skill provides a comprehensive guide for deploying LangChain chains and agents into production-grade APIs. It supports multiple development stacks, including Python (LangServe/FastAPI) and Node.js (Express). Topics covered include containerization using Docker, setting up robust health checks, and deploying to major cloud platforms like Google Cloud Run and AWS Lambda. Learn how to build scalable, observable, and enterprise-grade API endpoints for advanced LLM applications.