Skill UI

A comprehensive guide and code implementation for setting up observability, metrics, and alerting for Exa search integrations. This skill tracks crucial KPIs such as search latency (P50/P95), error rates, result usage, and cache hit ratios. It is essential for monitoring search quality, debugging performance bottlenecks, and managing API resource usage in production environments.

A comprehensive guide detailing the end-to-end process for deploying Expo applications to production environments. This includes setting up build configurations, submitting to major app stores (iOS/Android), managing over-the-air (OTA) updates, and implementing robust release and version control strategies.

Automates guidance for implementing factory patterns within test automation, offering best-practice code generation, validation, and setup assistance for unit, integration, and mocking scenarios.

Guide for automating Fibery operations through Composio’s Rube MCP toolkit, covering connection setup, tool discovery, execution steps, and schema-compliant use of search/multi-execute calls.

Guides use of the Figma MCP server to pull design context, screenshots, variables, and assets, then convert nodes into production-ready code following project conventions and validation steps.

Implements user-provided Figma nodes into production-ready code via the MCP workflow, ensuring design-token compliance, asset handling, and 1:1 visual fidelity for agreed UI components.

This comprehensive skill covers the full scope of Firebase services, including Authentication, Firestore, Cloud Functions, and Hosting. It goes beyond basic implementation, focusing on critical architectural principles: adopting a query-first, denormalized NoSQL mindset. Key insights include mandatory security rule design, optimal data modeling for performance, and correctly utilizing modular SDKs to build scalable and secure backends.

This skill guides the end-to-end development, deployment, and secure integration of complex AI features into Firebase applications. It covers setting up Cloud Functions, Firestore/Storage rules, Authentication, and connecting robustly with Vertex AI/Gemini for advanced AI capabilities. Focuses on building production-grade, highly secure, data-intensive AI systems.

This guide details setting up automated Continuous Integration/Continuous Deployment (CI/CD) pipelines for Firecrawl scraping services. It covers configuring GitHub Actions, managing API keys, and implementing both mock unit tests and live integration tests. Use this when ensuring scraping behavior is validated automatically across pull requests and merges.

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.

This guide provides comprehensive instructions for setting up the Firecrawl SDK, covering both Node.js and Python environments. It details how to install the necessary packages via npm or pip, and how to securely configure the API key using environment variables. Use this guide when initializing web scraping functionality or integrating Firecrawl into a new development project.

This comprehensive guide details how to set up a robust, local development environment for Firecrawl integrations. It utilizes self-hosted Docker containers and Redis to eliminate reliance on paid API credits during development. The setup supports both unit testing (with mocked SDK) and full integration testing against the local instance, ensuring a seamless and efficient workflow for scraping and web data extraction projects.