Skill UI

This guide provides a comprehensive setup for a fast and reproducible local development workflow tailored for Exa API integrations. It covers establishing optimal project structure, implementing detailed unit tests using mock responses, and setting up robust integration tests. Ideal for developers who need to rapidly build, test, and iterate on applications relying on Exa's search and content fetching capabilities.

A comprehensive guide for migrating search pipelines from traditional keyword-based APIs (Google, Bing, Tavily) to Exa's advanced neural search capabilities. This guide covers building an adapter layer, implementing feature-flag traffic shifting, translating legacy query syntax, and leveraging Exa's unique features like semantic and similarity search for superior results.

This guide provides advanced technical patterns for optimizing Exa API performance in production workloads. It covers critical strategies such as selecting the optimal search type based on latency budgets (e.g., instant vs. neural), implementing robust result caching using LRU, minimizing content retrieval size, and executing parallel queries to maximize throughput. Essential for building reliable, low-latency, and cost-effective search integrations.

This skill provides robust, production-grade patterns for building fault-tolerant search service integrations, specifically targeting Exa. It covers implementing query fallback chains, circuit breakers to prevent cascading failures, exponential backoff for retries, and graceful degradation levels. Use this when designing mission-critical search features that must remain available and functional even if underlying services fail, rate limit, or suffer performance degradation.

A comprehensive guide to implementing production-ready patterns for the Exa-js SDK. Covers critical architectural concerns such as client singletons, type-safe wrapper functions, advanced error handling, exponential backoff retry logic, and robust response validation using Zod. Use this when integrating Exa APIs into large-scale applications or establishing team coding standards for maximum stability and maintainability.

This comprehensive guide details essential security practices for integrating the Exa API. It covers key areas such as secure API key management using environment variables, implementing content moderation to filter unsafe results, restricting results via domain allow/block lists, and sanitizing user queries to prevent injection attacks. Use this when hardening or auditing an Exa-powered application.

This skill provides comprehensive guidance on deploying Expo applications to production across multiple platforms, including iOS, Android, and Web, utilizing Expo Application Services (EAS). It covers the full lifecycle, from building and configuring profiles (eas.json) to submitting builds to App Store Connect, Google Play, and TestFlight. Furthermore, it details deploying web bundles, managing app versions, optimizing App Store metadata, and implementing automated CI/CD workflows.

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

This guide provides a comprehensive pattern for creating robust and scalable FastAPI routers. It covers essential best practices, including implementing proper authentication logic (required/optional dependencies), defining structured response models using Pydantic, and correctly assigning HTTP status codes. Use this skill when building structured and secure Python backend APIs.

Provides expert guidance on secure and performant file upload mechanisms for cloud environments. Covers best practices for using services like AWS S3 and Cloudflare R2, including implementing presigned URLs, handling large files via streaming, and optimizing images. Crucially emphasizes critical security measures such as validating file types using magic bytes, enforcing size limits, and preventing path traversal attacks.

A comprehensive guide to selecting and implementing the optimal Firecrawl architecture pattern for various data ingestion needs. Learn to choose between On-Demand, Scheduled Crawl Pipelines, and Real-Time Ingestion Pipelines based on data volume, required latency, and complexity. This is essential for designing scalable, production-grade scraping infrastructure, including RAG and knowledge base applications.

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.