Skill UI

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.

Automates fastify plugin creation within backend development workflows by guiding through best practices, generating production-ready code, and validating configurations for microservices, caching, and database integrations.

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.

Scans Android APKs for Firebase misconfigurations, testing databases, storage, authentication, and cloud functions to surface vulnerabilities during mobile security audits.

Automatically assists with generating Firebase security rules, offering best-practice guidance, template configurations, and validation checks tailored for GCP compute, storage, and data workloads.

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.

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 outlines how to implement robust Role-Based Access Control (RBAC) for Firecrawl scraping resources. It allows enterprises to manage multiple API keys, enforce per-team credit consumption limits, and restrict scraping to specific allowed domains (allowlisting). Use this when scaling scraping operations across various departments or services that require granular resource governance and budget tracking.

This skill provides advanced methods for load testing and scaling web scraping pipelines using Firecrawl. It manages rate limits and optimizes throughput by implementing concurrency control, batch processing, and queue-based request throttling. Essential for capacity planning, stress-testing scraping performance, or maximizing data ingestion efficiency across various service plans.

This guide provides comprehensive setup for implementing CI/CD pipelines for Fireflies.ai integrations. It covers best practices for validating GraphQL queries, running mock-based unit tests (using Vitest), and executing live integration tests against the actual API. It integrates seamlessly with GitHub Actions, ensuring that code changes are automatically validated in a stable, automated testing environment before deployment. Essential for maintaining API stability and robustness.

Provides a robust, event-driven reference architecture for building meeting intelligence platforms using Fireflies.ai. This guide details the full production pipeline, covering multi-source meeting ingestion (Zoom, Teams), webhook processing, and subsequent data enrichment into structured action items, analytics reports, and database storage. Ideal for designing complex integrations or advanced meeting analytics systems.

Automated Firestore index guidance for GCP Skills, triggering on index creator requests to deliver best-practice configs, validation, and production-ready code for Firebase/BigQuery/Vertex AI workloads.