Skill UI

A comprehensive guide to diagnosing and resolving common errors and status codes (400, 401, 429, etc.) encountered when integrating with the Exa API. Provides specific solutions for malformed requests, authentication failures, rate limit issues, and content fetching errors, ensuring smooth API debugging and integration.

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.

A comprehensive guide containing minimal working examples for all core Exa search operations. It demonstrates basic metadata search, deep content extraction (text, highlights, summary), finding semantically similar pages, and retrieving content for specified URLs. Ideal for developers starting a new Exa integration or learning advanced RAG patterns.

A comprehensive runbook providing structured procedures for responding to Exa search API outages and integration failures. It guides users through initial triage, analyzing specific HTTP error codes (e.g., 401, 429, 5xx), implementing mitigation strategies, and completing thorough post-incident reviews and root cause analyses.

This guide details how to configure and manage the Exa search API across development, staging, and production environments. It ensures API key isolation, implements environment-specific search limits, and provides strategies for cost control and performance optimization by utilizing caching layers like Redis. Essential for scalable, multi-stage deployments.

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 tool for querying and analyzing deep blockchain data across multiple EVM-compatible networks, including Ethereum, Polygon, Arbitrum, and Optimism. It supports tracking transactions by hash, checking address balances, inspecting blocks, and resolving ERC-20 token holdings. Ideal for crypto analysts, developers, and researchers requiring programmatic, detailed insights into on-chain financial activity and protocol interactions.

FAISS (Facebook AI Similarity Search) is a library designed for efficient similarity search and clustering of dense vectors. It supports indexing and searching on billions of vectors at scale, featuring GPU acceleration and multiple index types (Flat, IVF, HNSW, PQ) to balance speed and accuracy. It is essential for building high-performance, large-scale applications requiring fast k-NN retrieval or pure vector similarity matching, especially in fields like RAG and recommendation systems.

This skill provides AI-powered capabilities for advanced image manipulation. It specializes in style transfer, allowing users to apply the aesthetic characteristics of one image to another, and object removal, enabling seamless retouching by eliminating unwanted elements. It is essential for designers, photographers, and artists requiring high-quality, creative photo editing and visual enhancement.

Provides FDA regulatory guidance for medical device manufacturers encompassing pathway strategy for 510(k)/PMA/De Novo submissions, QSR compliance, HIPAA assessments, and cybersecurity planning to support premarket filings.

This skill guides users through structured workshops to define comprehensive product features from scratch. It adopts dual perspectives (Product Manager and Developer) to gather detailed requirements, user stories, and non-functional needs. The output includes EARS-formatted functional requirements, testable acceptance criteria (Given/When/Then), and detailed implementation checklists, ensuring the feature is complete and ready for development.

Automates Figma workflows through Rube MCP and Composio, covering file inspection, component/token extraction, exports, comments, and team browsing while guiding schema discovery before each run.