Skill UI

A specialized skill for optimizing database performance and scalability across PostgreSQL and MySQL. It guides users through analyzing slow queries, interpreting execution plans (EXPLAIN ANALYZE), designing optimal indexing strategies, tuning configuration parameters, and resolving bottlenecks like lock contention. Essential for DevOps engineers, database administrators, and backend developers.

Detect, analyze, and resolve production database deadlocks via the database-deadlock-detector plugin, which runs `/deadlock` to monitor lock contention, report transaction patterns, and suggest fixes for recurring issues.

This comprehensive workflow manages the entire lifecycle of notes within Evernote. It provides structured methods for creating new notes (supporting plain text, checklists, and ENML formatting), retrieving existing content, updating notes, and organizing both notes and notebooks/stacks. Essential for building full-featured note-taking applications and content management systems.

This skill details best practices for handling and structuring Evernote data. It covers designing local database schemas (SQL), advanced ENML content processing (to Markdown/HTML), managing attachments, and implementing reliable incremental synchronization using Update Sequence Numbers (USN). Ideal for developers building robust applications requiring deep, local data access and integrity checks for Evernote content.

A comprehensive toolkit designed to debug and troubleshoot Evernote API integrations. It provides deep logging of requests, responses, and timings. Features include an instrumented client wrapper for zero-config tracing, ENML content validation with auto-fix capabilities, and token inspection utilities to diagnose connectivity issues and quota limits.

A comprehensive diagnostic tool designed for deep debugging of complex Exa system issues. It systematically checks multiple layers (DNS, Authentication, Neural Search, Content Retrieval, etc.) and provides detailed latency profiling (p50, p95) to pinpoint performance bottlenecks or intermittent failures. Use this when standard troubleshooting steps fail or when preparing detailed evidence for support escalation.

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 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.

This guide provides comprehensive instructions for developers to install and set up the Exa SDK (exa-js for Node.js, exa-py for Python). It covers setting up API key authentication using environment variables and demonstrates client initialization for both major languages, enabling developers to utilize Exa's neural search API for web content retrieval.

This guide identifies and avoids anti-patterns and common integration mistakes when working with Exa's neural search API. It covers crucial topics such as distinguishing between keyword and neural searches, properly retrieving content, handling date filters, and managing client instances. Use this when reviewing Exa-powered code, onboarding new team members, or auditing existing integrations for robustness and correctness.

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.

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.