Skill UI

A comprehensive guide on integrating Algolia search into modern React applications. This skill covers utilizing React InstantSearch with custom hooks (like useSearchBox, useHits) for type-ahead search, handling Next.js Server-Side Rendering (SSR) with InstantSearchNext, and implementing best practices for data synchronization and indexing strategies (e.g., partial updates, batching). Learn how to build high-performance, scalable search features.

This tool optimizes the codebase indexing within Cursor. It processes your entire codebase, converting code chunks into vector embeddings stored in a vector database. This enables advanced @Codebase semantic search, allowing users to ask natural language questions (e.g., "Where is authentication handled?") and retrieve the most relevant code snippets, significantly improving AI context awareness across chat and coding tasks.

A comprehensive guide to diagnosing and resolving common codebase indexing issues in Cursor IDE. Covers problems like stuck indexing, empty search results (@Codebase), stale data, and performance bottlenecks in large monorepos. Includes steps for cache clearing, configuration management, and optimizing resource limits.

A comprehensive guide to configuring Cursor's privacy features. This documentation covers enabling Privacy Mode for zero data retention, managing data flow from chat and indexing, implementing sensitive file exclusion using .cursorignore, and understanding telemetry settings. It also provides compliance mapping for GDPR, HIPAA, and SOC 2 compliance.

A comprehensive guide for setting up and maintaining Cursor IDE for enterprise and production use. It covers essential best practices including security hardening, defining project-wide coding rules, optimizing codebase indexing, ensuring data privacy compliance, and standardizing team development workflows. Essential for adopting AI coding tools in a professional environment.

A comprehensive reference guide for structuring development projects within the Cursor IDE. It details best practices for directory layout, defining layered rules (covering global conventions, security constraints, and technology-specific patterns like API routes and database access), and optimizing indexing strategies. This ensures maximum AI effectiveness, improves code maintainability, and streamlines the entire development workflow.

An expert architect specializing in designing, optimizing, and scaling entire data layers from inception (greenfield) to re-architecture. Proficient in selecting the optimal database technology (SQL, NoSQL, Graph, Time-series), designing robust schemas, implementing sophisticated indexing strategies, and planning complex migration paths. Suitable for defining data storage solutions that meet high performance, scalability, and data integrity requirements.

Provides a practical checklist and principles for designing database schemas, indexing strategies, query optimization, and migration planning for both SQL and NoSQL systems, plus guidance on spotting and fixing N+1 issues and denormalization trade-offs.

Actionable guidance for analyzing plans, indexing, connection pooling, partitioning, and detecting N+1 queries to keep PostgreSQL and MySQL workloads fast and reliable.

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.

A comprehensive pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.

Quick reference of PostgreSQL best practices covering indexing cheatsheets, data type choices, security policies, and configuration templates to guide optimization and troubleshooting workflows.