Skill UI

A comprehensive guide to avoiding common pitfalls when using AI-powered IDEs like Cursor. It covers best practices for AI interaction, secure coding habits (handling secrets, credentials), project configuration (rules, ignore files), and performance optimization. Essential for maintaining high code quality and security in AI-assisted workflows.

A comprehensive guide detailing how to efficiently manage large, complex codebases using Cursor's multi-root workspace features. Learn to set up monorepos, utilize selective indexing, and maintain cross-project context across multiple repositories, ensuring fast and accurate code understanding in massive projects.

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.

Manages and enforces project-specific coding standards and architectural conventions within the Cursor IDE. Developers can define rules using `.mdc` files, specifying required patterns (e.g., TypeScript strict mode, React component structure, API validation). This system guides the AI assistant by providing deep context, ensuring code consistency and adherence to best practices across the entire codebase.

A comprehensive guide for administrators and teams on setting up Cursor for enterprise use. It covers organizational plan selection (Pro, Business, Enterprise), advanced features like SSO, SCIM provisioning, and Privacy Mode enforcement. The guide details setting up roles, managing usage analytics, defining shared project rules via Git, and providing a structured onboarding checklist to ensure seamless adoption and secure coding practices across the organization.

This comprehensive guide details how to install and configure the Customer.io SDK for programmatic integration in Node.js projects. It covers the critical setup of authentication using two distinct API keys: the Track API Key (for user identification and event tracking) and the App API Key (for sending transactional messages and broadcasts). Essential for developers integrating Customer.io's core customer data and marketing features.

A protocol that provides LLM agents with persistent structural memory of a codebase. It maps the project as a directed graph, tracking not only dependencies (what imports what) but also the 'reasons' (why it connects). This allows agents to understand context, perform impact analysis, and navigate large repositories without loading the entire source code into the context window.

Automates database schema design guidance for backend projects, activating whenever you ask about schema patterns, best practices, or implementation in node.js, Python, Go, microservices, caching, or messaging contexts.

A comprehensive guide to setting up a minimal working example on Databricks. This skill covers creating a development cluster, uploading and executing notebooks (Python SDK), running tasks as jobs, and setting up a Serverless SQL Warehouse. It is ideal for new users or initializing a basic data engineering project.

A comprehensive guide detailing the setup process for Databricks CLI v2 and Python SDK. It covers essential steps for connecting to various Databricks workspaces, including configuring Personal Access Tokens (PAT), interactive OAuth U2M flows, and secure OAuth M2M service principals for CI/CD pipelines. Essential for developers integrating Databricks into their projects.

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.