Skill UI

A comprehensive guide to optimizing context window usage within AI coding environments like Cursor. Learn how to strategically use @-mentions, manage context pills, and leverage different context sources (codebase, documentation, web) to prevent context overflow and ensure the AI generates highly accurate, relevant, and context-aware code suggestions and answers for complex development tasks.

A comprehensive guide detailing the installation, setup, and authentication process for Cursor IDE across macOS, Linux, and Windows. It covers account sign-in, plan activation, and the seamless migration of settings and extensions from VS Code, ensuring a fully configured development environment.

A comprehensive guide to mastering Cursor IDE's keyboard shortcuts. This resource covers dedicated AI features (such as Inline Editing and Chat interaction) alongside essential standard IDE commands (navigation, refactoring, etc.). Learn how to utilize, customize, and manage keybindings via the UI or JSON for maximum development efficiency.

A comprehensive guide detailing how to optimize the entire software development lifecycle within the Cursor IDE. It walks users through an AI-augmented loop covering planning (Chat), scaffolding (Composer), implementing (Tab), refining (Inline Edit), testing (Chat/Terminal), committing, and reviewing code using integrated AI and Git features. Designed for maximizing developer efficiency and code quality.

A comprehensive guide designed to diagnose and optimize Cursor IDE performance. Learn advanced strategies for minimizing memory usage, speeding up indexing, tuning AI features, and managing large codebases to eliminate lag and maximize development efficiency.

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.

A comprehensive guide covering how to upgrade Cursor IDE versions, migrate settings and configurations from VS Code, and reliably transfer your entire development environment between different machines. Includes steps for auto-update, manual installation, and handling breaking changes.

This skill provides a comprehensive setup for Customer.io CI/CD pipelines. It includes GitHub Actions workflows for running unit and integration tests, managing test fixtures, and implementing pre-commit hooks. It ensures robust, automated validation of Customer.io code changes, providing a reliable quality gate during the development and deployment lifecycle.

This comprehensive guide identifies and explains the most common anti-patterns and mistakes when integrating with Customer.io. It covers crucial development topics, including proper API key usage, Unix timestamp formatting, ensuring correct event tracking order (Identify before Track), and optimizing API calls for performance. Use this as a definitive checklist for code review and developer onboarding.

Provides a comprehensive workflow for developing against Customer.io in local or staging environments. It implements environment isolation, dry-run clients, and service mocks to ensure development work does not affect production data, making unit and integration testing safe and reliable.

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.