Skill UI

Assists with coverage report analyzer tasks within test automation, offering best-practice guidance, code/config templates, step-by-step workflows, and validation to keep reports reliable.

Automates css module generator workflows, offering best-practice guidance, code samples, and validation for production-ready frontend modules when working with React, Vue, or accessibility-focused builds.

Master Cursor Composer for multi-file AI edits by following structured steps for feature scaffolding, refactoring, and coordinated code generation across a project workspace, improving productivity through Composer patterns.

Integrates Git workflows with Cursor IDE, supplying AI-generated commit messages, code reviews, conflict resolution, and branch management guidance for faster version control operations.

Guides diagnosing and fixing Cursor codebase indexing issues like stuck processes, missing search results, or slow performance, ensuring AI features see updated project data.

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.

Guides developers through mastering Cursor IDE tab completion and AI code suggestions, explaining how to trigger, accept, and configure ghost text workflows for consistent code style and context-aware results.

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.

Guides engineers through data partitioner setups within ETL workflows, offering best practice advice, ready-to-run code, and validation for streaming and batch pipelines.

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 dataset loader creation within ML Training workflows, offering best-practice guidance, validation checks, and production-ready code to accelerate data prep and model tuning.

Automates guidance for date range analytics, covering SQL, visualization, statistics, and BI, with code generation and validation to support data analytics workflows.