Skill UI

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.

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.

Automated Data Quality Checker offers guidance, best practices, and production-ready code for validating pipelines whenever you work with ETL, streaming, or data transformation scenarios.

A comprehensive runbook designed for on-call engineers to manage Databricks incidents, outages, and major job failures. It provides structured procedures for immediate triage, decision tree guidance (covering cluster failures, code errors, and data quality issues), and steps for evidence collection and postmortem documentation, ensuring rapid and systematic incident resolution.

This comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

This specialized skill acts as an expert debugger, designed for deep root cause analysis of software errors, test failures, and unexpected behavior. It guides the user through systematic debugging processes, including capturing stack traces, isolating failure points, implementing minimal fixes, and providing comprehensive reports detailing the root cause, evidence, specific code patches, and prevention recommendations. Ideal for complex bug resolution and quality assurance.

This expert guide provides a systematic, structured methodology for debugging complex software issues. It teaches users how to analyze stack traces, correlate logs, establish consistent reproduction steps, and apply hypothesis-driven testing to isolate and resolve bugs, crashes, or unexpected behavior in any codebase. Essential for professional troubleshooting and quality assurance.

This skill teaches the critical principles and decision-making processes for safe production releases. It covers platform selection (e.g., Kubernetes, Vercel), mandatory pre-deployment verification (Code Quality, Environment), structured workflows (Prepare, Backup, Deploy, Verify), and advanced strategies like Blue-Green and Canary deployments. Focuses on thinking through risk mitigation, not just executing scripts.

A comprehensive, end-to-end workflow bundle guiding developers through the entire software development lifecycle. It covers project scaffolding, frontend development (React/Next.js), robust backend architecture, database design, rigorous testing (TDD, E2E), code quality review, and modern CI/CD deployment practices for web and mobile applications.

This comprehensive guide details how to configure robust CI/CD pipelines for Documenso integrations using GitHub Actions. It covers best practices for automated unit testing with mocks, comprehensive integration testing against a staging environment, and secure deployment workflows, ensuring code quality and reliability from commit to production.

This skill provides comprehensive guidance on building reliable, fast, and maintainable end-to-end (E2E) test suites. It ensures code quality by validating critical user journeys and catching regressions in complex workflows before deployment. Essential for setting up robust CI/CD pipelines, cross-browser compatibility, and establishing rigorous QA standards.

A comprehensive toolkit for evaluating the performance of code generation models. It supports benchmarking against industry standards like HumanEval, MBPP, and MultiPL-E across multiple languages. Use this tool to quantitatively compare different LLM coding abilities and measure code synthesis quality using pass@k metrics.