Skill UI

Guides deliberate skill development during AI-assisted coding by offering optional 10–15 minute exercises after architecture work, schema changes, or “why” questions so developers stay engaged and understand decisions.

This comprehensive guide details how to optimize the Replit development environment. It covers setting up run commands, implementing hot reloading for various languages (Node.js, Python), configuring ports, managing development vs. production databases, and leveraging the Replit Agent for powerful AI-assisted coding. Use this guide when setting up a new project or refining your entire dev workflow.

This expert skill acts as a master orchestrator for Test-Driven Development (TDD), guiding users through the complete Red-Green-Refactor cycle. It specializes in coordinating complex, multi-agent testing workflows, integrating modern practices like BDD, ATDD, and AI-assisted test generation. Use it to ensure robust, highly maintainable, and thoroughly tested software systems across diverse technological stacks and complex development pipelines.

This skill enables AI-assisted API development workflows. It can generate type-safe API clients, comprehensive documentation, and SDKs for multiple languages (such as Python or TypeScript) directly from OpenAPI/Swagger specifications. It is essential for developers building complex integrations or needing to standardize API usage across multiple platforms.

This skill facilitates the rapid and standardized onboarding of new projects onto the Windsurf platform. It optimizes the development environment by configuring the Cascade AI agent, establishing necessary project rules (.windsurfrules), setting team context defaults, and ensuring best practices for AI-assisted coding. Use this when initiating a new project or integrating a team to maximize AI efficiency.

This skill provides AI-assisted capabilities for creating and managing Continuous Integration/Continuous Deployment (CI/CD) workflows using GitHub Actions. It can generate new workflows from project requirements, optimize existing pipelines, debug failures, and implement industry-standard security best practices, streamlining the entire development and deployment process.

This skill enables AI-assisted debugging and comprehensive error analysis within the development environment. It utilizes Cascade to analyze complex error messages, stack traces, and surrounding code context to identify root causes. Use it when troubleshooting code or encountering runtime errors to receive detailed fix recommendations and prevention strategies.

This skill provides comprehensive, AI-assisted linting configuration for ensuring high code quality standards. It assists with setting up and integrating major linting tools (such as ESLint, Prettier, and Stylelint), helping resolve complex configuration conflicts, suggesting best practices based on project patterns, and fully automating code quality enforcement across the development lifecycle. Use this when establishing or refining coding standards for a project.

This guide outlines critical reliability patterns for integrating advanced AI coding assistants into professional development workflows. It emphasizes best practices such as mandatory Git checkpointing, utilizing dedicated feature branches, breaking large tasks into incremental steps, and implementing comprehensive validation gates (linting, testing). Use this when developing fault-tolerant, AI-assisted applications to prevent accidental code breaks and maintain project stability.

This skill provides AI-powered assistance for advanced terminal operations within development environments. It helps users execute complex commands, suggests completions, explains cryptic error messages, and generates optimized shell scripts. Use it when you need advanced shell scripting help, command suggestions, or debugging support in your command-line interface (CLI) workflow.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.