Skill UI

This skill is designed to configure a Rails project workspace to function correctly within Conductor, a platform for parallel coding agents. It sets up the necessary scaffolding, including `conductor.json`, executable setup scripts, and server scripts. Additionally, it updates critical Rails configuration files (such as Sidekiq, Cable, and development environment settings) to ensure proper environment variable handling, Redis isolation, and port management for reliable parallel agent execution.

This advanced system transforms session interactions into reusable, atomic knowledge units called "Instincts." By hooking into tool usage and session activity, it observes patterns (like preferred coding styles, error fixes, or repeated workflows). These instincts are then clustered and "evolved" into structured Skills, Commands, or specialized Agents, capturing deep contextual knowledge with weighted confidence scoring to enhance development productivity.

This skill enables AI agents to manage complex, multi-step projects by persisting planning artifacts (task_plan.md, findings.md, progress.md) to the local file system. It ensures that work survives context loss and allows for automatic session recovery, making it ideal for long research tasks or complex coding workflows.

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 comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.

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.

A powerful agentic workflow for complex software development. Cascade Write mode acts as an autonomous coding agent, allowing users to build features, refactor code across multiple files, run terminal commands, and debug errors—all from a single natural language prompt. Ideal for full-stack development, complex system building, and large-scale code refactoring.

This skill provides a comprehensive, hands-on introduction to Windsurf's core AI capabilities. Users will experience Supercomplete for advanced tab completions, Cascade for agentic chat and direct code modifications (Write mode), and Command for targeted inline code adjustments. It is ideal for developers starting with Windsurf or needing a quick overview of its powerful IDE integrations.

Audits GitHub Actions workflows that invoke AI coding agents to surface prompt injection and sandbox risks, tracing attacker-controlled inputs through env vars, reusable actions, and workflow references before deployment or code review.

A powerful tool to scaffold and ship production-ready Model Context Protocol (MCP) servers directly from OpenAPI specifications. It supports Python and TypeScript, ensuring schema validation, safe evolution, and robust tool definition generation, eliminating the need for manual wrapper coding. Use it to expose existing REST APIs to LLM agents or build shared tool integrations.

Performs static security analysis on GitHub Actions workflows that incorporate AI coding agents (e.g., Claude, Gemini, OpenAI). This skill helps identify critical data flow vulnerabilities, such as where attacker-controlled input can reach AI agent prompts or internal systems via CI/CD pipelines, going beyond simple expression checks.

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.