Skill UI

An autonomous system designed to continuously improve code quality by scanning codebase metrics. It functions as an agent, proposing mechanical code changes, executing them, and committing only if defined metrics improve. It supports multiple modes (Scan, Scaffold, Run) for systematic, data-driven codebase refactoring.

This tool provides a preferred, end-to-end method for modifying code in real-world repositories. It simulates a professional development workflow: cloning the target repository, running changes on a dedicated task branch, and critically, verifying the implementation through an independent red $\to$ green $\to$ regression test loop. It is designed for fixing GitHub issues, implementing new features, or adding functions to existing codebases, ensuring the structural integrity and correctness of the changes.

A specialized tool designed to detect, suggest, and evaluate Gang of Four (GoF) design patterns within TypeScript and JavaScript codebases. It analyzes code structure, identifies code smells, and provides stack-aware refactoring recommendations, supporting frameworks like React, Angular, NestJS, and Vue.

This skill provides a structured way to understand complex codebases, functions, or architectural concepts. It allows the user to specify the depth of explanation (Simple, Standard, Deep), ensuring the output covers purpose, step-by-step flow, key decisions, and trade-offs for maximum learning efficiency.

This tool analyzes your entire codebase, assessing project identity, team size, test maturity, and existing architectural signals. It then recommends the most appropriate, AI-assisted development methodology stack (e.g., Microservices, Enterprise-Gov) and asks targeted questions to refine the plan, providing a concrete quick start guide.

This mode defines an autonomous, senior-level AI agent capable of resolving complex tasks from start to finish with minimal human supervision. It operates by strictly verifying every claim against real evidence (live web search, codebase, available tools). The agent prioritizes action over questioning, only interrupting the user for irreversible actions, genuine dead-ends, or critical ambiguities, ensuring maximum efficiency in hands-on engineering and operational workflows.

A comprehensive assistant designed to analyze existing codebases for adherence to SOLID principles, common code smells, and complexity metrics. It identifies anti-patterns like tight coupling, excessive responsibility, and duplication, providing prioritized, actionable refactoring suggestions to improve code maintainability and quality.

This tool analyzes a given codebase to automatically identify high-value, actionable automation routines. It surfaces use cases across three primary trigger types: scheduled cron jobs, external API calls, and GitHub repository events. By mapping manual pain points to autonomous workflows, it helps teams implement robust CI/CD and DevOps practices.

This tool automatically formats Python code using the industry-standard 'black' formatter. It ensures your codebase adheres to consistent style guidelines, improving readability and maintainability across the entire team. Ideal for development workflows and code reviews.

This skill addresses the common problem of documentation decay in long-lived software projects. It establishes a canonical governance layer by assigning four non-overlapping roles—Constitution, Map, Status, and History—to existing documentation. By linking these roles to the active agent harness, it prevents documentation drift, ensuring that all project information remains accurate, authoritative, and contextually linked to the current codebase, making it ideal for the maintenance phase.

This agent is dedicated to systematically exploring technical ideas, such as new features, refactoring, or bug fixes, *before* committing to code changes. It investigates the existing codebase, analyzes current architecture, compares multiple potential approaches (including pros, cons, and effort), and generates a structured 'exploration' report with a clear recommendation and identified risks. It ensures thorough planning and risk mitigation during the design phase.

Guides users through a complete Spec-Driven Development (SDD) cycle on their actual codebase, from exploration to archive. Interactive walkthrough with narrated phases, teaching by doing. Suitable for onboarding developers to SDD methodology.