Skill UI

A comprehensive toolkit for the full software development lifecycle. It guides stack selection using a detailed decision engine, automatically scaffolds complex boilerplate projects across multiple popular stacks (Next.js, MERN, FastAPI, Django), and provides deep code quality analysis. Features include security vulnerability detection, complexity scoring, and dependency auditing, ensuring high-quality development from the start.

This skill provides comprehensive guidelines for architecting and developing high-quality, maintainable software. It is deeply rooted in Clean Architecture and Domain-Driven Design (DDD) principles. It covers best practices for code decomposition, naming conventions, separation of concerns, and avoiding common anti-patterns. Use this skill when designing, analyzing, or developing complex, domain-specific software systems.

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.

A detailed, multi-phase workflow that strictly enforces the Test-Driven Development (TDD) discipline using the Red-Green-Refactor cycle. This guide covers the entire software development lifecycle, from test specification and architecture design to minimal implementation, rigorous refactoring, and comprehensive integration testing. It mandates high code and test coverage (e.g., 80% line coverage) to ensure robust, maintainable, and high-quality production code.

This skill provides expertise in building robust, scalable, and intelligent test automation ecosystems. It covers AI-powered test generation, self-healing tests, Test-Driven Development (TDD), cross-platform automation (Web/Mobile/API), advanced CI/CD integration, and comprehensive quality engineering practices, ensuring high quality across the entire software development lifecycle.

A specialized skill designed to ensure robust software quality by covering the entire testing lifecycle. This includes defining test strategies, writing unit, integration, and E2E tests, and implementing advanced testing types like performance (k6), security (OWASP), and functional testing. It guides users through coverage analysis, debugging flaky tests, and generating detailed defect and quality reports.

This comprehensive workflow provides a complete cycle of quality assurance for software development. It covers every stage from defining the test strategy (TDD, Unit Testing) through integration, E2E testing, browser automation, performance profiling, security scanning, and final code review. Use this when setting up robust testing infrastructure or ensuring production-ready software quality.

A comprehensive skill for structured software development life cycle (SDLC). It guides users through adaptive phases—Specify, Design, Tasks, and Execute—ensuring that every feature is built with traceable requirements, atomic tasks, and rigorous verification. Features include an independent Verifier, persistent decision logging, and a self-improving lessons layer to enhance future development quality. Ideal for complex, multi-component feature implementation.

This workflow suite provides robust tools for automating complex, multi-step software development tasks. It allows users to define reusable workflows (like feature scaffolding, deployment pipelines, and quality checks) that are triggered via slash commands. By integrating persistent 'Memories', it maintains project context across sessions, significantly reducing repetitive prompting and standardizing development practices.

The /hub:spawn command launches N subagents that work on the same task simultaneously. Each agent operates within an isolated git worktree, ensuring true parallelism and resource separation. Users can optionally specify templates (e.g., `optimizer`, `refactorer`) to guide agents toward specific strategies for improving code quality, performance, or test coverage. Ideal for complex software engineering tasks requiring diverse, competing approaches.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.

This meta-skill set provides a structured framework covering the entire software development lifecycle (SDLC). It guides developers through every phase, from initial concept refinement and requirement specification to incremental implementation, testing, code review, and deployment. It enforces critical professional behaviors like surfacing assumptions, managing ambiguity, and maintaining scope discipline to ensure high-quality, robust engineering outcomes.