Skill UI

Guides branch completion by verifying tests, identifying the base branch, presenting merge/PR/keep/discard options, and cleaning up worktrees when appropriate.

Guides execution of implementation plans by dispatching isolated subagents per task, enforcing a spec-first then quality review loop, and preserving overall coordination context for faster, high-quality iterations.

Detailed guidance for a strict test-driven development cycle: write a failing test first, confirm the failure, add minimal code to pass, refactor cleanly, and repeat so every feature, fix, or refactor ships with confidence.

Guides authors through adapting test-driven development to agent skills documentation, covering when to create skills, required background, directory structure, and how to run pressure scenarios to ensure compliance before publishing.

Guides product managers from discovery notes to an aligned PRD by orchestrating problem framing, user context, solution definition, and success criteria so engineering and stakeholders share a clear source of truth.

Creates assumption-based proto personas from available research, market signals, and stakeholder insights to align teams early in product development and surface validation gaps.

This comprehensive guide facilitates the development of a production-ready Minimum Viable Product (MVP) for a SaaS business. It covers the entire lifecycle, including idea validation, modern tech stack selection (Next.js, Supabase, Stripe), architectural design for multi-tenancy, database schema planning, authentication setup, payment integration, and a detailed pre-launch checklist for technical and marketing readiness.

Guides teams through writing Mike Cohn‑style user stories with Gherkin acceptance criteria so product needs become actionable, testable development work with shared understanding.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

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 comprehensive guide and skill for building robust, production-ready REST API endpoints. This skill covers full lifecycle development, including input validation, centralized error handling, authentication/authorization checks, CRUD operations, pagination, and generating proper API documentation. It ensures that back-end services are secure, scalable, and adhere to industry best practices.

This skill guides the user through a structured process of requirement clarification before any development work begins. It ensures that all key details—including objectives, scope, constraints, and acceptance criteria—are explicitly defined. It prevents costly rework by forcing a pause and systematically identifying 'must-have' missing information.