Skill UI

This guide provides a systematic, safe workflow for refactoring rapidly developed or AI-generated fullstack applications. It focuses on identifying and removing dead code, fixing broken imports, and consolidating repetitive logic while strictly maintaining existing API contracts, routes, and core functionality. Use this before launch to ensure a stable, maintainable production baseline, following a 'surgery, not demolition' philosophy.

A comprehensive, multi-phase quality assurance tool designed for Next.js fullstack applications. It verifies production readiness by checking code integrity, build success, SEO metadata, API authentication, route stability, performance, and security vulnerabilities. Use it as a mandatory pre-deployment checklist before promoting an app to production.

Use the composio CLI to orchestrate complex, end-to-end product releases across multiple services: Stripe for billing updates, Supabase for database migrations, and Vercel for frontend deployments. This single pipeline ensures that billing models, database schemas, and user interfaces are consistently updated in the correct sequence, guaranteeing reliable full-stack product launches.

This tool automates the diagnosis of production errors tracked by Sentry. By leveraging the Composio CLI, it retrieves comprehensive issue details, full stack traces, breadcrumbs, and associated commits. It maps stack frames to local source code, allowing agents to propose fixes and triage issues directly, eliminating manual copy-pasting.

This comprehensive skill provides an index of official blueprints and implementation guides for building full-stack, AI-powered applications using Weaviate. It covers advanced patterns such as Retrieval-Augmented Generation (RAG), agentic workflows, data exploration, and multimodal document search, guiding users through best practices for setup, clients, and frontends.

This skill guides users through an in-depth interview process to achieve maximum project context completeness (100% "Gold Code") for their .faf file. By systematically asking questions about project goals, architecture, and technology stack, it ensures all critical context slots are filled, making the project fully structured and highly readable by AI systems.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

A comprehensive guide detailing the architectural standards, technology stack, file structure, and coding patterns for building modern, AI-driven full-stack applications. It covers best practices for Next.js, FastAPI, database interaction (Supabase), and structured AI integration (Claude), ensuring consistency and scalability across the development lifecycle.