Skill UI

This skill is designed for conducting comprehensive production audits. It helps users systematically check processes, identify quality gaps, and ensure compliance standards are met in a production environment. (Note: The provided content is a translation placeholder.)

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.

This module is designed to document, track, and manage quality nonconformance issues found during manufacturing or process audits. It is essential for root cause analysis, implementing corrective and preventive actions (CAPA), and ensuring compliance with industry quality standards. Use it when recording deviations from specified requirements.

A comprehensive guide detailing idiomatic React 18/19 patterns. Covers core principles like component purity, side effect management, hooks discipline, state location decision trees, and advanced topics such as Server/Client Component boundaries (RSC), Suspense, error handling, and using React 19 form actions for building robust, performant, and accessible UIs.

This guide provides comprehensive patterns for behavior-focused testing of React components, custom hooks, and pages. It covers best practices using React Testing Library (RTL), including accessibility assertions, simulating user interactions (userEvent), and network mocking with MSW. It also helps developers define clear test boundaries between unit component tests and full E2E flows.

This skill provides a structured framework for designing and implementing composable recommendation, ranking, and feed pipelines. It models the industry-standard six-stage pattern: Source, Hydrator, Filter, Scorer, Selector, and SideEffect. Use it for any system that selects "the top K items for a (user, context)", such as social feeds, ad ranking, RAG rerankers, or content recommendation engines, ensuring a robust and scalable architecture.

A structured framework for managing complex, iterative, and high-stakes decision processes (e.g., stochastic optimization, ensemble comparison, model rollouts). It mandates the creation of a detailed, auditable 'ledger' that records every prior result, new input, search space metric, and decision mark. This tool enforces rigorous evidence trails and defaults to safe, dry-run modes, ensuring that confidence is built through verifiable, step-by-step proof rather than assumed certainty.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

This tool synthesizes deep, accumulated research findings stored in memory into structured, actionable reports. It systematically grades evidence quality (High, Medium, Low), identifies and resolves conflicting claims, and generates comprehensive outputs including an executive summary, ranked key findings, limitations, and concrete recommendations. Ideal for transforming raw research data into strategic insights.

Standardized expert guidance for return authorization, inspection, grading, disposition decisions, refunds, fraud detection, and vendor recovery in retail and e-commerce operations.

This skill acts as an expert design system guideline author for the Roku brand. It provides structured, implementation-ready guidance covering style foundations, component anatomy, accessibility (WCAG 2.2 AA), and advanced authoring workflows. It ensures consistent, developer-first UX/UI design principles for modern dashboards.

RouterBase acts as a unified, OpenAI-compatible API gateway, allowing applications to route requests across diverse large language models (LLMs) like GPT, Claude, and Gemini. It helps developers migrate existing OpenAI SDK calls, implement sophisticated primary/fallback model selection based on cost, latency, and quality, and manage multimodal payloads (image, audio, video) from a single, standardized endpoint.