Skill UI

A comprehensive guide for designing and implementing 3D user interfaces across web, mobile, and cross-platform environments. Learn core principles like perspective transformations, Z-axis depth, and interactive rotation to build visually stunning, spatially immersive, and highly engaging product experiences.

Expert in creating immersive 3D experiences for web platforms using technologies like Three.js, React Three Fiber, and WebGL. Specializes in building interactive scenes, product configurators, and optimizing performance for diverse devices. Ideal for enhancing digital portfolios and modernizing websites with depth and visual impact while maintaining accessibility.

This expert conducts comprehensive audits to ensure digital products comply with WCAG standards. It specializes in identifying accessibility barriers, testing user experiences for users with disabilities, and providing step-by-step remediation guidance. Use it for auditing web or mobile platforms, establishing compliance, and improving overall inclusivity.

A comprehensive guide to building production-grade evaluation systems for Large Language Models (LLMs). This skill covers advanced methodologies, including Direct Scoring and Pairwise Comparison, alongside critical techniques for mitigating systematic biases (e.g., Position Bias, Length Bias). Learn how to select appropriate metrics and structure prompts to ensure reliable, consistent, and objective quality assessment across various AI applications.

Evaluate AI agent behavior using versioned cases and explicit verifiers. This skill helps compare agent or prompt changes, reproduce failures, and run regression tests. It ensures reliability on declared task distributions without inferring product readiness from generic scores. Includes methods for uncertainty reporting and safety verification.

Generate decision-ready reports from AI agent evaluation runs. This skill ensures autonomous, assisted, failed, and timed-out outcomes remain distinct and comparable. It defines metrics with correct denominators, handles latency and cost populations honestly, and maps evidence to decision gates. Ideal for benchmarking, regression testing, and production readiness validation of AI agents.

This skill enables deterministic fault injection testing for agent workflows to verify recovery mechanisms, state preservation, and safety boundaries. It generates fault matrices and event timelines to distinguish between recovered, contained, and unrecoverable failures without impacting production systems or real user data.

A comprehensive, multi-phase workflow designed for building sophisticated AI systems. This guide covers everything from designing autonomous agents and implementing single-agent logic, to orchestrating complex multi-agent systems and integrating advanced tools and memory mechanisms. Ideal for developers building production-grade AI applications using frameworks like CrewAI and LangGraph.

This skill enables building production-ready LLM applications, advanced RAG systems, and intelligent agents. It covers vector search, multimodal AI, agent orchestration, and enterprise integrations. Learn to design architectures, optimize embeddings, ensure safety, and deploy models at scale using frameworks like LangChain and vector databases.

This toolkit provides six production-ready AI engineering workflows including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior engineering partners with structured methodologies, quantitative scoring, and checklists for consistent results in LLM system optimization and security.

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

This skill guides the development of AI-powered products, focusing on LLM integration, RAG architecture, scalable prompt engineering, trustworthy AI UX, and cost optimization. It ensures products are production-ready rather than failing demos.