Skill UI

This guide provides production-ready patterns for implementing the TwinMind AI memory and meeting intelligence REST API. It covers best practices for authentication, storing contextual memories, searching knowledge bases, and processing meeting transcripts to extract actionable insights. Use this when building robust, scalable AI-powered applications or standardizing team coding practices.

Comprehensive guidance for mastering advanced TypeScript type features, including generics, conditional types, mapped types, and utility types. This skill is essential for building robust, maintainable, and highly type-safe applications, such as large-scale libraries, complex API clients, and advanced state management solutions.

This skill provides deep, practical expertise in TypeScript and JavaScript, covering advanced type-level programming, performance optimization, and complex build toolchain management. It is ideal for architects building large-scale, highly performant, and type-safe corporate applications, including monorepo setup and intricate dependency resolution.

A comprehensive toolkit for senior UI designers to build robust and scalable design systems. It guides the generation of design tokens (colors, spacing, typography), structuring component libraries (Atoms to Organisms), implementing responsive layouts, and preparing detailed developer handoff documentation. Essential for maintaining visual consistency and bridging the gap between design and development.

An expert-level skill set covering the entire spectrum of user-centered design. Mastery includes building scalable design systems using atomic principles and design tokens, conducting deep user research (usability testing, persona creation), ensuring WCAG accessibility compliance, and managing cross-platform implementation (web, mobile, desktop). Ideal for developing complex, inclusive, and highly structured digital products.

An expert guide for building high-performance, cross-platform games using Unity 6 LTS. This skill covers advanced C# programming (DOTS, Job System), modern rendering pipelines (URP/HDRP), comprehensive performance optimization (Profiling, Culling), advanced game architecture (ECS, MVC), and networked multiplayer implementation. Ideal for professional game developers tackling complex, large-scale projects.

This skill provides production-grade patterns for Unity's Data-Oriented Technology Stack (DOTS). It covers essential components like Entity Component System (ECS), the Job System, and the Burst Compiler, enabling developers to build high-performance, scalable games. Use this when optimizing CPU-bound logic, managing thousands of entities, or migrating traditional OOP game code to a data-oriented design.

Neon is a robust serverless Postgres platform that separates compute and storage, offering advanced features like autoscaling, branching, and instant restore. This guide provides comprehensive resources on setting up, connecting to, and utilizing Neon databases. It is fully compatible with standard Postgres and integrates seamlessly with various languages, ORMs, and developer tools for robust data management.

This skill scans web application endpoints and cookies to spot missing CSRF safeguards—tokens, double-submit cookies, SameSite attributes, and origin checks—then documents vulnerable paths and mitigation steps for security reviews.

This advanced workflow orchestrates complex, multi-node GPU clusters on Vast.ai. It is designed for large-scale distributed machine learning training, automatically handling spot instance preemption and resuming jobs from checkpoints. Furthermore, it provides comprehensive cost analysis by tracking billing history, allowing users to optimize GPU spending and manage cluster teardown efficiently.

This deep dive guides users through migrating complex GPU workloads, such as deep learning training pipelines, from major hyperscalers (AWS, GCP, Azure) or other cloud platforms to Vast.ai. It covers critical steps including cost comparison analysis, adapting Docker images for new environments, securely handling cloud credentials via environment variables, and implementing robust validation and rollback procedures to ensure a smooth transition.

A comprehensive guide to optimizing deep learning workflows and maximizing efficiency on cloud GPU platforms like Vast.ai. Learn techniques for selecting optimal instances by performance-per-dollar, reducing startup latency, tuning data pipelines (DataLoader, pin_memory), and scaling multi-GPU training using PyTorch and Bash scripting.