Skill UI

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

This elite workflow simulates the comprehensive process of a Senior Software Engineer and Product Designer. It enforces a rigorous development pipeline: starting with planning, solid architecture, and TDD implementation, through clean code practices, and technical auditing. Crucially, it includes mandatory, pixel-perfect UX visual validation to ensure the output is flawless, consistent, and production-grade.

This advanced workflow emulates an elite Senior Software Engineer and Senior Product Designer, ensuring maximum quality output. It enforces a rigorous, multi-step process including ultra-fast planning, solid architecture design, TDD implementation, clean code refactoring, technical auditing, and crucially, mandatory pixel-perfect UI/UX validation. Use this for complex projects demanding both robust code and flawless visual presentation.

An expert guide for performing complex data analysis, cleaning, and transformation using pandas DataFrames. This skill covers advanced techniques such as vectorized operations, efficient merging, time series resampling, group-by aggregations, and memory optimization, ensuring production-grade code and robust data validation.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

This skill acts as an expert guide for developing clean, idiomatic, and highly performant Ruby code. It specializes in the entire Rails ecosystem, covering metaprogramming, ActiveRecord patterns, gem development, and comprehensive testing using RSpec/Minitest. Use it to ensure best practices, optimal code structure, and maintainable architecture in Ruby projects.

This skill provides comprehensive guidelines for architecting and developing high-quality, maintainable software. It is deeply rooted in Clean Architecture and Domain-Driven Design (DDD) principles. It covers best practices for code decomposition, naming conventions, separation of concerns, and avoiding common anti-patterns. Use this skill when designing, analyzing, or developing complex, domain-specific software systems.

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.

An expert agent focused on enhancing code clarity, consistency, and maintainability. It simplifies complex code structures, eliminates redundant logic, and applies project-specific best practices, all while guaranteeing that the original functionality and behavior remain absolutely unchanged. Ideal for code reviews, cleanup tasks, or improving readability.

Learn how to handle errors in TypeScript using functional patterns like `Either` and `TaskEither`, eliminating messy try/catch blocks. This guide teaches that errors are simply data, allowing you to write cleaner, more predictable, and type-safe code that explicitly handles potential failures through explicit return values.

This guide offers a jargon-free, practical approach to functional programming using fp-ts in TypeScript. It focuses on the 80/20 principles, teaching core patterns like Pipe, Option, and Either. Learn how to handle missing values safely, manage errors explicitly, and chain operations cleanly, improving code readability without over-engineering.

A specialized tool for auditing source code (C/C++/Rust) to ensure sensitive data (keys, passwords, PII) is properly zeroized. It analyzes memory handling at the assembly and IR level to detect if compiler optimizations, such as dead-store elimination, have accidentally removed or weakened the secure cleanup process. Essential for maintaining security in critical codebases.