Skill UI

Repo-scan is a powerful tool designed for comprehensive auditing of large and complex codebases, especially monorepos. It performs cross-stack analysis across C++, Android, iOS, and Web technologies. Key features include classifying files (core code, third-party, build artifacts), identifying known libraries, and assessing code modules into four stages: Core, Extract, Refactor, or Deprecate. Essential for planning major refactoring efforts or inheriting legacy systems.

This skill provides deep insights into idiomatic Rust development. It covers core concepts like ownership and borrowing to prevent data races, robust error handling using `anyhow` and `thiserror`, advanced pattern matching with enums, and utilizing traits and generics for safe abstraction. Master these patterns when writing or refactoring complex, high-performance, and reliable Rust applications.

This tool initializes a structured multi-agent swarm with built-in anti-drift configuration. It is designed for orchestrating complex, multi-file tasks that require coordinated efforts from three or more specialized AI agents, such as feature implementation, cross-module refactoring, or comprehensive security audits. It supports various topologies (hierarchical, mesh, etc.) to ensure robust and manageable parallel work execution across large teams.

This pattern demonstrates how to build a robust, thread-safe data persistence layer in Swift using the 'actor' model. It combines fast in-memory caching with reliable file-backed storage, automatically eliminating data races and the need for manual locks. Ideal for offline-first applications and managing shared mutable state in modern Swift concurrency.

A guide to the Swift 6.2 concurrency model, which mandates default single-threaded execution to eliminate accidental data races. Learn how to use MainActor for isolated consistency and utilize the @concurrent attribute for explicitly offloading CPU-intensive tasks to background threads, ensuring safe and predictable application architecture.

This workflow guides developers through Test-Driven Development (TDD) principles. It mandates writing tests (unit, integration, E2E) before writing implementation code, ensuring minimum 80% test coverage. The structured process (Red -> Green -> Refactor) guarantees robust code quality, effective bug fixing, and safe feature addition, while integrating Git checkpoint commits for full traceability.

This comprehensive guide outlines an opinionated set of conventions for building robust Ruby on Rails applications. It covers core architectural principles, including rich models, state-as-records, minimal dependencies, and best practices for CRUD operations and testing. It is mandatory to consult these guidelines whenever designing, writing, reviewing, or refactoring any Rails code.

A comprehensive skill to extract and transcribe subtitles from any YouTube video URL. It utilizes a two-stage approach: first, attempting fast extraction via the yt-dlp CLI tool; failing that, it falls back to advanced browser automation using Chrome DevTools MCP to scrape the transcript panel content directly from the webpage DOM. The output is always a clean, timestamped plain text file.

This tool identifies, models, and optimizes compounding growth loops (e.g., viral, content, data, paid) within a business. By analyzing key metrics, calculating amplification factors, and determining cycle times, it pinpoints growth bottlenecks and proposes actionable strategies, ensuring sustainable and scalable business expansion.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.

A comprehensive guide for authors targeting top Human-Computer Interaction (HCI) conferences like CHI. It helps authors position their manuscripts, define required contribution types (e.g., system, dataset, empirical finding), and structure the paper to meet the rigorous standards of human-centered computing reviewers, focusing on user experience and design implication.

This guide provides rigorous guidelines for creating high-quality figures and tables for complex systems manuscripts, especially in agricultural science. It emphasizes shifting focus from single-factor bar charts to systemic visualizations that reveal interactions, trade-offs (e.g., Pareto fronts), model dynamics, resource flows (Sankey diagrams), and model performance (observed-vs-simulated fit). Exhibits must be self-contained, reproducible, and convey uncertainty.