Skill UI

A comprehensive guide detailing best practices for executing commands on Windows using PowerShell and CMD. It addresses critical pitfalls such as handling spaces in file paths, ensuring correct character encoding (UTF-8), using native cmdlets, and managing long path limits. Essential for developers building robust, cross-platform Windows automation scripts.

This skill generates championship-grade test suites for any project using the WJTTC methodology. It analyzes the codebase, classifies components, and scaffolds executable test files. It emphasizes pre-defined test plans, inline testing, and two-layer testing architecture to ensure high code quality and signal integrity in CI/CD pipelines.

F1-inspired test executor and reporter: runs test plans, reproduces and root-causes bugs, audits CI signal integrity, and files tiered WJTTC reports with Brake/Engine/Aero/Tyre/Pit verdicts.

This skill fetches the latest writing guidelines from a specified source URL and reviews provided files against those rules. It outputs findings in a terse format, ensuring content compliance with established standards. Useful for maintaining consistency in documentation or technical writing workflows within development environments.

Create, edit, and analyze .xlsx files with pandas and openpyxl. Enforce zero formula errors, color coding, number formatting, and dynamic Excel formulas for financial models and data analysis.

This tool converts YouTube talks into local study notes featuring slide snapshots, transcripts, and editable annotations. It builds a personal library where each video becomes a markdown file with an interactive viewer. No database or cloud service is required, ensuring full ownership of your data. Ideal for researchers and students creating deep-dive notes from video content.

This skill fetches raw text transcripts from any YouTube video. It prioritizes using the DeepAPI service for reliable, server-side scraping, which bypasses local bot detection issues. If DeepAPI fails or the API key is unavailable, it gracefully falls back to using the local `yt-dlp` tool. The output is always saved as a clean, unformatted .txt file.

This skill optimizes LLM context usage through prompt caching, log pruning, and AST-based file inspection. It enforces ultra-dense output, minimizes filler text, and manages token budgets efficiently. Ideal for technical tasks requiring precise code edits, structured data handling, and reduced context window consumption during long sessions.