Skill UI

A comprehensive guide to implementing Test-Driven Development (TDD) for Django projects. This skill covers the full Red-Green-Refactor cycle, utilizing pytest, pytest-django, and factory_boy to write robust unit and integration tests for Django models, views, and DRF APIs. Essential for developers aiming to build large-scale, high-quality Python applications.

This guide provides comprehensive, structured error handling patterns for modern applications using TypeScript, Python, and general architectural principles. It covers essential concepts like typed custom errors, Result patterns, API error transformers, and React Error Boundaries. Learn how to implement fail-fast mechanisms, differentiate user-facing messages from developer logs, and ensure system resilience against unexpected failures.

Flox is a declarative, Nix-based environment manager designed to create perfectly reproducible development environments. It handles system-level dependencies (like compilers, databases, and native libraries) and ensures that complex toolchains across multiple languages (Python, Node.js, Go, etc.) function identically across macOS and Linux. Ideal for solving 'works on my machine' issues and onboarding new team members instantly.

An expert skill for creating highly optimized, minimal, and fast-starting Windows installers for Python applications. Utilizing advanced techniques like Nuitka's extreme compilation, DLL footprint analysis, and Inno Setup packaging. This process ensures a clean, professional, and resource-efficient deployment, suitable for commercial products, rather than basic script conversion.

Manim is a powerful Python library used for creating high-quality mathematical and scientific animations. This skill facilitates the generation of educational videos and complex visualizations for presentations. It is ideal for researchers, educators, or content creators who need to visualize complex concepts in an engaging animated format. Note: This specific file is related to localization/translation.

This guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

A comprehensive guide to writing robust, efficient, and maintainable Python applications. Covers core principles like readability, explicit coding, and EAFP (Easier to Ask Forgiveness than Permission), advanced features like modern type hinting, context managers, and sophisticated exception handling techniques.

Master professional Python testing practices using the pytest framework. This guide covers Test-Driven Development (TDD) methodology, advanced features like fixtures (with different scopes), parametrization, assertion techniques, and achieving high code coverage, ensuring robust and maintainable software.

This guide provides a comprehensive onboarding process for RuView (WiFi-DensePose). It details three deployment tiers: Docker simulation for quick testing and UI exploration, source code build verification (Rust/Python), and live sensing setup utilizing ESP32 hardware. Ideal for new users or rapid prototyping.

This comprehensive tool provides a multi-stage verification pipeline for the RuView system. It executes full Rust workspace tests, runs a deterministic Python proof (Trust Kill Switch) to detect behavioral drift, and generates a detailed ADR-028 witness bundle. Use this before merging pull requests or when creating an official attestation package for recipients.

Provides comprehensive guidelines for safely upgrading GitHub Actions workflows and dependencies. Use this when encountering runtime deprecation warnings (e.g., Node.js or Python migrations). It outlines best practices, such as upgrading one action at a time and pinning releases using immutable commit SHAs, ensuring workflow behavior remains unchanged during modernization and validation.

This package ensures full reproducibility for international macroeconomics research. It is designed for projects utilizing complex, multi-source, and often restricted data (such as IMF or central bank files). It mandates capturing the entire data lineage, pinning the execution environment (R, Python, Stata), and documenting access instructions, allowing any authorized replicator to rebuild the analysis from scratch.