This repository is a meticulously curated knowledge base and toolkit for AI video prompt engineering. Its core is a structured data pipeline: raw knowledge from official vendor documentation and YouTube masterclasses is reverse-engineered into standardized methodologies (21+ SOPs) and a centralized prompt database (`prompts/data/all-prompts.json`). This data is then exposed through a cross-model benchmark (`prompts/data/cross-model-matrix.json`), zero-dependency web tools (Prompt Browser, Cross-Model Matrix), and executable Claude Code Skills (`skills/`). The architecture is data-centric. The primary value lies in the curated, structured data assets and the rigorous methodology used to create them. The application layer (web tools, skills) is a thin, well-crafted consumer of this data. The repository actively manages its primary risk—model version drift—through an automated monitoring system (`scripts/monitor_models.py`) that hashes external endpoints and files issues on change. While primarily a data_research project, it exhibits strong application characteristics through its interactive tools and AI skill integrations. It is a living knowledge base, actively maintained with a clear contribution workflow and a strong emphasis on data provenance and reproducibility. The `cross-model-matrix.json` is a standout asset, providing a controlled benchmark for comparing prompt styles across 11 major AI video models.
VOID is a Netflix research codebase for removing objects from videos while also erasing their physical side effects, such as shadows, reflections, and secondary motion caused by the removed object. The core runtime is a two-pass diffusion inpainting system built on CogVideoX: pass 1 performs coarse object removal guided by a quadmask, and pass 2 refines temporal consistency using warped noise and optical-flow-inspired conditioning. The repo is primarily an application/research library rather than a pure data project: it ships a `videox_fun` Python package with model definitions, custom diffusion pipelines, inference and training scripts, a FastAPI/Ray inference API, Gradio UIs, and a Colab notebook, alongside synthetic data-generation code that uses Blender and Kubric.