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.
Flow Kit is an AI video production pipeline orchestrator that manages the end-to-end workflow of creating cinematic videos via Google Flow. It consists of a FastAPI backend (Python) with SQLite persistence, a React dashboard (TypeScript, shadcn/ui) for monitoring and control, a Chrome MV3 extension that bridges the local agent to flow.google.com for batchexecute RPCs, and a worker queue for asynchronous processing. The system supports project creation, character/entity management, scene image/video generation (Veo 3, Omni Flash), AI-powered video review with contact sheets and dimension scoring, TTS narration via OmniVoice, music generation via Suno API, post-processing (concat, branding, SFX), and YouTube upload with channel rules and SEO metadata. The architecture is modular with a provider abstraction layer (Flow, assistant, muse2api), an event bus for real-time updates, and a skill-based workflow system (40+ markdown recipes) that serves as the single source of truth for AI agents (Claude Code, Codex, agy). The codebase is actively maintained with recent migrations (September 2026 Flow API migration) and a comprehensive test suite, though it carries technical debt from multiple fallback layers, hardcoded payload shapes, and tight coupling to Google Flow's internal API.
This repository is an AI-driven story-to-video production platform. It is a full-stack Django 3.2 backend with Vue 2 frontend, Celery task queue, Redis pub/sub, and Docker deployment. The system ingests user story ideas and orchestrates a multi-stage pipeline: rewrite → storyboard → image generation (text2image/image2video) → camera movement → video generation → Jianying (å‰ªæ˜ ) draft export. All AI capabilities are consumed via external APIs (OpenAI, ComfyUI, Volcano Engine, etc.) through a pluggable AI client abstraction layer. Key entry points include RESTful APIs (DRF), SSE streaming for real-time progress, MCP (Model Context Protocol) JSON-RPC endpoints for agent integration, and a Vue-based canvas UI for visual workflow editing. The architecture follows a modular Django app pattern with clear separation: core infrastructure (pipeline orchestration, AI clients, Redis streaming), domain apps (projects, prompts, models, workflows, content, agent, MCP), and a frontend SPA. External dependencies include Redis, Celery, MySQL/PostgreSQL, external AI vendor APIs, and optional Jianying draft generation. The codebase demonstrates strong engineering practices with reusable patterns for AI client abstraction, Redis pub/sub streaming, MCP tool registration, and pipeline stage processors, but has notable technical debt: Django 3.2 (EOL), mixed async/gevent, Vue 2 (EOL), and some closed-source compiled components.
Jellyfish is an open-source, full-stack AI short drama studio application. It provides an end-to-end production pipeline for generating vertical short dramas from scripts: script parsing (division, element extraction, consistency checking), storyboard generation (shot breakdown, asset linking, camera language), image/video generation via multiple AI providers (OpenAI, Volcengine), and async task orchestration with Celery. The backend is built with FastAPI, SQLAlchemy 2.0 async, and Redis; the frontend uses React 18, TypeScript, Ant Design, and Vite. The application is production-oriented with Docker Compose deployment, GitHub Actions CI, and automated OpenAPI client generation. It emphasizes visual consistency through global seed, style inheritance, and asset reuse. While primarily an application, it includes a significant amount of AI/LLM agent code for script analysis, giving it a hybrid character with research-oriented structured extraction pipelines.