# claude2video **Repository Path**: likefallwind/claude2video ## Basic Information - **Project Name**: claude2video - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-03-19 - **Last Updated**: 2026-03-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # claude2video An AI agent skill for generating educational videos from a topic or Markdown outline. Built on [Manim Community Edition](https://www.manim.community/) with TTS narration via edge-tts. Adapted from the [Code2Video](https://github.com/showlab/Code2Video) paradigm (arXiv:2510.01174) into a Claude Code skill format. --- ## Installation **Step 1 — Install the skill into your project:** ```bash npm install claude2video ``` This copies the skill files into `.agents/skills/claude2video/` in your project. **Step 2 — Install Python and system dependencies:** ```bash bash node_modules/claude2video/setup.sh ``` Supports Ubuntu/Debian (including WSL2), Fedora, and macOS. **Step 3 — Use the skill in Claude Code:** ``` @claude2video 生成一个讲解「光合作用」的教学视频 ``` --- ## Overview The skill takes a **learning topic string** or a **Markdown course outline file** as input and produces a fully narrated educational video (MP4) through an 8-stage pipeline: ``` Planner → TTS → Coder → Critic → Audio-Video Merge → Final Assembly ``` Each section of the course becomes a separate Manim animation scene. Narration audio is synthesized first, and animation timing is then paced to match the audio exactly. --- ## Prerequisites | Dependency | Install | |---|---| | Python 3.9+ | — | | Manim Community v0.19+ | `pip install manim` | | edge-tts | `pip install edge-tts` | | ffmpeg | system package | --- ## Repository Structure ``` . ├── .agents/skills/claude2video/ # The skill (copied to user project on npm install) │ ├── SKILL.md # Entry point — 8-stage pipeline execution guide │ ├── planner.md # P_parse / P_outline / P_storyboard / P_asset prompts │ ├── coder.md # P_coder + Duration Control + ScopeRefine prompts │ ├── critic.md # P_refine (layout) + P_aesth (scoring) prompts │ ├── tts.py # CLI: storyboard.json → audio/ + durations.json │ ├── teaching_scene.py # TeachingScene base class (6×6 grid system) │ └── example_section.py # Working example — manim render -ql example_section.py ├── examples/ │ └── biology_cells.md # Sample Markdown course outline (7th grade biology) ├── scripts/ │ └── install.js # npm postinstall — copies skill to .agents/skills/ ├── package.json ├── requirements.txt # Python dependencies ├── setup.sh # System dependency installer ├── CLAUDE.md # Project instructions for Claude Code ├── .gitignore └── README.md ``` Output is written to `output/{topic}/` (gitignored): ``` output/{topic}/ ├── outline.json # Stage 1 output ├── storyboard.json # Stage 2 output (includes narrations) ├── teaching_scene.py # Copied from skill dir ├── sections/ │ ├── section_1.py # Generated Manim scene │ └── ... ├── audio/ │ ├── section_1/ │ │ ├── line_1.mp3 # Per-line TTS audio │ │ └── section_1.mp3 # Merged section audio (with 0.5s gaps) │ └── durations.json # Per-line durations for animation sync ├── sections_with_audio/ │ ├── section_1.mp4 # Video + audio merged │ └── ... ├── media/ # Manim render output └── final_video.mp4 # Final output ``` --- ## Usage Invoke the skill from Claude Code: ``` /claude2video ``` Or reference it directly in a prompt: > 请用 claude2video skill 生成一个讲解「勾股定理」的教学视频 The skill will ask whether to generate sections in **parallel** (faster) or **sequential** (step-by-step review) before starting. ### Input options **Option A — Topic string:** > 生成一个讲解「自由落体」的教学视频 **Option B — Markdown outline file:** > 用这个大纲文件生成视频：`my_course.md` The Markdown outline format: ```markdown # 课程标题 目标受众：高中生 ## 第一节：概念介绍 内容... ## 第二节：公式推导 内容... ``` Section count in the outline is preserved exactly. --- ## Pipeline Stages | Stage | Role | Description | |---|---|---| | 1 | Planner | Parse Markdown outline or generate outline from topic | | 2 | Planner | Build storyboard (lecture_lines + narrations + animations) | | 3 | Planner | Select & download reference image assets | | 4 | TTS | Synthesize narration audio, output durations.json | | 5 | Coder | Generate + render Manim section code (parallel or sequential) | | 6 | Critic | Visual layout refinement via frame extraction (up to 3 rounds) | | 7 | — | Merge section video + audio via ffmpeg | | 8 | Critic | Optional aesthetic scoring + final concatenation | --- ## Core Design ### 6×6 Visual Anchor Grid All animations are placed using a named grid (`A1`–`F6`). The left column is reserved for lecture notes; the right area is the animation canvas. ``` A1 A2 A3 A4 A5 A6 B1 B2 B3 B4 B5 B6 lecture | C1 C2 C3 C4 C5 C6 D1 D2 D3 D4 D5 D6 E1 E2 E3 E4 E5 E6 F1 F2 F3 F4 F5 F6 ``` Two positioning methods (no manual coordinates): - `self.place_at_grid(obj, 'B2')` — snap to grid point - `self.place_in_area(obj, 'A1', 'C3')` — fit within grid region ### Audio-First TTS Sync TTS durations are generated **before** code generation. The Coder receives `line_durations` and pads each animation block with `self.wait()` so video duration matches audio exactly. ### ScopeRefine Debugging Render failures are resolved through escalating scope: 1. **Line scope** — fix the offending line (≤3 attempts) 2. **Block scope** — rewrite the animation block (≤2 attempts) 3. **Global scope** — regenerate the entire scene from scratch --- ## References - Paper: [Code2Video: Automated Educational Video Generation Leveraging AI Coding Agents](https://arxiv.org/abs/2510.01174) - Reference implementation: https://github.com/showlab/Code2Video