Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/coding-ax/docvideoer/claude-mdgit clone --depth 1 https://github.com/coding-ax/docvideoerWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01560 | $0.01560 |
| Opus 5 | $0.00780 | $0.00780 |
| Sonnet 5 | $0.00312 | $0.00312 |
| Haiku 4.5 | $0.00156 | $0.00156 |
Grade A, and why
docvideoer CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — docvideoer
docvideoer 是一个 AI 驱动的文档转视频工具,将文档内容(URL 或文本)自动转换为讲解视频。它编排了文档抓取、内容分析、分镜生成、TTS 语音合成、Remotion 视频渲染的完整流水线。
触发方式
当用户使用 /docvideo 命令或在对话中说「把这个文档转成视频」「根据这篇文章生成视频」等意图时激活本 skill。
外部 Skill 依赖
Remotion 相关实现依赖官方 remotion-best-practices skill。处理 Remotion 项目、组件、时间线、动画、音频同步、字幕或渲染问题时,优先使用该 skill;如果未安装,提示用户运行:
npx skills add https://github.com/remotion-dev/skills --skill remotion-best-practices
工作目录
所有视频项目在用户当前工作目录的 docvideo-output/ 下创建。每个视频项目有独立的子目录,包含脚本、素材、音频和最终的 Remotion 项目。
核心流水线(6 步)
第 1 步:获取文档内容
- 如果用户提供 URL,使用
scripts/fetch-doc.mjs抓取网页内容(自动提取正文、翻译处理) - 如果用户直接粘贴文本,直接使用
- 将内容保存到
docvideo-output/<project-name>/source.md
第 2 步:内容分析与分镜生成
- 分析文档核心内容,提取关键要点
- 生成结构化的分镜脚本(storyboard),包含:
- 场景编号、场景类型(title/content/bullet/image/end)
- 每段的讲解文案(narration script)
- 建议的视觉呈现方式(标题、列表、图示等)
- 预估时长(按中文 ~4 字/秒 计算)
- 将分镜保存为
docvideo-output/<project-name>/storyboard.json
分镜 JSON 结构:
{
"title": "视频标题",
"description": "视频简介(用于社交媒体)",
"totalDuration": 120,
"scenes": [
{
"id": "scene-1",
"type": "title",
"title": "场景标题",
"narration": "讲解文案……",
"duration": 8,
"visuals": {
"type": "title",
"title": "大标题",
"subtitle": "副标题"
}
}
]
}
第 3 步:TTS 语音合成
- 使用
scripts/tts.mjs合成语音(免费方案,无需 API 凭证) - 也可以使用统一 CLI:
npm run docvideoer -- tts --storyboard <storyboard.json> --audio-dir <audio-dir> - 默认使用 Edge TTS(微软在线 TTS,音质好,自动生成字幕)
- 需要安装:
pip3 install edge-tts
- 需要安装:
- 备用 macOS say(系统自带,离线可用)
- 为每个场景分别合成语音(支持并行)
- 获取每段音频的实际时长,更新分镜时间线
- 音频保存到
docvideo-output/<project-name>/audio/
第 4 步:Remotion 视频项目搭建
- 在
docvideo-output/<project-name>/video/下创建 Remotion 项目 - 使用
templates/doc-video/作为基础模板 - 可使用:
npm run docvideoer -- setup --storyboard <storyboard.json> --audio-dir <audio-dir> --video-dir <video-dir> - 根据分镜信息生成对应的场景组件
- 实现音频和视觉的精确同步
第 5 步:渲染视频
- 使用
npm run docvideoer -- render --video-dir <video-dir> --out <output.mp4> --install或npx remotion render命令渲染最终 MP4 - 输出到
docvideo-output/<project-name>/output.mp4
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 144 lines · 1,560 tokens per session scan A e8f46cd3f443
docvideoer CLAUDE.md is an instructions file published in the GitHub repository coding-ax/docvideoer (5 stars, last pushed 3mo ago), licensed MIT. It adds 1,560 tokens to every session, about $0.0078 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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