Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/chenyuxiaojin/video-agent-skillsnpx agentmods add skills/chenyuxiaojin/video-agent-skills/video-agent-jianying-editorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chenyuxiaojin/video-agent-skills/video-agent-jianying-editor)<a href="https://agentmods.dev/skills/chenyuxiaojin/video-agent-skills/video-agent-jianying-editor"><img src="https://agentmods.dev/badge/skills/chenyuxiaojin/video-agent-skills/video-agent-jianying-editor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chenyuxiaojin/video-agent-skills/video-agent-jianying-editor"><img src="https://agentmods.dev/badge/skills/chenyuxiaojin/video-agent-skills/video-agent-jianying-editor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00128 | $0.03235 |
| Opus 5 | $0.00064 | $0.01618 |
| Sonnet 5 | $0.00026 | $0.00647 |
| Haiku 4.5 | $0.00013 | $0.00324 |
Grade A, and why
video-agent-jianying-editor scanned grade A with 2 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post(f"{CAPCUT_API}/create_draft", json={ Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
video-agent-jianying-editor（剪映剪辑师）
与达芬奇剪辑师的关系
两个剪辑师共享完全相同的输入文件，区别仅在输出目标：
| 维度 | 达芬奇剪辑师 (editor) | 剪映剪辑师 (jianying-editor) |
|---|---|---|
| 输入 | visual-timeline.json + 音频 + 素材 | 相同 |
| 输出 | DaVinci Resolve 项目（API 直连） | 剪映 draft 文件夹（离线生成） |
| 依赖 | 达芬奇 Studio 必须运行 | VectCutAPI 服务必须运行 |
| 导入方式 | 自动创建到达芬奇 | 复制 draft 到剪映目录，重启剪映 |
制片人在调度时通过参数 output_target 指定：
output_target: "resolve"→ 调达芬奇剪辑师output_target: "jianying"→ 调剪映剪辑师output_target: "both"→ 两个都调，生成两份项目
职责边界
- ✅ 读取 visual-timeline.json，通过 VectCutAPI 构建剪映草稿
- ✅ 创建多轨时间轴（视频轨、音频轨、字幕轨）
- ✅ 处理"后期制作"标记的镜头（生成占位素材放入草稿）
- ✅ 为静态图片设置展示时长和基础动画
- ✅ 导入字幕（SRT 格式）
- ✅ 添加基础转场效果
- ❌ 设计画面内容（分镜师负责）
- ❌ 搜索或生成素材（美术师负责）
- ❌ 配音（配音师负责）
- ❌ 视频渲染导出（在剪映中手动完成）
输入 → 输出
输入（与达芬奇剪辑师完全相同）
visual-timeline.json（美术师产出）audio/voiceover.mp3audio/subtitles.srtvisuals/*.png（美术师下载/生成的素材）storyboard.md（参考，用于后期制作镜头的细节）
输出
jianying-draft/dfd_<项目名>/— 剪映草稿文件夹，包含 draft_content.json 和素材引用jianying-editor-report.md— 剪辑报告（包含导入指南和待手动完成的任务清单）
轨道结构
| 轨道 | 类型 | 内容 |
|---|---|---|
| 主视频轨 | 视频 | 图片/视频素材（按 visual-timeline.json 排列） |
| 叠加轨 1 | 视频 | 文字动效占位素材（后期替换） |
| 叠加轨 2 | 视频 | 数据动效/图表占位素材（后期替换） |
| 主音频轨 | 音频 | voiceover.mp3 |
| 音频轨 2 | 音频 | BGM 预留（留空） |
| 字幕轨 | 字幕 | 根据 subtitles.srt 生成 |
前置环境
1. 安装 VectCutAPI
git clone https://github.com/sun-guannan/VectCutAPI.git
cd VectCutAPI
python -m venv venv-capcut
source venv-capcut/bin/activate
pip install -r requirements.txt
cp config.json.example config.json
2. 启动 VectCutAPI 服务
cd /path/to/VectCutAPI
source venv-capcut/bin/activate
python capcut_server.py
# 服务启动后监听 http://localhost:9001
3. macOS 剪映草稿目录
剪映专业版（macOS）的草稿目录：
/Users/<用户名>/Movies/JianyingPro/User Data/Projects/com.lveditor.draft/
如果用户使用 CapCut 国际版：
/Users/<用户名>/Movies/CapCut/User Data/Projects/com.lveditor.draft/
脚本会自动检测两个路径，优先使用存在的那个。
4. 依赖
- Python 3.10+
- VectCutAPI 服务（运行中）
- FFprobe（获取音频/视频时长）
- Pillow（生成占位素材）
- requests（调用 VectCutAPI）
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.
- 12d ago First seen · 358 lines · 128 tokens per session scan A 72444aa9ad2e
video-agent-jianying-editor is a skill published in the GitHub repository chenyuxiaojin/video-agent-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 128 tokens to every session and 3,235 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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