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/wanglongxiao/seedance-drama-makernpx agentmods add skills/wanglongxiao/seedance-drama-maker/vlog-auto-editWrote 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/wanglongxiao/seedance-drama-maker/vlog-auto-edit)<a href="https://agentmods.dev/skills/wanglongxiao/seedance-drama-maker/vlog-auto-edit"><img src="https://agentmods.dev/badge/skills/wanglongxiao/seedance-drama-maker/vlog-auto-edit/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/wanglongxiao/seedance-drama-maker/vlog-auto-edit"><img src="https://agentmods.dev/badge/skills/wanglongxiao/seedance-drama-maker/vlog-auto-edit.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 | $0.00066 | $0.11575 |
| Opus 5.5 | $0.00026 | $0.04630 |
| Sonnet 5.5 | $0.00013 | $0.02315 |
| Haiku 4.5 | $0.00007 | $0.01158 |
Grade A, and why
vlog-auto-edit 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 yesterday.
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.
import base64, json, urllib.request Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(cmd, shell=True) This is a copy
100% identical to vlog-auto-edit — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 953 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent 自动剪辑旅行 Vlog
Author: nyx研究所 · GitHub · B站 @nyx研究所 · 小红书 @nyx研究所 · X @znyupup_music
把一堆手机拍的旅行素材，用AI自动剪成一个完整vlog。 最小依赖：ffmpeg + Python(whisper或FunASR+Pillow) + 视觉API。系统级只装ffmpeg，其余pip装。
触发条件
- 用户有一批旅行/日常视频素材，想自动剪成vlog
- 用户说"帮我剪个视频"、"自动剪辑"、"vlog剪辑"
前置要求
1. 系统工具
| 工具 | 用途 | 安装 |
|---|---|---|
| ffmpeg | 视频裁剪/编码/拼接/抽帧/音量检测 | brew install ffmpeg (macOS) / apt install ffmpeg (Linux) |
| Python 3.9+ | 脚本胶水 | macOS/Linux 系统自带 |
2. Python依赖（先检测，已有则跳过）
先检测系统是否已安装所需包，不要无脑创建 venv：
# 检测 whisper
python3 -c "import whisper; print('✅ whisper已安装:', whisper.__file__)"
# 检测 Pillow
python3 -c "from PIL import Image; print('✅ Pillow已安装')"
⚠ 重要：不要用 2>/dev/null 吃掉错误！要看到实际报错才能判断是真没装还是PATH问题。
⚠ macOS 注意： 检查系统已安装的包再决定是否需要 venv。如果系统 python3 已经能 import whisper 和 from PIL import Image，直接用就行，不要盲目创建 venv 导致找不到已有的包。
仅在检测不通过时才安装：
# 方案A: 直接装到用户环境（推荐）
pip install openai-whisper Pillow
# 方案B: 如果用户环境有冲突，再用 venv
python3 -m venv .venv && source .venv/bin/activate
pip install openai-whisper Pillow
⚠ 不要每个项目都新建 venv 重装一遍！whisper模型文件1.4GB，pip install也要几分钟。
⚠ ASR引擎二选一：Whisper 或 FunASR
本工作流支持两个ASR引擎，优先用 whisper，whisper 装不上就用 FunASR：
| Whisper (OpenAI) | FunASR (阿里达摩院) | |
|---|---|---|
| 安装 | pip install openai-whisper |
pip install funasr modelscope torchaudio |
| 模型 | medium (1.4GB, 从GitHub下载) | paraformer-zh (1.05GB, 从ModelScope下载) |
| 附加模型 | 无 | VAD模型 + 标点模型 (首次运行自动下载, 共~50MB) |
| 中文效果 | 好 | 好（与whisper medium相当，细节略多） |
| 速度 | 14s音频 ≈ 5-10s (CPU) | 14s音频 ≈ 1.9s (CPU, RTF=0.132) ⚡ |
| 时间戳粒度 | 句级别 (每句话一个start/end) | 字级别 (每个字一个时间戳) |
| 模型下载源 | GitHub (国内不稳定，容易失败) | ModelScope (国内极快，~15MB/s) |
| 隐性依赖 | torch | torch + torchaudio (⚠ 必须额外装) |
检测已安装的ASR引擎：
python3 -c "import whisper; print('✅ whisper可用')" 2>/dev/null \
|| python3 -c "from funasr import AutoModel; print('✅ funasr可用')" 2>/dev/null \
|| echo "❌ 没有可用的ASR引擎，需要安装一个"
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 953 lines · 66 tokens per session scan A 913d45d6b15f
vlog-auto-edit is a skill published in the GitHub repository wanglongxiao/seedance-drama-maker (21 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 11,575 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). It is 100% identical to vlog-auto-edit, differing in 0 lines, and is treated as a copy.
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