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/yehyakin/hermes-skillsnpx agentmods add skills/yehyakin/hermes-skills/ecommerce-video-clip-workflowWrote 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/yehyakin/hermes-skills/ecommerce-video-clip-workflow)<a href="https://agentmods.dev/skills/yehyakin/hermes-skills/ecommerce-video-clip-workflow"><img src="https://agentmods.dev/badge/skills/yehyakin/hermes-skills/ecommerce-video-clip-workflow/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/yehyakin/hermes-skills/ecommerce-video-clip-workflow"><img src="https://agentmods.dev/badge/skills/yehyakin/hermes-skills/ecommerce-video-clip-workflow.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 | $0.00062 | $0.00996 |
| Opus 5 | $0.00031 | $0.00498 |
| Sonnet 5 | $0.00012 | $0.00199 |
| Haiku 4.5 | $0.00006 | $0.00100 |
Grade A, and why
ecommerce-video-clip-workflow 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 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.
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.
What it actually says
电商带货视频切片工作流
从长视频（直播/竞品视频）中自动识别高价值带货片段，精剪为挂车切片。
适用场景
- 竞品视频话术分析 + 切片提取
- 直播回放高光时刻挖掘
- 视频挂车素材批量制作
前置要求
- whisper.cpp 已编译 Metal 支持（Mac Apple Silicon）
- ffmpeg-full（支持 libass subtitles 滤镜）
- 模型：ggml-small.bin（中文识别准确 + 速度快）
Pipeline
Step 1: 提取音频
ffmpeg -i INPUT.mp4 -vn -c:a pcm_s16le audio.wav
Step 2: Whisper 转写（Metal GPU，8x 实时）
cd ~/whisper.cpp
./build/bin/whisper-cli \
-m models/ggml-small.bin \
-f audio.wav \
-l zh \
--output-txt \
--output-file transcript
速度：small 模型 Metal 加速 ~8x 实时（base 模型仅 0.8x 且中文差）
Step 3: 定位带货节点
# 找"一号链接"、"必买"、"鲜货"、"限量"等关键词
grep -n "一号链接\|必买\|鲜货\|限量\|划算" transcript.txt
# 计算行号 → 视频时间（每行 ≈ 视频秒数 / 总行数）
python3 -c "
video_sec = 13112 # 视频总秒数
lines = 8480 # transcript 总行数
per_line = video_sec / lines # ≈ 1.54秒/行
"
Step 4: 精剪片段（ffmpeg）
ffmpeg -y -i INPUT.mp4 \
-ss 1630 -t 25 \
-c:v libx264 -crf 20 -preset fast \
-c:a aac -b:a 128k \
clip.mp4
Step 5: 生成 SRT 字幕文件
# 根据行号范围 + 每行秒数生成 SRT
def lines_to_srt(transcript_path, start_line, end_line, per_line_sec=1.54):
# 生成带时间戳的 SRT 文件
Step 6: 烧字幕（ffmpeg-full + libass）
/opt/homebrew/opt/ffmpeg-full/bin/ffmpeg -y \
-ss START -t DURATION \
-i INPUT.mp4 \
-vf "subtitles=SRT_PATH:force_style='FontSize=28,Bold=1,OutlineColour=&H00000000,Outline=2'" \
-c:v libx264 -crf 20 -preset fast \
-c:a aac -b:a 128k \
OUTPUT.mp4
关键：必须用 ffmpeg-full（Homebrew），标准 ffmpeg 缺 libass 滤镜。
关键经验
| 问题 | 原因 | 解决方案 |
|---|---|---|
| Whisper CPU 太慢（3.6小时要跑7小时） | base 模型 + CPU | 改 whisper.cpp small 模型 + Metal |
| base 模型中文识别差 | 模型太小 | 换 ggml-small.bin |
| ffmpeg subtitles 滤镜报错 | 标准 ffmpeg 不含 libass | brew install ffmpeg-full |
| SRT 时间戳和片段对不上 | transcript 是纯文本无时间戳 | 用 总秒数/总行数 估算每行时长 |
| 中文路径导致 ffmpeg 报错 | 路径含中文字符 | 复制到 ~/tmp/ 简单路径 |
输出规格
- 分辨率：1088x1920（竖屏抖音/视频号）
- 时长：20-30秒（挂车最佳）
- 编码：H264 + AAC
- 字幕：内嵌 SRT burn-in
What ships with it
1 file 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.
- 12d ago First seen · 97 lines · 62 tokens per session scan A 331cfefbeb6e
ecommerce-video-clip-workflow is a skill published in the GitHub repository yehyakin/hermes-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 62 tokens to every session and 996 once invoked, about $0.0003 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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