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 skills add yehyakin/hermes-skills --skill ecommerce-video-highlightsgit clone --depth 1 https://github.com/yehyakin/hermes-skillsWrote 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-highlights)<a href="https://agentmods.dev/skills/yehyakin/hermes-skills/ecommerce-video-highlights"><img src="https://agentmods.dev/badge/skills/yehyakin/hermes-skills/ecommerce-video-highlights/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-highlights"><img src="https://agentmods.dev/badge/skills/yehyakin/hermes-skills/ecommerce-video-highlights.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 | $0.00059 | $0.01495 |
| Opus 5 | $0.00030 | $0.00747 |
| Sonnet 5 | $0.00012 | $0.00299 |
| Haiku 4.5 | $0.00006 | $0.00150 |
Grade A, and why
ecommerce-video-highlights 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.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
电商视频精彩片段 AI 提取
从长视频（直播回放/种草视频）中识别适合挂车的高价值片段。
Pipeline 三阶段
阶段 1：提取帧图片
# 每分钟1帧，分辨率降到960px方便分析
ffmpeg -i "input.mp4" \
-vf "fps=1/60,scale=540:960" \
-q:v 5 "frame_%06d.jpg"
- 采样频率：每60秒1帧是平衡点（太长会漏片段，太短分析量太大）
- 分辨率：保留原始比例，降为960px宽足够 AI 分析
阶段 2：音频转写（Whisper）
import whisper
model = whisper.load_model('base')
result = model.transcribe('audio.wav', language='zh', task='translate')
# 保存到 transcript.json
- 语言：中文视频用
language='zh', task='translate' - 后台运行：3小时音频需要10+分钟，用
background=true+notify_on_complete=true - 输出：
transcript.json含text（全文）和segments（带时间戳的句子）
阶段 3：AI 视觉分析
- 选每10分钟1帧（约22帧）进行视觉分析
- 用
vision_analyze工具，prompt 要简洁：描述视频画面：场景、人物穿着、产品展示、文字内容、时间戳约XX:XX - 商业价值判断维度：
- 是否有产品特写（服装/护肤品等）
- 主播讲解姿势 vs 纯展示
- 是否有价格/优惠文字
- 画面是否清晰、主体是否突出
输出格式
推荐片段表：
| 时间 | 场景类型 | 商业价值 | 理由 |
|---|---|---|---|
| 01:41 | 产品特写 | ⭐⭐⭐⭐⭐ | 手持服装展示，可看清材质 |
| 03:22 | 搭配讲解 | ⭐⭐⭐⭐ | 全身穿搭演示 |
步骤 4：完成后自动存入 neirong-fuoli 素材库 ⭐
必须执行，不得跳过。
分析完成后，将以下内容写入：
~/content-fuoli/
├── 03-素材库/爆款话术库/
│ └── YYYYMMDD-{竞品名}-{片段名}.md
├── 06-竞品监控/竞品分析/
│ └── YYYYMMDD-{竞品名}-高光分析.md
└── 04-数据复盘/content_data_table.md（追加记录）
分析报告文件模板：
# {竞品名} 视频高光分析
**原始视频**：{标题/链接}
**视频时长**：{X}分钟
**分析时间**：{日期}
**推荐片段数**：{N}个
---
## 片段清单
| 时间 | 场景类型 | 商业价值 | 理由 |
|------|----------|----------|------|
| ... | ... | ... | ... |
---
## 话术亮点摘录
{从Transcript中提取的关键话术，3-5条}
---
## 标签
#竞品 #高光分析 #{竞品名} #电商 #服装
飞书通知用户：
🎯 竞品视频分析完成
**竞品**：{竞品名}
**推荐片段**：{N}个
**高价值片段**：{X}个（⭐⭐⭐⭐以上）
💾 已存入 content-fuoli 竞品监控目录
📋 发布清单：{路径}
工具依赖
| 工具 | 安装方式 |
|---|---|
| ffmpeg | brew install ffmpeg |
| whisper | pip3 install openai-whisper |
踩坑记录（2026-04-25 实测）
竞品视频实测（218分钟，5.7GB）
- 帧提取：219帧，4分24秒（符合预期）
- Whisper CPU：3小时38分音频，跑了30分钟仍未完成 → CPU模式极慢，建议GPU或接受更长等待
- AI画面分析：每10分钟取1帧共22帧，约30分钟分析完毕
- 实测结论：男装竖屏穿搭视频，手持产品 + 讲解场景商业价值最高
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 · 154 lines · 59 tokens per session scan A 9e6b276941e5
ecommerce-video-highlights is a skill published in the GitHub repository yehyakin/hermes-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 59 tokens to every session and 1,495 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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