ecommerce-visual-clip-scanning

ecommerce-visual-clip-scanning is a skill for Claude Code, Codex from yehyakin/hermes-skills. It costs 62 tokens per session (1,581 once invoked), scanned A, original, MIT.

A method for finding product-selling moments by taking still images from a livestream or competitor video and having visual AI inspect them. It is useful when subtitles are missing or speech does not match the picture.

In plain words
What is it for?
Scanning long videos for product demonstrations, checking whether a person is holding or wearing a product, and precisely verifying promising time ranges.
Why use it?
It can detect what is actually shown on screen instead of relying only on what the speaker says.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Scanning long videos for product demonstrations, checking whether a person is holding or wearing a product, and precisely verifying promising time ranges.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yehyakin/hermes-skills/ecommerce-visual-clip-scanning
Install

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.

Any agent
npx skills add yehyakin/hermes-skills --skill ecommerce-visual-clip-scanning
Clone the repo
git clone --depth 1 https://github.com/yehyakin/hermes-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ecommerce-visual-clip-scanning

README.md
[![agentmods](https://agentmods.dev/badge/skills/yehyakin/hermes-skills/ecommerce-visual-clip-scanning/github.svg)](https://agentmods.dev/skills/yehyakin/hermes-skills/ecommerce-visual-clip-scanning)
Your own site
<a href="https://agentmods.dev/skills/yehyakin/hermes-skills/ecommerce-visual-clip-scanning"><img src="https://agentmods.dev/badge/skills/yehyakin/hermes-skills/ecommerce-visual-clip-scanning/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.

agentmods 80×15 button for ecommerce-visual-clip-scanning

Your own site · 80×15
<a href="https://agentmods.dev/skills/yehyakin/hermes-skills/ecommerce-visual-clip-scanning"><img src="https://agentmods.dev/badge/skills/yehyakin/hermes-skills/ecommerce-visual-clip-scanning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,581 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00062 $0.01581
Opus 5 $0.00031 $0.00790
Sonnet 5 $0.00012 $0.00316
Haiku 4.5 $0.00006 $0.00158

Measured 11d ago against content hash 402059a319ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ecommerce-visual-clip-scanning 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 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (examples/visual_scan.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

ecommerce-visual-clip-scanning/SKILL.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

电商视频视觉切片扫描法

通过画面抽帧 + 视觉AI分析识别直播/竞品视频中的带货高光片段。替代 Whisper 转写方法,更可靠(主播说到产品时镜头可能没对着产品)。

适用场景

  • 直播回放:主播说到"这件外套"但镜头可能在拍书架/空镜
  • 没有字幕的竞品视频
  • 验证 transcript/关键词方法找到的候选片段是否真有产品展示

完整流程

Step 1: 确认视频路径和规格

# 找视频文件
find /Users/yehya -type f -name "*.mp4" -size +100M 2>/dev/null | grep -v Library

# 确认时长、分辨率、编码
ffprobe -v quiet -print_format json -show_format -show_streams "VIDEO.mp4" 2>&1 | grep -E '"duration"|"width"|"height"|"codec_name"'

Step 2: 全局采样抽帧(每5分钟1帧)

INPUT="/path/to/video.mp4"
OUTPUT_DIR="/tmp/scan"
DURATION=$(ffprobe -v quiet -show_entries format=duration -of csv=p=0 "$INPUT")

mkdir -p "$OUTPUT_DIR"
for i in $(seq 0 300 $DURATION); do
  ffmpeg -ss $i -i "$INPUT" -vframes 1 -q:v 2 "$OUTPUT_DIR/frame_$(printf "%04d" $i).jpg" -y 2>/dev/null
  echo -n "."
done
echo ""
echo "完成: $(ls $OUTPUT_DIR/frame_*.jpg | wc -l) 帧"

经验:5分钟间隔是平衡精度和工作量的最优值。3.6小时视频 → 44帧。

Step 3: 批量视觉AI分析

每批5张并行分析(避免单批过多延迟):

每批次问题模板:
"主播在展示产品吗?画面里有没有拿着衣服/鞋子/配饰?简述画面内容。"

判断标准:
✅ 有产品:主播手持/举起服装/鞋/包
⚠️  不确定:主播在穿/戴产品(可接受)
❌  无产品:主播只说话/打电话/看手机/空镜

Step 4: 定位时间 → 二次精确验证

初筛发现的时间点,在前后±30秒做精确验证:

# 精细验证帧
for ts in 2900 2930 2960 2990; do
  ffmpeg -ss $ts -i "$INPUT" -vframes 1 -q:v 2 "$OUTPUT_DIR/verify_$(printf '%06d' $ts).jpg" -y 2>/dev/null
done

教训:5分钟采样会漏掉短于5分钟的展示段(本案例:毛衣展示在54分钟,5分钟采样跳过了)。 解决方案:在发现产品类型后,对该时段做密集扫描(每30秒1帧)。

Step 5: 切片段

INPUT="/path/to/video.mp4"
OUTPUT="/path/to/clips"

# 时间点(秒)→ 时:分:秒
ffmpeg -ss 3240 -i "$INPUT" -t 25 "$OUTPUT/精彩片段.mp4" -y 2>/dev/null
# 3240秒 = 54分钟

Step 6: 验证片段画面质量

必须步骤:从切好的片段中抽帧再次确认,不能假设切的是对的。

# 从片段第5秒抽1帧验证
ffmpeg -ss 5 -i "$OUTPUT/clip.mp4" -vframes 1 "$OUTPUT/vc.jpg" -y 2>/dev/null

# vision_analyze 用 file:// 前缀
vision_analyze(image_url="file:///path/to/vc.jpg", question="主播有没有在展示产品?手持衣服/皮鞋吗?")

Step 7: 交付或后续处理

  • 直接交付 MP4 片段(无字幕)
  • 或用 mcp-video 工具链烧字幕、加字幕
  • 片段规格:20-25秒,H264,竖屏 1088x1920

关键教训

❌ 不要只信字幕/Transcript

教训来源(2026/04/25): 用 transcript 文字选了5个片段,字面上都是"带货话术",但验画面后发现:

  • 片段5:主播站窗前说话,背景书架,看不到产品
  • 片段7:主播坐椅子上说话,全程没拿产品
  • 片段10:主播根本没入镜,3帧全是空镜

Read the full file on GitHub · 141 lines

Files

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.

Changes

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.

  1. 11d ago First seen · 141 lines · 62 tokens per session scan A 402059a319ed

Subscribe to this mod's changes

ecommerce-visual-clip-scanning 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 1,581 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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