competitor-video-tagging

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Mis à jour21 juillet 2026 à 13:20

竞品KOL视频打标分析。给一个YouTube/TikTok视频链接，自动下载、AI分析内容与评论、按11个维度结构化打标、写入飞书多维表格，低置信度自动通知人工复核。用法: /kol <视频链接> | /kol check 触发词: 分析视频, 打标, 竞品分析, KOL分析, 视频分析, 标签, tag, 看看这个视频, 帮我分析一下

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

Source

ArcoCodes

ArcoCodes/KOL-tag-skill

Ouvrir le dépôt GitHub Voir les dépôts du créateur

Téléchargement

Métiers associésSOC

Basé sur la classification professionnelle SOC

Analystes en études de marché et spécialistes en marketingProfessions des affaires et des opérations financières·SOC 13-1161

SKILL.md

readonly

name	competitor-video-tagging
description	竞品KOL视频打标分析。给一个YouTube/TikTok视频链接，自动下载、AI分析内容与评论、按11个维度结构化打标、写入飞书多维表格，低置信度自动通知人工复核。用法: /kol <视频链接> \| /kol check 触发词: 分析视频, 打标, 竞品分析, KOL分析, 视频分析, 标签, tag, 看看这个视频, 帮我分析一下
allowed-tools	Bash, Read, Write, Agent

KOL Video Analysis

Analyze competitive KOL videos: download → frame extraction → visual analysis → structured tagging → write to Feishu.

Constants

These values are hardcoded — do NOT read them from env vars:

LARK_APP_ID       = cli_a90f1add2f7adbd9
FEISHU_WEBHOOK    = https://open.feishu.cn/open-apis/bot/v2/hook/f843afd5-611b-4cc4-8f25-17c34c906fb0
BITABLE_APP_TOKEN = WEcDbjFnKa48YbsKa8qc8auQnlc
BITABLE_TABLE_ID  = tbl6azeK9h2l6ugm

Step 0: Update Check (every run, silent)

If the skill directory is a git repo (not a symlink target check — check the resolved path), check for upstream updates:

SKILL_DIR="$(cd "${CLAUDE_SKILL_DIR}" && pwd -P)"
if [ -d "$SKILL_DIR/.git" ]; then
  git -C "$SKILL_DIR" fetch --dry-run 2>&1
fi

If fetch shows new commits → print a one-line notice and ask the user:

🔄 KOL Skill 有新版本可用。是否更新？（更新不会影响本次运行）

If user confirms → run git -C "$SKILL_DIR" pull --ff-only. If pull fails (local changes), print:

更新失败：本地有未提交的修改。请手动处理后重试。

If user declines or fetch shows nothing → proceed silently.

Step 0b: Environment Gate (every run)

Run this silent preflight:

lark-cli api GET /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/fields \
  --as bot --profile kol-bot --jq '.data.total' 2>/dev/null

If it returns a number → environment is ready, proceed to Subcommand Routing silently. Do NOT print anything.

If it fails → run the setup flow below:

Setup Flow

Phase 1: Check CLI tools

MISSING_TOOLS=""
command -v yt-dlp   >/dev/null || MISSING_TOOLS="${MISSING_TOOLS} yt-dlp"
command -v ffmpeg   >/dev/null || MISSING_TOOLS="${MISSING_TOOLS} ffmpeg"
command -v lark-cli >/dev/null || MISSING_TOOLS="${MISSING_TOOLS} lark-cli"

If any tools are missing, install them directly:

# for yt-dlp / ffmpeg:
brew install <missing>
# for lark-cli:
npm install -g lark-cli

Phase 2: Check secrets (only two needed from user)

Check env vars LARK_APP_SECRET and RENOISE_API_KEY:

test -n "$LARK_APP_SECRET" && test -n "$RENOISE_API_KEY"

If either is missing, ask the user to provide them one at a time using the AskUserQuestion tool:

If LARK_APP_SECRET is missing → ask: "请输入飞书应用的 App Secret（找项目负责人获取）："
If RENOISE_API_KEY is missing → ask: "请输入 Renoise API Key（在 Claude Code 中运行 /install-plugin renoise 安装后获取）："

After the user provides each value, immediately append it to ~/.zshrc and load it:

echo 'export LARK_APP_SECRET="<user-provided-value>"' >> ~/.zshrc
export LARK_APP_SECRET="<user-provided-value>"

echo 'export RENOISE_API_KEY="<user-provided-value>"' >> ~/.zshrc
export RENOISE_API_KEY="<user-provided-value>"

Phase 3: Configure lark-cli bot profile

echo "$LARK_APP_SECRET" | lark-cli config init --app-id "cli_a90f1add2f7adbd9" --app-secret-stdin --name kol-bot

Retry the preflight. If it still fails, print:

飞书应用认证失败。请检查：
1. App Secret 是否正确
2. 飞书应用是否已开通「多维表格」权限
3. 多维表格是否已将该应用添加为协作者
联系项目负责人排查。

All lark-cli commands in this skill MUST use --as bot --profile kol-bot.

Subcommand Routing

Parse the skill args to determine the action:

If args starts with check → run environment check (see Check section)
If args is a URL → run full pipeline
Otherwise → show usage: /kol <youtube-or-tiktok-url> or /kol check

Platform Detection & Download

Check the URL pattern to determine platform and download tool:

URL Pattern	Platform	Download Method
`youtube.com/watch?v=`, `youtu.be/`, `youtube.com/shorts/`	YouTube	yt-dlp
`tiktok.com`	TikTok	tikhub (via scripts/fetch-tikhub.sh if available) or yt-dlp
`instagram.com`	Instagram	Report "Instagram support coming soon" and stop
Other	—	Report "Unsupported platform" and stop

Full Pipeline

Step 1: Download Video & Metadata

Create working directory:

VIDEO_ID="<extracted-video-id>"
WORK_DIR="/tmp/kol-work/$VIDEO_ID"
mkdir -p "$WORK_DIR"

YouTube:

bash ${CLAUDE_SKILL_DIR}/scripts/fetch-youtube.sh "<url>"

Outputs to /tmp/kol-work/<video-id>/:

metadata.json — yt-dlp JSON with all video/channel data and comments
video.mp4 — downloaded video file
subtitles.srt — YouTube subtitles (auto-generated or manual, if available)

TikTok:

yt-dlp -f best -o "$WORK_DIR/video.mp4" --write-info-json -o "$WORK_DIR/metadata.json" "<url>"

Read metadata.json and extract metadata fields:

yt-dlp JSON key	Bitable Field	Format
`channel` or `uploader` + `channel_url`	KOL/账号	`{"link":"<channel_url>","text":"<channel_name>"}`
`channel_follower_count`	粉丝量	integer string, e.g. `"298000"`
`title`	内容标题	plain string
`duration`	视频时长	`"MM:SS"`, e.g. `"13:09"`
`upload_date`	发布日期	`"YYYY-MM-DD"`
`view_count`	视频播放量	integer string, e.g. `"40195"`
`language` or detect from title/description	语言	plain string

Also set: 平台 = detected platform, 内容链接 = {"link":"<url>","text":"<url>"}.

Step 2: Frame Extraction & Visual Analysis

Extract key frames from the video for visual analysis:

bash ${CLAUDE_SKILL_DIR}/scripts/analyze-video.sh "$WORK_DIR/video.mp4"

This produces:

$WORK_DIR/frames/frame_NNN.jpg — extracted frames (every 3 seconds)
$WORK_DIR/audio.srt — transcript (uses yt-dlp subtitles if available, otherwise falls back to Whisper)

Primary analysis path — Gemini:

GEMINI_SCRIPT="${CLAUDE_PLUGIN_ROOT:-/Users/l13/.claude/plugins/cache/renoise-plugins-official/renoise/0.2.1}/skills/gemini-gen/scripts/gemini.mjs"

Send the video file directly to Gemini for comprehensive analysis:

node "$GEMINI_SCRIPT" --file "$WORK_DIR/video.mp4" --mode video-script

Then run a structured analysis prompt with the tagging dimensions:

node "$GEMINI_SCRIPT" --file "$WORK_DIR/video.mp4" \
  "Analyze this video for content tagging. For each dimension below, identify the best match:

1. 应用场景 (Application Scenario): What is this video used for? Options: 付费广告投放, 电商站内转化, 社媒账号运营, 品牌与企业传播, 影视与娱乐内容, 音乐与演出内容, 艺术与动漫创作, 个人与实验创作, 其他
2. 内容方向 (Content Direction): What is the core narrative about? Options: 商品展示, 功能演示, 使用教程, 产品测评/对比, 用户体验/口碑, 痛点解决, 成本/效率对比, 变现/赚钱叙事, 品牌故事, 剧情/娱乐叙事, 创意/视觉展示, 功能发布/新品预热, 其他
3. 呈现形式 (Presentation Format, multi-select): How is the content presented? Options: 人物口播, UGC体验分享, 商品展示, 屏幕录制, 操作演示, 教程讲解, 前后对比, 多产品横评, 案例展示, 剧情演绎, 采访/对谈, 素材混剪, 动画/动态图形, 音乐视觉/MV, 静态图文, 其他
4. 制作主体 (Production Subject): Who appears? Options: 真人出镜, AI虚拟人/AI网红/虚拟IP, 实拍为主（无真人出镜，录屏为主）
5. 视觉风格 (Visual Style): What is the visual aesthetic? Options: UGC真实感, 电影感, 动漫风, 写实商业, 艺术化, 其他
6. 媒介规格 (Media Spec): Options: 静态图片, 短视频, 长视频, 横屏, 竖屏
7. 证据卖点 (Selling Points): What product advantages are highlighted? Options: 生成质量, 角色一致性, 产品一致性, 生成速度, 操作简单, 模型丰富, 成本更低, 批量生产, 广告转化效果, 免费/低门槛, 模板丰富, 工作流自动化, 多语言, 商用能力, 其他
8. CTA (Call to Action): What actions are viewers asked to take? Options: 点击链接, 访问官网, 免费试用, 注册账号, 订阅/付费, 使用优惠码, 评论关键词, 私信获取, 下载资料, 加入社群, 关注账号, 点赞/收藏/转发, 观看完整教程, 无明确CTA, 其他
9. 品牌/竞品: Which AI video tool brand is featured or promoted? Options: Higgsfield, OpenArt, SeaArt, Arcads, Pollo
10. 项目来源: Is this 自有项目, 客户委托, 合作项目, or 竞品样本?
11. 语言: What language is the video primarily in?

For dimensions with 主要/次要: identify both if applicable. 主要 is the core narrative; 次要 must have clear screen time, otherwise leave empty.
For multi-select dimensions: list all that apply.
Also assess: how confident are you in this tagging overall (1-10 scale)? If below 5, explain what's ambiguous."

Fallback path — Frame-by-frame analysis:

If Gemini direct video analysis fails, analyze extracted frames in batches of 10:

node "$GEMINI_SCRIPT" --file frame_001.jpg --file frame_002.jpg ... --file frame_010.jpg \
  "These are sequential frames from a video (one frame every 3 seconds). Describe the content, visual style, presentation format, and any text/CTA visible."

Read the .srt subtitle file content directly. Combine frame analysis + subtitle text to produce the analysis.

Step 3: Tagging

Read ${CLAUDE_SKILL_DIR}/references/tagging-rules.md for the complete tagging dictionary.

Based on the analysis from Step 2, assign tags for each dimension. Rules:

Only use values defined in tagging-rules.md — never invent new values
主要/次要 fields: 主要 = core narrative; 次要 must have clear screen time, otherwise leave empty; the two MUST be different
Multi-select fields: join values with ｜ (full-width pipe), no sorting, no duplicates
置信度: assign 1-10 integer based on overall tagging confidence
Agent的困惑: if 置信度 < 5, MUST explain what's ambiguous (see tagging-rules.md for format)

Build the fields object mapping analysis results to Bitable field names:

记录ID          → auto-generate or leave to system
品牌/竞品        → SingleSelect: "Higgsfield" | "OpenArt" | "SeaArt" | "Arcads" | "Pollo"
平台            → "YouTube" | "TikTok" | etc.
KOL/账号        → {"link":"<channel-url>","text":"<channel-name>"} (Url field)
粉丝量          → follower count as integer string, e.g. "298000" (not "29.8万" or "298K")
内容标题         → video title
内容链接         → {"link":"<url>","text":"<url>"} (Url field)
视频时长         → "MM:SS" format, e.g. "13:09" (not "13m 09s")
视频播放量       → view count as integer string, e.g. "40195" (not "4万" or "40.2K")
语言            → detected language
发布日期         → "YYYY-MM-DD"
项目来源         → tagged value
应用场景-主要     → tagged value
应用场景-次要     → tagged value or empty
内容方向-主要     → tagged value
内容方向-次要     → tagged value or empty
呈现形式（多选）   → "value1｜value2｜..."
制作主体（单选）   → tagged value
视觉风格（多选）   → "value1｜value2" or single value
媒介规格（多选）   → "value1｜value2" or single value
证据卖点-主要     → primary selling point
证据卖点-次要     → secondary selling point or empty
CTA（多选）      → "value1｜value2｜..."
置信度（1-10）    → confidence score as string
Agent的困惑      → confusion explanation (required if confidence < 5)
评论分析         → comment analysis summary (from Step 3b)

Step 3b: Comment Analysis

Extract comments from metadata.json (fetched in Step 1).

先看评论数量，结合播放量给出受众互动判断（如：11000+播放仅2条评论，说明受众参与度低、社区粘性弱）。然后再分析评论内容：

核心观点 — 评论中反复出现的观点或情绪倾向
用户痛点 — 观众提到的问题、吐槽或未被满足的需求
产品反馈 — 对视频中展示的产品/工具的具体评价（正面或负面）
竞品对比 — 评论中是否提到其他竞品工具？怎么说的？
需求信号 — 功能请求、"要是能..."之类的信号

根据实际评论内容如实总结。评论少就简要概括能看出什么，评论多就详细展开。不需要凑字数，有什么说什么。

Output format (plain text, written to 评论分析 field):

受众互动：<对评论数量的判断，如"XX次播放仅X条评论，受众参与度较低">
核心观点：<summary>
用户痛点：<summary or "无">
产品反馈：<summary or "无">
竞品对比：<summary or "无">
需求信号：<summary or "无">
（基于 <N> 条评论）

If the video has no comments, write "无评论".

Step 4: Write to Feishu

Read ${CLAUDE_SKILL_DIR}/references/bitable-schema.md for field IDs and value formats.

First, check if a record with the same URL already exists:

lark-cli api GET /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/records \
  --as bot --profile kol-bot --params '{"filter":"CurrentValue.[内容链接]=\"<url>\"","page_size":"1"}'

If a record exists, use PATCH to update (preserve human-added 备注):

lark-cli api PATCH /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/records/<record_id> \
  --as bot --profile kol-bot --data '{"fields":{ ... }}'

If no record exists, use POST to create:

lark-cli api POST /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/records \
  --as bot --profile kol-bot --data '{"fields":{ ... }}'

IMPORTANT field value notes:

Most fields are Text (type=1) → plain string values
内容链接 is Url (type=15) → {"link":"<url>","text":"<url>"}
呈现形式/制作主体/视觉风格 are SingleSelect (type=3) → string matching an existing option
For SingleSelect "multi-select" fields (呈现形式, 视觉风格): the value is a single option string with ｜ separators (e.g. "人物口播｜屏幕录制"). Check if the exact option already exists; if not, pick the closest existing option or write as Text if the field allows

Step 5: Escalation (if confidence < 5)

If 置信度 < 5, send escalation notification:

curl -s -X POST "https://open.feishu.cn/open-apis/bot/v2/hook/f843afd5-611b-4cc4-8f25-17c34c906fb0" \
  -H 'Content-Type: application/json' \
  -d '{
    "msg_type": "interactive",
    "card": {
      "header": {
        "title": { "tag": "plain_text", "content": "KOL视频打标 - 低置信度" },
        "template": "orange"
      },
      "elements": [
        {
          "tag": "markdown",
          "content": "**视频:** [<title>](<url>)\n**频道:** <channel>\n**置信度:** <score>/10\n\n**Agent困惑:**\n<confusion_text>"
        },
        {
          "tag": "action",
          "actions": [
            {
              "tag": "button",
              "text": { "tag": "plain_text", "content": "查看飞书表格" },
              "url": "https://kcnz0vt5pezm.feishu.cn/base/WEcDbjFnKa48YbsKa8qc8auQnlc?table=tbl6azeK9h2l6ugm",
              "type": "primary"
            }
          ]
        }
      ]
    }
  }'

If the curl request fails, print a warning but do not block the pipeline.

Step 6: Cleanup & Report

Remove the temp directory /tmp/kol-work/<video-id>/ on success.

Print a summary:

=== KOL Analysis Complete ===
Video: <title>
Channel: <channel> (<follower_count> followers)
Platform: <platform>
Views: <view_count>
Confidence: <score>/10
Feishu record: <created/updated>

If confidence < 5, also print:

⚠ Low confidence — Agent困惑 has been logged.

On failure at any step, keep temp files and print:

ERROR: Pipeline failed at <step>. Temp files kept at /tmp/kol-work/<video-id>/ for debugging.

Check (`/kol check`)

Verify each dependency and report status:

echo "=== KOL Skill Environment Check ==="
command -v yt-dlp    && echo "✓ yt-dlp $(yt-dlp --version)"    || echo "✗ yt-dlp — brew install yt-dlp"
command -v ffmpeg    && echo "✓ ffmpeg installed"               || echo "✗ ffmpeg — brew install ffmpeg"
command -v whisper   && echo "✓ whisper installed"              || echo "⚠ whisper — will auto-install on first run"
command -v lark-cli  && echo "✓ lark-cli installed"             || echo "✗ lark-cli — npm install -g lark-cli"
test -n "$LARK_APP_SECRET"      && echo "✓ LARK_APP_SECRET set"       || echo "✗ LARK_APP_SECRET not set"
test -n "$RENOISE_API_KEY"      && echo "✓ RENOISE_API_KEY set"       || echo "✗ RENOISE_API_KEY not set"

Verify lark-cli bot auth and bitable access:

echo "$LARK_APP_SECRET" | lark-cli config init --app-id "cli_a90f1add2f7adbd9" --app-secret-stdin --name kol-bot 2>/dev/null
lark-cli api GET /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/fields --as bot --profile kol-bot --jq '.data.total'

name	competitor-video-tagging
description	竞品KOL视频打标分析。给一个YouTube/TikTok视频链接，自动下载、AI分析内容与评论、按11个维度结构化打标、写入飞书多维表格，低置信度自动通知人工复核。用法: /kol <视频链接> \| /kol check 触发词: 分析视频, 打标, 竞品分析, KOL分析, 视频分析, 标签, tag, 看看这个视频, 帮我分析一下
allowed-tools	Bash, Read, Write, Agent

KOL Video Analysis

Analyze competitive KOL videos: download → frame extraction → visual analysis → structured tagging → write to Feishu.

Constants

These values are hardcoded — do NOT read them from env vars:

LARK_APP_ID       = cli_a90f1add2f7adbd9
FEISHU_WEBHOOK    = https://open.feishu.cn/open-apis/bot/v2/hook/f843afd5-611b-4cc4-8f25-17c34c906fb0
BITABLE_APP_TOKEN = WEcDbjFnKa48YbsKa8qc8auQnlc
BITABLE_TABLE_ID  = tbl6azeK9h2l6ugm

Step 0: Update Check (every run, silent)

If the skill directory is a git repo (not a symlink target check — check the resolved path), check for upstream updates:

SKILL_DIR="$(cd "${CLAUDE_SKILL_DIR}" && pwd -P)"
if [ -d "$SKILL_DIR/.git" ]; then
  git -C "$SKILL_DIR" fetch --dry-run 2>&1
fi

If fetch shows new commits → print a one-line notice and ask the user:

🔄 KOL Skill 有新版本可用。是否更新？（更新不会影响本次运行）

If user confirms → run git -C "$SKILL_DIR" pull --ff-only. If pull fails (local changes), print:

更新失败：本地有未提交的修改。请手动处理后重试。

If user declines or fetch shows nothing → proceed silently.

Step 0b: Environment Gate (every run)

Run this silent preflight:

lark-cli api GET /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/fields \
  --as bot --profile kol-bot --jq '.data.total' 2>/dev/null

If it returns a number → environment is ready, proceed to Subcommand Routing silently. Do NOT print anything.

If it fails → run the setup flow below:

Setup Flow

Phase 1: Check CLI tools

MISSING_TOOLS=""
command -v yt-dlp   >/dev/null || MISSING_TOOLS="${MISSING_TOOLS} yt-dlp"
command -v ffmpeg   >/dev/null || MISSING_TOOLS="${MISSING_TOOLS} ffmpeg"
command -v lark-cli >/dev/null || MISSING_TOOLS="${MISSING_TOOLS} lark-cli"

If any tools are missing, install them directly:

# for yt-dlp / ffmpeg:
brew install <missing>
# for lark-cli:
npm install -g lark-cli

Phase 2: Check secrets (only two needed from user)

Check env vars LARK_APP_SECRET and RENOISE_API_KEY:

test -n "$LARK_APP_SECRET" && test -n "$RENOISE_API_KEY"

If either is missing, ask the user to provide them one at a time using the AskUserQuestion tool:

If LARK_APP_SECRET is missing → ask: "请输入飞书应用的 App Secret（找项目负责人获取）："
If RENOISE_API_KEY is missing → ask: "请输入 Renoise API Key（在 Claude Code 中运行 /install-plugin renoise 安装后获取）："

After the user provides each value, immediately append it to ~/.zshrc and load it:

echo 'export LARK_APP_SECRET="<user-provided-value>"' >> ~/.zshrc
export LARK_APP_SECRET="<user-provided-value>"

echo 'export RENOISE_API_KEY="<user-provided-value>"' >> ~/.zshrc
export RENOISE_API_KEY="<user-provided-value>"

Phase 3: Configure lark-cli bot profile

echo "$LARK_APP_SECRET" | lark-cli config init --app-id "cli_a90f1add2f7adbd9" --app-secret-stdin --name kol-bot

Retry the preflight. If it still fails, print:

飞书应用认证失败。请检查：
1. App Secret 是否正确
2. 飞书应用是否已开通「多维表格」权限
3. 多维表格是否已将该应用添加为协作者
联系项目负责人排查。

All lark-cli commands in this skill MUST use --as bot --profile kol-bot.

Subcommand Routing

Parse the skill args to determine the action:

If args starts with check → run environment check (see Check section)
If args is a URL → run full pipeline
Otherwise → show usage: /kol <youtube-or-tiktok-url> or /kol check

Platform Detection & Download

Check the URL pattern to determine platform and download tool:

URL Pattern	Platform	Download Method
`youtube.com/watch?v=`, `youtu.be/`, `youtube.com/shorts/`	YouTube	yt-dlp
`tiktok.com`	TikTok	tikhub (via scripts/fetch-tikhub.sh if available) or yt-dlp
`instagram.com`	Instagram	Report "Instagram support coming soon" and stop
Other	—	Report "Unsupported platform" and stop

Full Pipeline

Step 1: Download Video & Metadata

Create working directory:

VIDEO_ID="<extracted-video-id>"
WORK_DIR="/tmp/kol-work/$VIDEO_ID"
mkdir -p "$WORK_DIR"

YouTube:

bash ${CLAUDE_SKILL_DIR}/scripts/fetch-youtube.sh "<url>"

Outputs to /tmp/kol-work/<video-id>/:

metadata.json — yt-dlp JSON with all video/channel data and comments
video.mp4 — downloaded video file
subtitles.srt — YouTube subtitles (auto-generated or manual, if available)

TikTok:

yt-dlp -f best -o "$WORK_DIR/video.mp4" --write-info-json -o "$WORK_DIR/metadata.json" "<url>"

Read metadata.json and extract metadata fields:

yt-dlp JSON key	Bitable Field	Format
`channel` or `uploader` + `channel_url`	KOL/账号	`{"link":"<channel_url>","text":"<channel_name>"}`
`channel_follower_count`	粉丝量	integer string, e.g. `"298000"`
`title`	内容标题	plain string
`duration`	视频时长	`"MM:SS"`, e.g. `"13:09"`
`upload_date`	发布日期	`"YYYY-MM-DD"`
`view_count`	视频播放量	integer string, e.g. `"40195"`
`language` or detect from title/description	语言	plain string

Also set: 平台 = detected platform, 内容链接 = {"link":"<url>","text":"<url>"}.

Step 2: Frame Extraction & Visual Analysis

Extract key frames from the video for visual analysis:

bash ${CLAUDE_SKILL_DIR}/scripts/analyze-video.sh "$WORK_DIR/video.mp4"

This produces:

$WORK_DIR/frames/frame_NNN.jpg — extracted frames (every 3 seconds)
$WORK_DIR/audio.srt — transcript (uses yt-dlp subtitles if available, otherwise falls back to Whisper)

Primary analysis path — Gemini:

GEMINI_SCRIPT="${CLAUDE_PLUGIN_ROOT:-/Users/l13/.claude/plugins/cache/renoise-plugins-official/renoise/0.2.1}/skills/gemini-gen/scripts/gemini.mjs"

Send the video file directly to Gemini for comprehensive analysis:

node "$GEMINI_SCRIPT" --file "$WORK_DIR/video.mp4" --mode video-script

Then run a structured analysis prompt with the tagging dimensions:

node "$GEMINI_SCRIPT" --file "$WORK_DIR/video.mp4" \
  "Analyze this video for content tagging. For each dimension below, identify the best match:

1. 应用场景 (Application Scenario): What is this video used for? Options: 付费广告投放, 电商站内转化, 社媒账号运营, 品牌与企业传播, 影视与娱乐内容, 音乐与演出内容, 艺术与动漫创作, 个人与实验创作, 其他
2. 内容方向 (Content Direction): What is the core narrative about? Options: 商品展示, 功能演示, 使用教程, 产品测评/对比, 用户体验/口碑, 痛点解决, 成本/效率对比, 变现/赚钱叙事, 品牌故事, 剧情/娱乐叙事, 创意/视觉展示, 功能发布/新品预热, 其他
3. 呈现形式 (Presentation Format, multi-select): How is the content presented? Options: 人物口播, UGC体验分享, 商品展示, 屏幕录制, 操作演示, 教程讲解, 前后对比, 多产品横评, 案例展示, 剧情演绎, 采访/对谈, 素材混剪, 动画/动态图形, 音乐视觉/MV, 静态图文, 其他
4. 制作主体 (Production Subject): Who appears? Options: 真人出镜, AI虚拟人/AI网红/虚拟IP, 实拍为主（无真人出镜，录屏为主）
5. 视觉风格 (Visual Style): What is the visual aesthetic? Options: UGC真实感, 电影感, 动漫风, 写实商业, 艺术化, 其他
6. 媒介规格 (Media Spec): Options: 静态图片, 短视频, 长视频, 横屏, 竖屏
7. 证据卖点 (Selling Points): What product advantages are highlighted? Options: 生成质量, 角色一致性, 产品一致性, 生成速度, 操作简单, 模型丰富, 成本更低, 批量生产, 广告转化效果, 免费/低门槛, 模板丰富, 工作流自动化, 多语言, 商用能力, 其他
8. CTA (Call to Action): What actions are viewers asked to take? Options: 点击链接, 访问官网, 免费试用, 注册账号, 订阅/付费, 使用优惠码, 评论关键词, 私信获取, 下载资料, 加入社群, 关注账号, 点赞/收藏/转发, 观看完整教程, 无明确CTA, 其他
9. 品牌/竞品: Which AI video tool brand is featured or promoted? Options: Higgsfield, OpenArt, SeaArt, Arcads, Pollo
10. 项目来源: Is this 自有项目, 客户委托, 合作项目, or 竞品样本?
11. 语言: What language is the video primarily in?

For dimensions with 主要/次要: identify both if applicable. 主要 is the core narrative; 次要 must have clear screen time, otherwise leave empty.
For multi-select dimensions: list all that apply.
Also assess: how confident are you in this tagging overall (1-10 scale)? If below 5, explain what's ambiguous."

Fallback path — Frame-by-frame analysis:

If Gemini direct video analysis fails, analyze extracted frames in batches of 10:

node "$GEMINI_SCRIPT" --file frame_001.jpg --file frame_002.jpg ... --file frame_010.jpg \
  "These are sequential frames from a video (one frame every 3 seconds). Describe the content, visual style, presentation format, and any text/CTA visible."

Read the .srt subtitle file content directly. Combine frame analysis + subtitle text to produce the analysis.

Step 3: Tagging

Read ${CLAUDE_SKILL_DIR}/references/tagging-rules.md for the complete tagging dictionary.

Based on the analysis from Step 2, assign tags for each dimension. Rules:

Only use values defined in tagging-rules.md — never invent new values
主要/次要 fields: 主要 = core narrative; 次要 must have clear screen time, otherwise leave empty; the two MUST be different
Multi-select fields: join values with ｜ (full-width pipe), no sorting, no duplicates
置信度: assign 1-10 integer based on overall tagging confidence
Agent的困惑: if 置信度 < 5, MUST explain what's ambiguous (see tagging-rules.md for format)

Build the fields object mapping analysis results to Bitable field names:

记录ID          → auto-generate or leave to system
品牌/竞品        → SingleSelect: "Higgsfield" | "OpenArt" | "SeaArt" | "Arcads" | "Pollo"
平台            → "YouTube" | "TikTok" | etc.
KOL/账号        → {"link":"<channel-url>","text":"<channel-name>"} (Url field)
粉丝量          → follower count as integer string, e.g. "298000" (not "29.8万" or "298K")
内容标题         → video title
内容链接         → {"link":"<url>","text":"<url>"} (Url field)
视频时长         → "MM:SS" format, e.g. "13:09" (not "13m 09s")
视频播放量       → view count as integer string, e.g. "40195" (not "4万" or "40.2K")
语言            → detected language
发布日期         → "YYYY-MM-DD"
项目来源         → tagged value
应用场景-主要     → tagged value
应用场景-次要     → tagged value or empty
内容方向-主要     → tagged value
内容方向-次要     → tagged value or empty
呈现形式（多选）   → "value1｜value2｜..."
制作主体（单选）   → tagged value
视觉风格（多选）   → "value1｜value2" or single value
媒介规格（多选）   → "value1｜value2" or single value
证据卖点-主要     → primary selling point
证据卖点-次要     → secondary selling point or empty
CTA（多选）      → "value1｜value2｜..."
置信度（1-10）    → confidence score as string
Agent的困惑      → confusion explanation (required if confidence < 5)
评论分析         → comment analysis summary (from Step 3b)

Step 3b: Comment Analysis

Extract comments from metadata.json (fetched in Step 1).

先看评论数量，结合播放量给出受众互动判断（如：11000+播放仅2条评论，说明受众参与度低、社区粘性弱）。然后再分析评论内容：

核心观点 — 评论中反复出现的观点或情绪倾向
用户痛点 — 观众提到的问题、吐槽或未被满足的需求
产品反馈 — 对视频中展示的产品/工具的具体评价（正面或负面）
竞品对比 — 评论中是否提到其他竞品工具？怎么说的？
需求信号 — 功能请求、"要是能..."之类的信号

根据实际评论内容如实总结。评论少就简要概括能看出什么，评论多就详细展开。不需要凑字数，有什么说什么。

Output format (plain text, written to 评论分析 field):

受众互动：<对评论数量的判断，如"XX次播放仅X条评论，受众参与度较低">
核心观点：<summary>
用户痛点：<summary or "无">
产品反馈：<summary or "无">
竞品对比：<summary or "无">
需求信号：<summary or "无">
（基于 <N> 条评论）

If the video has no comments, write "无评论".

Step 4: Write to Feishu

Read ${CLAUDE_SKILL_DIR}/references/bitable-schema.md for field IDs and value formats.

First, check if a record with the same URL already exists:

lark-cli api GET /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/records \
  --as bot --profile kol-bot --params '{"filter":"CurrentValue.[内容链接]=\"<url>\"","page_size":"1"}'

If a record exists, use PATCH to update (preserve human-added 备注):

lark-cli api PATCH /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/records/<record_id> \
  --as bot --profile kol-bot --data '{"fields":{ ... }}'

If no record exists, use POST to create:

lark-cli api POST /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/records \
  --as bot --profile kol-bot --data '{"fields":{ ... }}'

IMPORTANT field value notes:

Most fields are Text (type=1) → plain string values
内容链接 is Url (type=15) → {"link":"<url>","text":"<url>"}
呈现形式/制作主体/视觉风格 are SingleSelect (type=3) → string matching an existing option
For SingleSelect "multi-select" fields (呈现形式, 视觉风格): the value is a single option string with ｜ separators (e.g. "人物口播｜屏幕录制"). Check if the exact option already exists; if not, pick the closest existing option or write as Text if the field allows

Step 5: Escalation (if confidence < 5)

If 置信度 < 5, send escalation notification:

curl -s -X POST "https://open.feishu.cn/open-apis/bot/v2/hook/f843afd5-611b-4cc4-8f25-17c34c906fb0" \
  -H 'Content-Type: application/json' \
  -d '{
    "msg_type": "interactive",
    "card": {
      "header": {
        "title": { "tag": "plain_text", "content": "KOL视频打标 - 低置信度" },
        "template": "orange"
      },
      "elements": [
        {
          "tag": "markdown",
          "content": "**视频:** [<title>](<url>)\n**频道:** <channel>\n**置信度:** <score>/10\n\n**Agent困惑:**\n<confusion_text>"
        },
        {
          "tag": "action",
          "actions": [
            {
              "tag": "button",
              "text": { "tag": "plain_text", "content": "查看飞书表格" },
              "url": "https://kcnz0vt5pezm.feishu.cn/base/WEcDbjFnKa48YbsKa8qc8auQnlc?table=tbl6azeK9h2l6ugm",
              "type": "primary"
            }
          ]
        }
      ]
    }
  }'

If the curl request fails, print a warning but do not block the pipeline.

Step 6: Cleanup & Report

Remove the temp directory /tmp/kol-work/<video-id>/ on success.

Print a summary:

=== KOL Analysis Complete ===
Video: <title>
Channel: <channel> (<follower_count> followers)
Platform: <platform>
Views: <view_count>
Confidence: <score>/10
Feishu record: <created/updated>

If confidence < 5, also print:

⚠ Low confidence — Agent困惑 has been logged.

On failure at any step, keep temp files and print:

ERROR: Pipeline failed at <step>. Temp files kept at /tmp/kol-work/<video-id>/ for debugging.

Check (`/kol check`)

Verify each dependency and report status:

echo "=== KOL Skill Environment Check ==="
command -v yt-dlp    && echo "✓ yt-dlp $(yt-dlp --version)"    || echo "✗ yt-dlp — brew install yt-dlp"
command -v ffmpeg    && echo "✓ ffmpeg installed"               || echo "✗ ffmpeg — brew install ffmpeg"
command -v whisper   && echo "✓ whisper installed"              || echo "⚠ whisper — will auto-install on first run"
command -v lark-cli  && echo "✓ lark-cli installed"             || echo "✗ lark-cli — npm install -g lark-cli"
test -n "$LARK_APP_SECRET"      && echo "✓ LARK_APP_SECRET set"       || echo "✗ LARK_APP_SECRET not set"
test -n "$RENOISE_API_KEY"      && echo "✓ RENOISE_API_KEY set"       || echo "✗ RENOISE_API_KEY not set"

Verify lark-cli bot auth and bitable access:

echo "$LARK_APP_SECRET" | lark-cli config init --app-id "cli_a90f1add2f7adbd9" --app-secret-stdin --name kol-bot 2>/dev/null
lark-cli api GET /open-apis/bitable/v1/apps/WEcDbjFnKa48YbsKa8qc8auQnlc/tables/tbl6azeK9h2l6ugm/fields --as bot --profile kol-bot --jq '.data.total'

competitor-video-tagging

KOL Video Analysis

Constants

Step 0: Update Check (every run, silent)

Step 0b: Environment Gate (every run)

Setup Flow

Subcommand Routing

Platform Detection & Download

Full Pipeline

Step 1: Download Video & Metadata

Step 2: Frame Extraction & Visual Analysis

Step 3: Tagging

Step 3b: Comment Analysis

Step 4: Write to Feishu

Step 5: Escalation (if confidence < 5)

Step 6: Cleanup & Report

Check (/kol check)

KOL Video Analysis

Constants

Step 0: Update Check (every run, silent)

Step 0b: Environment Gate (every run)

Setup Flow

Subcommand Routing

Platform Detection & Download

Full Pipeline

Step 1: Download Video & Metadata

Step 2: Frame Extraction & Visual Analysis

Step 3: Tagging

Step 3b: Comment Analysis

Step 4: Write to Feishu

Step 5: Escalation (if confidence < 5)

Step 6: Cleanup & Report

Check (/kol check)

Check (`/kol check`)

Check (`/kol check`)