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Agent Skill · 研究检索

youtube-transcript-analysis-api-skill(youtube transcript analysis API 技能)

用于辅助 API 设计、接口文档、请求响应结构和服务集成说明。它适合让 Agent 梳理 endpoint、生成 OpenAPI 草稿、检查字段命名、整理错误码或辅助前后端联调。使用时需要确认真实业务语义、鉴权方式、分页和错误处理规则;涉及生成接口文档时,应避免凭空补字段,最好从现有代码、schema 或接口样例中提取事实。

youtube-transcript-analysis-api-skill 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 24,917。

总安装 3,888周安装 162GitHub Stars 公开资料未说明下载 1,296核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:youtube-transcript-analysis-api-skill(youtube transcript analysis API 技能)
来源仓库:https://github.com/browseract-ai/youtube-transcript-analysis-api-skill
安装命令:
openclaw skills install youtube-transcript-analysis-api-skill
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install youtube-transcript-analysis-api-skill

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

提取 YouTube 视频文字记录并进行深度竞争分析,辅助市场洞察决策。适用于竞品研究、关键词挖掘或用户评论情感分析等商业情报任务。代理主动调用此技能时,需提供视频 URL 与分析维度(如观点聚类、情绪倾向)。

安装命令:openclaw skills install youtube-transcript-analysis-api-skill;依赖 BrowserAct API 稳定性。分析结果为参考性结论,实际业务影响需结合多源数据交叉验证。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install youtube-transcript-analysis-api-skill;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

youtube-transcript-analysis-api-skill 怎么安装?

在终端执行 openclaw skills install youtube-transcript-analysis-api-skill。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

youtube-transcript-analysis-api-skill 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

youtube-transcript-analysis-api-skill 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

youtube-transcript-analysis-api-skill 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

youtube-transcript-analysis-api-skill 用的人多吗?

公开数据是累计安装 3,888 次,近一周安装 162 次,下载 1,296 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

youtube-transcript-analysis-api-skill 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
youtube-transcript-analysis-api-skill

youtube transcript analysis API 技能 · 本页

3,888—接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
youtube-transcript-analysis-api-skill
description
This skill helps users extract YouTube video transcripts and perform deep competitive analysis on the content. Agent should proactively apply this skill when users express needs like analyze YouTube video content strategy, perform competitive video content analysis, extract and analyze YouTube subtitles for marketing insights, understand competitor value propositions from their videos, identify target audience from YouTube video content, analyze pain points and needs mentioned in YouTube videos, evaluate competitor CTA strategies in video content, find content gaps in competitor YouTube videos, analyze video narrative structure and hooks, extract key messaging and positioning from YouTube content, benchmark competitor video content quality, research competitor marketing angles through video analysis, identify audience signals and terminology level in videos, analyze emotional tone and persuasion techniques in YouTube content.
metadata
{"openclaw":{"emoji":"🌐","requires":{"bins":["python"],"env":["BROWSERACT_API_KEY"]}}}

YouTube Transcript Analysis API Skill

📖 Brief

This skill provides an end-to-end YouTube video transcript extraction and deep content analysis service. By extracting video transcripts and then systematically analyzing them, users can understand competitors' core value propositions, target audience profiles, pain point strategies, and content gaps — all without manually watching hours of video.

This skill works in two phases:

  1. Phase 1 — Transcript Extraction: Uses BrowserAct API to extract raw transcript data (supports single video and batch modes).
  2. Phase 2 — Deep Analysis: The Agent performs structured 8-dimension analysis on the extracted transcripts.

✨ Features

  1. No hallucinations, ensuring stable and accurate data extraction: Pre-set workflows avoid AI generative hallucinations, ensuring stable and precise data extraction.
  2. No CAPTCHA issues: No need to handle reCAPTCHA or other verification challenges.
  3. No IP restrictions or geo-blocking: No need to handle regional IP restrictions or geofencing.
  4. Faster execution: Tasks execute faster compared to pure AI-driven browser automation solutions.
  5. Extremely high cost-efficiency: Significantly lowers data acquisition costs compared to high-token-consuming AI solutions.

🔑 API Key Guide

Before running, check the BROWSERACT_API_KEY environment variable. If not set, do not take other measures; ask and wait for the user to provide it. Agent must inform the user:

"Since you haven't configured the BrowserAct API Key yet, please go to the BrowserAct Console to get your Key."

🛠️ Input Parameters

The Agent should determine the extraction mode based on the user's needs:

Mode A: Single Video Analysis

Use when the user provides a specific YouTube video URL.

  1. TargetURL

- Type: string - Description: The URL of the YouTube video to extract and analyze. - Example: https://www.youtube.com/watch?v=st534T7-mdE - Required: Yes

Mode B: Batch Video Analysis

Use when the user wants to search and analyze multiple videos by keyword.

  1. KeyWords

- Type: string - Description: The keyword to search for on YouTube. - Example: AI Automation, SaaS Marketing - Required: Yes

  1. Upload_date

- Type: string - Description: Filter for the upload date of the videos. - Example: This week - Default: This week

  1. Datelimit

- Type: number - Description: The number of videos to extract and analyze. - Example: 3 - Default: 3

Optional Analysis Parameters

These parameters are set by the user's intent, not script arguments:

  1. Analysis Language

- Type: string - Description: The language the analysis report should be written in. Defaults to the same language as the user's request. - Example: Chinese, English

  1. Analysis Focus

- Type: string - Description: The user may specify an analysis focus. The Agent must dynamically adjust the depth of specific dimensions based on this focus. For example: - *Competitor Analysis* -> Deep dive into Dim 7 (Business Model) and Dim 8 (Gaps). - *Viral Deconstruction* -> Deep dive into Dim 1 (Hook), Dim 4 (Emotional Arc), and Dim 5 (Viral Drivers). - *Audience Research* -> Deep dive into Dim 3 (Persona & Intent) and Dim 4 (Pain Points). - Default: All 8 dimensions balanced. - Example: Competitor Analysis, Viral Deconstruction, Audience Research

🚀 Invocation Method

The Agent should execute the unified extraction script based on the mode:

Mode A — Single Video:

python -u ./scripts/youtube_transcript_analysis_api.py single "TargetURL"

Mode B — Batch Videos:

python -u ./scripts/youtube_transcript_analysis_api.py batch "keywords" "Upload_date" Datelimit

⏳ Running Status Monitoring

Since this task involves automated browser operations, it may take several minutes. The script will continuously output status logs with timestamps (e.g., [14:30:05] Task Status: running). Agent guidelines:

  • While waiting for the script to return results, keep monitoring the terminal output.
  • As long as the terminal continues to output new status logs, the task is running normally. Do not misjudge it as deadlocked.
  • Only consider triggering the retry mechanism if the status remains unchanged for a long time or the script stops outputting without returning a result.

Post-Extraction Workflow

After the script completes and returns transcript data, the Agent must proceed with two additional steps:

Step 1: Present Video Metadata — Display the extracted metadata to the user. *(Note: Do NOT output the full raw transcript text in your response, as it is too long. Use it internally for your analysis.)*

Step 2: Perform Concise 8-Dimension Analysis — Analyze the transcript across the 8 dimensions. ⚠️ CRITICAL: The analysis MUST be extremely concise, bullet-point driven, and free of filler words. Directly state the facts, evidence, and actionable insights without verbose explanations. Use the same language as the user's request.

📊 Data Output

After successful execution, the output includes two parts:

Part 1: Video Metadata

The script returns the following fields for each video:

  • video_title: The title of the YouTube video
  • video_url: The direct link to the original video
  • publisher: The name of the channel publishing the video
  • channel_link: The URL of the publisher's YouTube channel
  • video_likes_count: The number of likes the video has received
  • transcript: The complete extracted transcript/subtitles of the video (used internally for analysis, do not display full text)

Part 2: 8-Dimension Analysis

After presenting raw data, the Agent must produce structured analysis on the transcript content across the following 8 dimensions:

Dimension 1: Content Structure & Hook

Analyze the video's narrative architecture:

  • Opening Hook: What is the core hook in the first 30 seconds? Quote it and explain the hook logic (e.g., curiosity gap, bold claim).
  • Narrative Framework: Identify the overall structure (e.g., Problem-Agitate-Solve, Hero's Journey, Listicle).
  • Pacing & Time Allocation: Proportion of intro vs. core content vs. pitch/CTA.
Dimension 2: Core Messaging

Extract the central message:

  • Single Core Viewpoint: What is the ONE key thesis the video conveys?
  • Supporting Arguments: How is the viewpoint supported? (Data, analogies, personal experience).
  • Conclusion Clarity: Is the conclusion clear and memorable?
Dimension 3: Audience Persona & Intent

Identify the intended viewer and their mindset:

  • Target Viewer Profile & Level: Who is this for? (Beginner, Expert) What prior knowledge is assumed?
  • Viewer Intent: Why are they watching? (To learn a skill, be entertained, make a buying decision, or validate existing beliefs?)
Dimension 4: Pain Points & Emotional Arc

Map the emotional journey and problems addressed:

  • Explicit & Implicit Pain Points: What specific problems are stated or implied? Quote exact words.
  • Emotional Arc: How does the content shift the viewer's emotion? (e.g., from anxiety/confusion to clarity/relief/empowerment). This emotional shift drives retention and sharing.
Dimension 5: Viral & Engagement Drivers

Analyze the spreading mechanism:

  • Shareability Factors: Why is this video shared? (Controversial takes, highly relatable scenarios, title/thumbnail alignment inferred from script).
  • Memorable/Quotable Phrasing: Extract unique expressions, catchy concepts, or "aha" moments that stick in the mind.
Dimension 6: Evidence & Credibility

Evaluate trust-building elements:

  • Authority Signals: Data cited, expert references, or professional background mentioned.
  • Social Proof & Empathy: Real user stories, case studies, or the creator sharing their own past struggles to build rapport.
Dimension 7: Business Model & Conversion

Deconstruct the monetization and CTA strategy:

  • Primary Monetization Goal: What is the underlying business purpose? (Ad revenue, selling a course, affiliate marketing, brand sponsorship, lead generation).
  • CTA Strategy: What actions are requested? How is urgency or value constructed to drive this action?
Dimension 8: Categorized Content Gaps

Identify strategic opportunities by splitting gaps into three layers:

  • Creator's Weaknesses: Arguments that lack evidence, logical flaws, or poorly explained concepts.
  • Unresolved Viewer Questions: What specific questions would the audience still have after watching?
  • Industry Whitespace: What related angles or broader perspectives did the video entirely miss that you could cover?

Output Format

For Single Video Analysis:

## Video Metadata
[Present video metadata. DO NOT print full transcript]

## Concise Deep Analysis
*(Output in extremely brief bullet points, max 1-2 short sentences per point)*

### 1. Content Structure & Hook
[Concise bullets]

### 2. Core Messaging
[Concise bullets]

### 3. Audience Persona & Intent
[Concise bullets]

### 4. Pain Points & Emotional Arc
[Concise bullets]

### 5. Viral & Engagement Drivers
[Concise bullets]

### 6. Evidence & Credibility
[Concise bullets]

### 7. Business Model & Conversion
[Concise bullets]

### 8. Categorized Content Gaps
[Concise bullets]

### Key Takeaways
[3 short, actionable strategic insights]

For Batch Video Analysis:

## Video Metadata
[Present all video metadata. DO NOT print full transcripts]

## Concise Individual Analysis
[Repeat the concise 8-dimension analysis for EACH video using brief bullet points]

## Cross-Video Comparative Analysis
[After analyzing all videos individually, provide a comparative summary]:
- Common value propositions: What themes appear across multiple videos?
- Shared target audience: Is there a consistent audience profile?
- Recurring pain points: Which problems are mentioned most frequently?
- Dominant content strategies: What narrative structures and CTA patterns are most common?
- Competitive differentiation: How do different creators/brands position themselves differently?
- Industry content gaps: What topics are consistently missing across all analyzed videos?

⚠️ Error Handling & Retry

If an error occurs during script execution (e.g., network fluctuations or task failure), the Agent should follow this logic:

  1. Check Output Content:

- If the output contains "Invalid authorization", it means the API Key is invalid or expired. Do not retry; guide the user to re-check and provide the correct API Key. - If the output contains "concurrent" or "too many running tasks" or similar concurrency limit messages, it means the current subscription plan's concurrent task limit has been reached. Do not retry; guide the user to upgrade their plan. Agent must inform the user: > "The current task cannot be executed because your BrowserAct account has reached the limit of concurrent tasks. Please go to the BrowserAct Plan Upgrade Page to upgrade your subscription plan and enjoy more concurrent task benefits." - If the output does not contain the above error keywords but the task failed (e.g., output starts with Error: or returns empty results), the Agent should automatically try to re-execute the script once.

  1. Retry Limit:

- Automatic retry is limited to one time. If the second attempt fails, stop retrying and report the specific error information to the user.

  1. Analysis Phase Notes:

- If the transcript is too short (fewer than 50 words), note this and provide analysis only on available content. - If the transcript appears to be auto-generated and contains many errors, note this caveat at the beginning of the analysis.

🌟 Typical Use Cases

  1. Competitive content strategy analysis: Analyze competitors' top-performing videos to understand their messaging and positioning.
  2. Target audience research: Identify who competitors are targeting and how they speak to them.
  3. Pain point discovery: Extract customer pain points mentioned in competitor videos for product development insights.
  4. Content gap identification: Find topics competitors haven't covered well to create differentiated content.
  5. CTA strategy benchmarking: Understand how competitors drive conversions through their video content.
  6. Value proposition mapping: Map out what value propositions competitors emphasize most.
  7. Messaging framework extraction: Learn from competitors' narrative structures and persuasion techniques.
  8. Market trend analysis: Batch analyze recent videos in a niche to identify emerging themes and shifts.
  9. Content quality benchmarking: Evaluate the depth and credibility of competitor content.
  10. Marketing copy inspiration: Extract memorable phrases and emotional hooks for your own content creation.

平台分布

OpenClaw

92.13%
按下载量换算1,194

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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