youtube-transcript-analysis-api-skill
This skill extracts YouTube video transcripts using the BrowserAct API and performs structured eight-dimension analysis to identify competitor value propositions, target audiences, pain points, content gaps, CTAs, messaging strategies, and persuasion techniques. Use it when analyzing competitor video marketing strategies, researching audience positioning, benchmarking content quality, or identifying marketing angles without manually reviewing hours of video content.
git clone --depth 1 https://github.com/browser-act/skills /tmp/youtube-transcript-analysis-api-skill && cp -r /tmp/youtube-transcript-analysis-api-skill/solutions/video-platforms/youtube-transcript-analysis-api-skill ~/.claude/skills/youtube-transcript-analysis-api-skillSKILL.md
# 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.
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 purely AI-driven browser automation solutions.
5. **Extremely high cost-efficiency**: Significantly reduces data acquisition costs compared to AI solutions that consume massive amounts of tokens.
## 🔑 API Key Guide
Before running, you must check the `BROWSERACT_API_KEY` environment variable. If it is not set, do not take other actions first; you should 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](https://www.browseract.com/reception/integrations) 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
2. **Upload_date**
- **Type**: `string`
- **Description**: Filter for the upload date of the videos.
- **Example**: `This week`
- **Default**: `This week`
3. **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:
4. **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`
5. **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:**
```bash
python -u ./scripts/youtube_transcript_analysis_api.py single "TargetURL"
```
**Mode B — Batch Videos:**
```bash
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 a long time (several minutes). The script will **continuously output status logs with timestamps** while running (e.g., `[14:30:05] Task Status: running`).
**Agent guidelines**:
- While waiting for the script to return results, please keep an eye on the terminal output.
- As long as the terminal continues to output new status logs, it means the task is running normally. Do not misjudge it as a deadlock or unresponsiveness.
- If the status remains unchanged for a long time or the script stops outputting without returning a result, only then consider triggering the retry mechanism.
### 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 mExtracts comprehensive wholesale product data from 1688.com product detail pages: title, tiered pricing, SKU variants with dimensions/weight, product images, seller info, shop scores, buyer protection, cross-border flags, product attributes, coupon/promotion data, and review stats. Use when user mentions 1688, 1688.com, wholesale China, alibaba wholesale, B2B China sourcing, Chinese wholesale scraper, 1688 product scrape, 1688 offer, 1688 detail, extract 1688 data, pull 1688 listings, get wholesale price, 1688 supplier info, factory stats 1688, 1688 SKU variants, 1688 product attributes, 1688 shop score, DSR score 1688, 1688 buyer protection, 1688 cross-border, 1688 dropship. Also applies to: scraping bulk product data from 1688 by offer ID list, monitoring 1688 supplier metrics, extracting 1688 pricing tiers for resale analysis.
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Amazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.
This skill helps users extract structured product details from Amazon using a specific ASIN (Amazon Standard Identification Number). Use this skill when the user asks to get Amazon product details by ASIN, lookup Amazon product title and price using ASIN, extract Amazon product ratings and reviews count for a specific ASIN, check Amazon product availability and current price, get Amazon product description and features via ASIN, enrich product catalog with Amazon data using ASIN, monitor Amazon product price changes for specific ASINs, retrieve Amazon product brand and material information, fetch Amazon product images and specifications by ASIN, validate Amazon ASIN and get product metadata.
This skill helps users extract structured best-selling product data from Amazon via the BrowserAct API. Agent should proactively apply this skill when users express needs like search for best selling products on Amazon, extract Amazon product data based on keywords, find top rated Amazon products, monitor Amazon competitor prices and sales, discover trending products on Amazon marketplace, extract Amazon product titles prices and ratings, gather Amazon product sales volume for market research, search Amazon best sellers in specific region, collect Amazon product reviews and promotion details, analyze Amazon product availability and badges, get Amazon product data for market analysis.
Amazon Best Sellers listing scraper: extract product cards from any Amazon Best Sellers (zgbs) or /gp/bestsellers/ category page — returns rank (position on chart), asin, title, url, image, imageAlt, price, stars, reviewCount, ratingRaw per item, plus category metadata (categoryName, categoryFullName, categoryUrl) and pagination state (currentPage, hasNextPage, nextPageUrl). Works across all Amazon regional TLDs (amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, etc.). Use when user mentions Amazon Best Sellers, Amazon bestsellers, Amazon top 100, Amazon zgbs, Amazon /zgbs/, Amazon /gp/bestsellers/, Amazon Best Sellers Rank, Amazon BSR, Amazon top ranked products, Amazon top-selling products, Amazon chart, Amazon category ranking, Amazon best sellers by category, Amazon best sellers electronics, Amazon best sellers kitchen, Amazon best sellers toys, scrape Amazon bestsellers, extract Amazon top 100, Amazon rank scraper, Amazon best seller list, Amazon leaderboard, Amazon trending products, discover trending Amazon products, Amazon niche discovery, Amazon top ranked ASINs. Also applies to competitive intelligence via ranking snapshots, spotting up-and-coming products, sourcing bestseller ASINs for further enrichment, tracking rank changes over time, and building bestseller-per-category datasets.
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