youtube-comments-api-skill
This skill extracts structured video and comment data from YouTube using the BrowserAct API, eliminating manual scraping and CAPTCHA issues. Use it when you need to gather viewer comments, analyze audience sentiment on specific topics, compile competitor video data, monitor public response to search keywords, or collect engagement metrics without token-intensive browser automation.
git clone --depth 1 https://github.com/browser-act/skills /tmp/youtube-comments-api-skill && cp -r /tmp/youtube-comments-api-skill/solutions/video-platforms/youtube-comments-api-skill ~/.claude/skills/youtube-comments-api-skillSKILL.md
# YouTube Comments API Automation Skill ## 📖 Introduction This skill provides a one-stop extraction service for YouTube video and comment data through the BrowserAct YouTube Comments API template. It can extract structured video results along with their respective comments directly from YouTube. By simply providing search keywords, comment limits, and scroll counts, you can acquire clean and ready-to-use video and comment datasets directly. ## ✨ Features 1. **Zero Hallucination, Ensuring Stable and Accurate Data Extraction**: Pre-configured workflows avoid AI generative hallucinations. 2. **No CAPTCHA Issues**: No need to handle reCAPTCHA or other verification challenges. 3. **No IP Access Restrictions or Geo-fencing**: No need to deal with regional IP limits. 4. **More Agile Execution Speed**: Faster task execution compared to pure AI-driven browser automation solutions. 5. **Extremely High Cost-Efficiency**: Significantly reduces data acquisition costs compared to AI solutions that consume a large number of tokens. ## 🔑 API Key Guidance Process Before running, you must first check the `BROWSERACT_API_KEY` environment variable. If it is not set, do not take any other actions; you should request and wait for the user to provide it. **At this point, the Agent must inform the user**: > "Since you have not configured the BrowserAct API Key yet, please go to the [BrowserAct Console](https://www.browseract.com/reception/integrations) first to get your Key." ## 🛠️ Input Parameters When invoking the script, the Agent should flexibly configure the following parameters based on user needs: 1. **keywords** - **Type**: `string` - **Description**: Search keywords used to find videos on YouTube. Can be any keyword or phrase. - **Example**: `AI`, `automation`, `web scraping` - **Default**: `AI` 2. **Comments_limit** - **Type**: `number` - **Description**: Maximum number of comments to extract per video. - **Example**: `10`, `20`, `50` - **Default**: `10` 3. **Scroll_count** - **Type**: `number` - **Description**: Number of times to scroll in the comments section to load more comments before extraction. - **Example**: `1`, `2`, `5`, `10` - **Default**: `2` ## 🚀 Invocation Method (Recommended) The Agent should execute the following standalone script to achieve "one command, get results": ```bash # Example invocation python -u ./scripts/youtube_comments_api.py "keywords" "Comments_limit" "Scroll_count" ``` ### ⏳ Running Status Monitoring Since this task involves automated browser operations, it may take a long time (several minutes). While running, the script will **continuously output timestamped status logs** (e.g., `[14:30:05] Task Status: running`). **Agent Instructions**: - While waiting for the script to return a result, please keep monitoring the terminal output. - As long as the terminal is still outputting new status logs, it means the task is running normally. Do not misjudge it as a deadlock or unresponsiveness. - Only if the status remains unchanged for a long time or the script stops outputting without returning a result, should you consider triggering the retry mechanism. ## 📊 Data Output Description Upon successful execution, the script will directly parse and print the results from the API response. The results include two linked datasets: **Video fields:** - `video_name`: Video title shown in the list - `video_url`: Video URL - `video_publication_time`: Published time - `video_view_count`: View count **Comment fields:** - `commenter_name`: Comment author display name - `commenter_url`: Comment author channel URL - `comment_text`: Comment content - `comment_publish_date`: Comment publish time - `comment_likes`: Like count for the comment - `reply_count`: Number of replies ## ⚠️ Error Handling & Retry During the execution of the script, if an error occurs (such as network fluctuations or task failure), the Agent should follow this logic: 1. **Check the Output Content**: - If the output **contains** `"Invalid authorization"`, it means the API Key is invalid or expired. At this time, **do not retry**; you should guide the user to recheck and provide the correct API Key. - If the output **does not contain** `"Invalid authorization"` but the task fails (e.g., the output starts with `Error:` or returns an empty result), the Agent should **automatically try to execute the script one more time**. 2. **Retry Limit**: - Automatic retries are limited to **one time** only. If the second attempt still fails, stop retrying and report the specific error message to the user. ## 🌟 Typical Use Cases 1. **Audience Insight**: Turning comments into product feedback and sentiment signals based on specific keywords. 2. **Content Research**: Understanding what viewers are discussing under popular video topics. 3. **Competitive Monitoring**: Tracking comments and feedback on competitors' YouTube channels. 4. **Community Insight**: Analyzing what users care about in a specific niche like automation or AI. 5. **Topic Tracking**: Monitoring the public response and interaction for trending search terms. 6. **Sentiment Analysis**: Gathering raw text data from comments to evaluate viewer opinions. 7. **Objections and Feature Requests**: Identifying user pain points from product-related video comments. 8. **Automated Data Integration**: Sending video and comment data directly into CRM or BI tools via API. 9. **Engagement Metrics Collection**: Tracking likes and reply counts for top comments. 10. **Market Research**: Extracting a large set of video metadata combined with user discussions for market studies.
Extracts 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.
Fetches complete Airbnb listing details for a given numeric listing ID via the internal GraphQL API, returning title, room type, description, amenities, photos, coordinates, city, house rules, highlights, ratings, review count, bedroom configuration, and property overview. Use when user mentions Airbnb listing details, Airbnb property info, Airbnb room details, get Airbnb listing data, Airbnb amenities list, Airbnb house rules, Airbnb property description, Airbnb detail page scraper, Airbnb rooms detail, Airbnb property page data, Airbnb listing info, fetch Airbnb room details, pull Airbnb listing.
Extracts Airbnb accommodation search results from a destination query via SSR-embedded data, returning listing ID, URL, name, coordinates, rating, price, photos, and badge info for each result, plus pagination cursors for multi-page retrieval. Use when user mentions Airbnb search results, Airbnb listings, vacation rental search, short-term rental listings, scrape Airbnb, get Airbnb data, find rentals on Airbnb, Airbnb destination search, Airbnb property list, Airbnb stays search, Airbnb accommodation results, pull Airbnb listings, collect Airbnb search data, Airbnb scraper, Airbnb search page extraction, Airbnb search by destination.
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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