youtube-channel-api-skill
This skill extracts structured data about YouTube channels from search results using the BrowserAct API, requiring only search keywords and optional upload date filters. Users should apply it when researching specific YouTube channels, analyzing competitors, building channel databases, tracking influencers, or batch collecting creator information without encountering CAPTCHAs or IP restrictions.
git clone --depth 1 https://github.com/browser-act/skills /tmp/youtube-channel-api-skill && cp -r /tmp/youtube-channel-api-skill/solutions/video-platforms/youtube-channel-api-skill ~/.claude/skills/youtube-channel-api-skillSKILL.md
# YouTube Channel API Skill ## 📖 Introduction This skill provides users with a one-stop channel data extraction service through BrowserAct's YouTube Channel API template. It can directly extract structured channel results from YouTube search. By simply entering search keywords and optional upload date filters, you can get clean, usable channel data directly. ## ✨ Features 1. **No hallucinations, ensuring stable and accurate data extraction**: Pre-set workflows to 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. 4. **Faster execution**: Faster task execution compared to purely AI-driven browser automation solutions. 5. **High cost-effectiveness**: Significantly reduces data acquisition costs compared to AI solutions that consume a large number of tokens. ## 🔑 API Key Guide 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. **The Agent must inform the user at this point**: > "Since you have not configured the BrowserAct API Key yet, please go to the [BrowserAct Console](https://www.browseract.com/reception/integrations) to get your Key." ## 🛠️ Input Parameters Agent should flexibly configure the following parameters according to user needs when calling the script: 1. **KeyWords** - **Type**: `string` - **Description**: Search keywords used to find YouTube channels. - **Example**: `AI`, `automation`, `tech reviews` 2. **Upload_date** - **Type**: `string` - **Description**: Filter channels that have published content within a defined time window. - **Optional Values**: `This week`, `Any time`, etc. - **Default**: `Any time` ## 🚀 Usage Agent should execute the following standalone script to achieve "one command gets results": ```bash # Example call python -u ./scripts/youtube_channel_api.py "Search Keywords" "Upload date" ``` ### ⏳ 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** (e.g., `[14:30:05] Task Status: running`) while running. **Agent Notes**: - 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, then consider triggering the retry mechanism. ## 📊 Data Output After successful execution, the script will directly parse and print the results from the API response. The results include: - `channel_title`: Channel name displayed on YouTube - `channel_link`: Channel page URL - `verified`: Whether the channel is verified - `handle`: Channel handle - `subscribers`: Subscriber count - `description`: Channel description text - `thumbnail`: Channel avatar or thumbnail image URL ## ⚠️ 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 point, **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 execution fails (for example, the output starts with `Error:` or returns empty results), the Agent should **automatically try to execute the script one more time**. 2. **Retry limit**: - Automatic retry is limited to **once**. If the second attempt still fails, stop retrying and report the specific error message to the user. ## 🌟 Typical Use Cases 1. **Creator Discovery**: Find channels related to specific topics for outreach. 2. **Influencer Sourcing**: Identify potential influencers in specific industries. 3. **Competitive Landscape Mapping**: Track competitors' channel growth and updates. 4. **Channel Database Building**: Extract structured data for building creator databases. 5. **Content Trend Tracking**: Monitor new channels popping up around a specific keyword. 6. **Market Research**: Collect data on leading YouTube channels in a target niche. 7. **Brand Monitoring**: Track unofficial or related channels discussing your brand. 8. **Audience Analysis**: Assess the size of audiences in specific fields via subscriber counts. 9. **Partnership Campaigns**: Find active channels that uploaded videos recently. 10. **Automated Data Enrichment**: Pipe YouTube channel details into CRMs or internal tools.
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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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