Mmcp.market

google-search-serp skill

by browser-act·browser-act/skills·6.0k stars·MIT

Extracts Google Search results page (SERP) data including organic results, paid ads, related searches, People Also Ask questions, AI Overview text, and total result count from google.com. Use when user mentions Google search results, SERP scraping, google search data, search engine results page, organic rankings, keyword SERP, Google SERP extraction, scrape Google search, Google search API alternative, SEO ranking data, paid search ads, PPC ads on Google, Google search monitoring, keyword research, search results export, check Google rankings, what shows up on Google, search engine scraper, google results checker.

A100/100content scan

Is the google-search-serp skill safe?

Clean: nothing in its files matched our rules. We read 2 files in the folder on 2026-09-28.

No findings.

Install the google-search-serp skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/browser-act/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -r /tmp/skills/solutions/search-research/google-search-serp ~/.claude/skills/google-search-serp
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Google — Search SERP Extraction

Search keyword + parameters → structured SERP data (organic results, ads, related queries, PAA, AI Overview)

Language

All process output to user (progress updates, process notifications) follows the user's language.

Objective

Extract all visible content from a Google Search results page: organic listings, paid ads, related searches, People Also Ask, AI Overview, and total result count.

Prerequisites

  • Target page is already open in the browser: https://www.google.com/search?q={query}

Pre-execution Checks

1. Tool Readiness

If browser-act has been confirmed available in the current session → skip this step.

Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

Capability Components

This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page, never bypassing authentication or access controls. Its role is equivalent to copy-pasting on the user's behalf — the data is already on screen, automation merely saves time. JS code is encapsulated in Python files under the scripts/ directory, invoked via eval "$(python scripts/xxx.py {params})". $(...) is bash syntax; it is recommended to use the bash tool for execution.

Below are all atomic capabilities discovered and verified during the exploration phase, listed by command template with parameters. Simply invoke them as needed — no need to read scripts/*.py source code or re-verify. Only inspect scripts when execution fails for troubleshooting. Combine freely as needed during execution.

DOM: Google Search SERP (data extraction)

Parameters are injected via URL navigation; data is extracted from the server-rendered HTML page:

  1. navigate https://www.google.com/search?q={query}&num={num}&hl={lang}&gl={country}&start={start}
  2. wait stable
  3. eval "$(python scripts/serp-extract.py)"

URL parameters:

  • q: Search query (required)
  • num: Results per page — 10 (default), 20, 50, 100
  • hl: Interface language code — e.g., en, zh-CN, fr, de (omit for browser default)
  • gl: Country targeting code — e.g., us, gb, de, cn (omit for browser default)
  • start: Pagination offset — 0 for page 1, 10 for page 2 (when num=10); formula: (page - 1) * num

Error handling: If extraction returns {"error": true, "message": "captcha required"}, the session is blocked by Google — switch to a browser with a US rotating proxy and retry. If "No search results found" is returned, run screenshot to verify the page loaded correctly before retrying.

Output example:

{
  "searchQuery": {
    "term": "machine learning",
    "url": "https://www.google.com/search?q=machine+learning",
    "device": "DESKTOP",
    "page": 1,
    "type": "SEARCH",
    "domain": "www.google.com",
    "countryCode": "US",
    "languageCode": "en"
  },
  "resultsTotal": "14900000000",
  "organicResults": [
    {
      "position": 1,
      "type": "organic",
      "title": "Machine learning - Wikipedia",
      "url": "https://en.wikipedia.org/wiki/Machine_learning",
      "displayedUrl": "en.wikipedia.org › wiki › Machine_learning",
      "description": "Machine learning (ML) is a field of study in artificial intelligence...",
      "emphasizedKeywords": ["machine learning", "ML"],
      "siteLinks": [
        {"title": "Supervised learning", "url": "https://en.wikipedia.org/wiki/Supervised_learning"}
      ]
    }
  ],
  "paidResults": [
    {
      "adPosition": 1,
      "type": "paid",
      "title": "Learn Machine Learning Online",
      "url": "https://example.com/ml-course",
      "displayedUrl": "example.com",
      "description": null,
      "siteLinks": []
    }
  ],
  "relatedQueries": [
    {"title": "machine learning examples", "url": "https://www.google.com/

Field notes:

  • resultsTotal: total result count string (commas removed), null when stat bar is absent
  • organicResults[*].emphasizedKeywords: bold/italic terms in the description, empty array when none
  • organicResults[*].siteLinks: sub-links shown under some results, empty array when none
  • paidResults[*].description: ad description text, null when the advertiser omits it
  • aiOverview: AI Overview paragraph text joined with spaces, null when absent or unavailable

Pagination

URL Pagination: URL pattern https://www.google.com/search?q={query}&num={num}&start={(page-1)*num}. Increment start by num for each subsequent page. Termination: organicResults array is empty, or start exceeds the desired page count.

Success Criteria

organicResults.length >= 1 and searchQuery.term matches the requested keyword.

Known Limitations

  • AI Overview unreliable in stealth sessions: Google rarely serves AI Overview to automated browsers. aiOverview will be null in most sessions; it only populates when Google serves it without login or cookie context.
  • Paid ad descriptions often null: Many ads omit a description block — paidResults[*].description returns null for those. This reflects the advertiser's choice, not an extraction failure.
  • Google anti-bot detection: Stealth browsers may be redirected to a CAPTCHA (/sorry/ page). Use a browser session with a US rotating proxy to reduce blocks. Solve any CAPTCHA manually via remote-assist if needed.
  • Related queries load asynchronously: relatedQueries requires wait stable after navigation; results may be empty if the page has not fully settled.

Execution Efficiency

  • Batch orchestration: Write a bash script to loop through keywords serially within one browser session; add a 2–5 second delay between requests to avoid triggering rate limits.
  • Test before batch execution: After writing a batch script, test with 1–2 keywords first to verify it runs correctly; only then run the full batch.
  • Reduce redundant pre-operations: Reuse the same browser session across multiple keywords — navigate directly to each search URL without returning to the homepage.
  • Error resumption: Save results keyword by keyword; on CAPTCHA or failure, resume from the breakpoint rather than starting over.
  • Multi-session parallelism: To increase throughput, open multiple stealth browser sessions (each with its own proxy fingerprint) and distribute keywords across them.

Experience Notes

Path: {working-directory}/browser-act-skill-forge-memories/google-search-scraper-google-search-serp.memory.md (working directory is determined by the Agent running the Skill, typically the project root or current working directory)

Before execution: If the file exists, read it first — it records unexpected situations encountered during past executions (e.g., a strategy has become ineffective); adjust strategy order accordingly.

After execution: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line: {YYYY-MM-DD}: {what happened} → {conclusion}

Normal execution does not write to the file. Do not record what keywords were used or how many results were returned — those are task outputs, not experience.

More skills from browser-act/skills

  • A1688-product-detailExtracts 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.
  • Aairbnb-listing-detailFetches 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.
  • Aairbnb-search-listingExtracts 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.
  • Aamazon-alexa-qaAmazon 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.
  • Aamazon-asin-lookup-api-skillThis 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.
  • Aamazon-best-selling-products-finder-api-skillThis 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.
  • Aamazon-bestseller-listingAmazon 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.
  • Aamazon-buy-box-monitor-api-skillThis skill helps users extract basic product details other sellers prices and seller ratings from Amazon via ASIN automatically using the BrowserAct API. Agent should proactively apply this skill when users express needs like query Amazon buy box information, monitor Amazon product prices, extract Amazon product details by ASIN, check other sellers prices on Amazon, get Amazon seller ratings and feedback count, monitor buy box ownership for a specific ASIN, track Amazon fulfillment methods for competitors, compare Amazon product prices across different sellers, retrieve Amazon buy box availability status, analyze Amazon seller profile details.
  • Aamazon-competitor-analyzerScrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.
  • Aamazon-listing-competitor-analysis-skillThis skill helps users analyze Amazon competitor listings by ASIN and produce structured competitive intelligence plus strategic opportunity points for their own go-to-market. The Agent should proactively apply this skill when users want to analyze a competitor Amazon listing by ASIN, understand what a top-ranked product does right in content keywords or visuals, find market gaps and unmet buyer needs, turn competitor research into opportunity maps for their brand, identify keyword placement patterns on rival listings, extract SEO insights from Amazon product pages, reverse-engineer competitor bullet and title strategies, mine competitor reviews for buyer psychology, compare seller and A plus content patterns, run gap analysis before launching a new SKU, research why a listing wins conversion signals, synthesize whitespace you can own versus the diagnosed listing, or say just look at this ASIN with a competitive or optimization angle.
  • Aamazon-product-api-skillThis skill helps users extract structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications. Use this skill when users want to search for products on Amazon, find the best selling brand products, track price changes for items, get a list of categories with high ratings, compare different brand products on Amazon, extract Amazon product data for market research, look for products in a specific language or marketplace, analyze competitor pricing for keywords, find featured products for search terms, get technical specifications like material or color for product lists.
  • Aamazon-product-detailAmazon product detail page scraper: extract full product data from any open Amazon product detail URL (any /dp/{asin} or /gp/product/{asin} page across all Amazon regional TLDs) — returns asin, url, title, brand, price, listPrice, stars, reviewsCount, starsBreakdown (5/4/3/2/1 star percentages), answeredQuestions, inStock, inStockText, delivery, fastestDelivery, returnPolicy, breadCrumbs, features (bullet points), description, bookDescription, thumbnailImage, highResolutionImages, galleryThumbnails, productOverview (Brand/Model/etc.), attributes (tech spec table), attributesMapped (flat key-value), bestsellerRanks (rank + category + url), variantAttributes (currently selected color/size/style), variantAsins, seller (name + id + url), isAmazonChoice, amazonChoiceText, monthlyPurchaseVolume, hasAPlusContent, hasBrandStory, aiReviewsSummary, reviewsLink, productPageReviews (sample), videosCount, locationText, loadedCountryCode. Works on amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, amazon.com.mx, amazon.com.br, amazon.nl, amazon.se, amazon.sg, amazon.ae, amazon.sa, amazon.pl, amazon.tr, amazon.eg. Use when user mentions Amazon product page, Amazon /dp/, Amazon dp URL, Amazon ASIN scraper, Amazon product detail, Amazon PDP, Amazon product data, Amazon product info, Amazon product fields, Amazon product attributes, Amazon full field extraction, Amazon per-ASIN enrichment, Amazon rating breakdown, Amazon stars breakdown, Amazon bestseller rank, Amazon BSR, Amazon variants, Amazon variant ASINs, Amazon color size options, Amazon feature bullets, Amazon A+ content, Amazon brand story, Amazon AI review summary, Amazon bought in past month, Amazon monthly sales volume, Amazon Amazon's Choice badge, Amazon seller info, scrape Amazon product, enrich Amazon ASIN, Amazon ASIN details, Amazon product review data. Also applies to bulk ASIN enrichment from a list of URLs, competitive product research, brand catalog audits, price and stock monitoring per ASIN, and building a normalized product dataset from a list of Amazon URLs.

All agent skills → · MCP servers