--- name: web-scraper-cd0bf65adc3d description: >- Extract structured data from web pages and load it into databases. Use when a user asks to scrape a website, build a data pipeline, extract data from a webpage, pull prices from a site, collect links, gather product listings, download page content, parse HTML, set up ETL, or automate data collection. Handles static HTML, JavaScript-rendered pages, anti-bot proxies (Bright Data), data transformation, deduplication, and database loading. license: Apache-2.0 compatibility: "Requires Python 3 with requests and beautifulsoup4 installed. For JS-rendered pages, requires playwright." metadata: author: terminal-skills version: "1.1.0" category: automation tags: "scraping, data-extraction, beautifulsoup, etl, bright-data" --- # Web Scraper ## Overview Extract structured data from web pages by parsing HTML, selecting elements with CSS selectors, and outputting clean data in JSON, CSV, or other formats. Handles both static HTML and JavaScript-rendered pages. ## Instructions When a user asks you to scrape or extract data from a web page, follow these steps: ### Step 1: Assess the target Determine: - **URL**: What page to scrape - **Data needed**: What specific elements to extract (prices, titles, links, tables) - **Rendering**: Is the page static HTML or does it require JavaScript? - **Scale**: Single page or multiple pages with pagination? ### Step 2: Fetch the page **For static HTML:** ```python import requests from bs4 import BeautifulSoup def fetch_page(url): headers = { "User-Agent": "Mozilla/5.0 (compatible; research-bot/1.0)" } response = requests.get(url, headers=headers, timeout=30) response.raise_for_status() # Pass bytes, not response.text: when the Content-Type header has no # charset, requests guesses one and symbols such as £ turn into mojibake. return BeautifulSoup(response.content, "html.parser") ``` **For JavaScript-rendered pages** (`pip install playwright`, then `playwright install` once to download the browsers): ```python from playwright.sync_api import sync_playwright def fetch_js_page(url): with sync_playwright() as p: browser = p.chromium.launch(headless=True) page = browser.new_page() page.goto(url, wait_until="networkidle") content = page.content() browser.close() return BeautifulSoup(content, "html.parser") ``` ### Step 3: Extract data with CSS selectors Identify the right selectors by inspecting the page structure: ```python def extract_items(soup, selectors): items = [] containers = soup.select(selectors["container"]) for container in containers: item = {} for field, selector in selectors["fields"].items(): el = container.select_one(selector) if el: if el.name == "img": item[field] = el.get("src", "") elif el.name == "a": item[field] = {"text": el.get_text(strip=True), "href": el.get("href", "")} else: item[field] = el.get_text(strip=True) else: item[field] = None items.append(item) return items ``` **Usage example:** ```python selectors = { "container": "div.product-card", "fields": {"name": "h2.product-title", "price": "span.price", "rating": "span.rating-value", "link": "a.product-link"}, } items = extract_items(soup, selectors) ``` ### Step 4: Handle pagination ```python def scrape_all_pages(base_url, selectors, max_pages=10): all_items = [] for page_num in range(1, max_pages + 1): url = f"{base_url}?page={page_num}" soup = fetch_page(url) items = extract_items(soup, selectors) if not items: break all_items.extend(items) return all_items ``` ### Step 5: Output structured data ```python import json import csv def save_json(data, filename): with open(filename, "w") as f: json.dump(data, f, indent=2) def save_csv(data, filename): if not data: return with open(filename, "w", newline="") as f: writer = csv.DictWriter(f, fieldnames=data[0].keys()) writer.writeheader() writer.writerows(data) ``` ## Examples ### Example 1: Extract product listings **User request:** "Scrape all product names and prices from this catalog page" **Script outline:** ```python soup = fetch_page("https://books.toscrape.com/") # public practice site for scrapers products = [] for card in soup.select("article.product_pod"): link = card.select_one("h3 a") price = card.select_one("p.price_color") products.append({ "name": link.get("title", "N/A") if link else "N/A", "price": price.get_text(strip=True) if price else "N/A", }) save_csv(products, "products.csv") print(f"Extracted {len(products)} products") ``` **Output:** ``` Extracted 20 products | name | price | |----------------------|---------| | A Light in the Attic | £51.77 | | Tipping the Velvet | £53.74 | ``` ### Example 2: Extract a data table from a page **User request:** "Pull the statistics table from this Wikipedia article" **Script outline:** ```python soup = fetch_page("https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)") table = soup.select_one("table.wikitable") headers = [th.get_text(strip=True) for th in table.select("tr:first-child th")] rows = [] for tr in table.select("tr")[1:]: cells = [td.get_text(strip=True) for td in tr.select("td")] if len(cells) == len(headers): rows.append(dict(zip(headers, cells))) save_json(rows, "table_data.json") print(f"Extracted {len(rows)} rows with columns: {headers}") ``` **Output:** ``` Extracted 238 rows with 6 columns (first: 'Country or territory', 'Population(1 July 2022)', ...) Saved to table_data.json ``` ### Step 6: Transform and deduplicate Clean raw scraped data before loading — normalize prices, deduplicate by content hash, validate required fields. ```python import hashlib from datetime import datetime, timezone def transform_products(raw_items): """Clean and deduplicate scraped product data.""" seen_hashes = set() clean = [] for item in raw_items: if not item.get("name") or not item.get("price"): continue # Normalize price: "$1,299.99" → 129999 (cents) price_str = item["price"].replace(",", "").replace("$", "").strip() try: price_cents = int(float(price_str) * 100) except ValueError: continue content_hash = hashlib.md5( f"{item['name']}|{item.get('link', '')}".encode() ).hexdigest() if content_hash in seen_hashes: continue seen_hashes.add(content_hash) clean.append({ "name": item["name"][:500], "price_cents": price_cents, "url": (item.get("link") or {}).get("href", ""), # link is None when missing "rating": item.get("rating"), "content_hash": content_hash, "scraped_at": datetime.now(timezone.utc).isoformat(), }) return clean ``` ### Step 7: Load into a database Batch upsert into Postgres/Supabase for persistent storage with automatic price change tracking. ```python from supabase import create_client def load_to_supabase(products, supabase_url, supabase_key): """Batch upsert products into Supabase with conflict handling.""" client = create_client(supabase_url, supabase_key) for i in range(0, len(products), 100): batch = products[i:i+100] client.table("products").upsert( batch, on_conflict="content_hash" # column needs a UNIQUE constraint; matching rows are updated ).execute() ``` ### Step 8: Handle anti-bot protection with Bright Data For sites that block datacenter IPs, route the request through a Bright Data zone proxy. The zone's Overview tab gives the customer ID, zone name and password; the host and port are on its Proxy Examples page. ```python import os import requests from bs4 import BeautifulSoup def fetch_with_proxy(url): # Username format from the Bright Data docs: brd-customer--zone- user = f"brd-customer-{os.environ['BRIGHT_DATA_CUSTOMER_ID']}-zone-{os.environ['BRIGHT_DATA_ZONE']}" host = os.environ["BRIGHT_DATA_PROXY_HOST"] # e.g. brd.superproxy.io:33335, copied from the zone page proxy_url = f"http://{user}:{os.environ['BRIGHT_DATA_ZONE_PASSWORD']}@{host}" response = requests.get( url, proxies={"http": proxy_url, "https": proxy_url}, headers={"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)"}, timeout=30, ) response.raise_for_status() return BeautifulSoup(response.content, "html.parser") ``` ## Guidelines - Always set a User-Agent header and a timeout (30 seconds) on every HTTP request. - Respect `robots.txt`. Check it before scraping and honor disallow rules. - Add delays between requests when scraping multiple pages (`time.sleep(1)`). Do not hammer servers. - If a selector returns no results, the page structure may have changed. Report this to the user rather than returning empty data silently. - For pages behind login walls, inform the user that authentication is required and ask for guidance. - When scraping tables, always validate that row cell counts match header counts before zipping. - Use Bright Data proxies only for sites that block datacenter IPs, and check their pricing before a large run. - Separate extract, transform, and load stages so each can fail and retry independently. --- Source attribution: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/web-scraper/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/web-scraper/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Marketing, Product Management Roles: Data Analyst, Data Scientist, Growth Marketer, Marketing Analyst, Product Manager Specialties: API, Backend, Cross Platform Tools: Beautifulsoup, Playwright, Requests Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: c7ad2b37158594e0c682b12af8df3bc5ef30528ff2e579234ec989f1e7be387d