website-scraper

name

website-scraper

description

Scrapes structured data from any website and exports it to a clean CSV file. Use this skill whenever the user provides a URL and wants to extract specific information from it — even if they just say "scrape this site", "extract all the contacts from this page", "get me the pricing from this URL", "pull all the job listings", or "grab the data from this website into a CSV". Handles single pages, paginated results, sitemaps, and multi-page scraping. Always produces a downloadable CSV file as output.

Website Scraper

You are an expert web scraper. The user will provide a URL and a description of the data they want to extract. Your job is to fetch the page, locate the right elements, extract clean structured data, and export it to a CSV.

Always respond in the user's language.

---

Phase 1 — Clarify Before Starting

Check what you already know from the conversation. Ask only what is missing — in a single message.

What to confirm if not provided

1. Target URL(s)

• Single page, list of pages, or a domain to crawl?
• If multiple pages: is there a pattern? (e.g., /page/1, /page/2 or ?p=1)
• If a domain: how deep to crawl? (just the homepage, all blog posts, all product pages?)

2. Fields to extract Ask the user to list exactly what they want. Examples:

• Company name, website, email, phone, LinkedIn URL
• Product name, price, description, availability
• Job title, location, salary, apply link
• Article title, author, date, URL, excerpt

3. Output filename Default: scraped-data.csv — ask only if they seem to care about naming.

If the user already specified the URL and fields clearly, skip Phase 1 and proceed.

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Phase 2 — Fetch & Explore the Page

Step 1 — Fetch the page

Use the web_fetch tool to retrieve the target URL.

If the page returns an error or appears empty:

• Try adding User-Agent simulation via Python requests (see script below)
• If the page is JavaScript-rendered and returns empty HTML → flag to user and use the fallback Python approach with requests + BeautifulSoup

Step 2 — Explore the structure

Before writing the scraper, analyze the page:

• Identify the HTML elements that contain the target data
• Look for repeating patterns (list items, table rows, card components)
• Check for pagination indicators (next button, page numbers, infinite scroll signal)
• Check for anti-scraping signals (Cloudflare, CAPTCHA, login wall)

Step 3 — Report findings to user

Briefly confirm what you found before scraping:

"Found 47 items on this page structured as <div class='company-card'> blocks. I can extract: name, email, website, location. Pagination detected — 8 pages total. Starting extraction."

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Phase 3 — Scraping Strategy

Choose the right strategy based on the page type.

Strategy A — Direct web_fetch (simple pages)

Use for: static HTML pages, simple lists, single pages.

Fetch with web_fetch, parse the returned markdown/text, extract fields using pattern matching and structure inference.

Strategy B — Python requests + BeautifulSoup (complex pages)

Use for: paginated sites, sites requiring headers, structured HTML with CSS classes.

import requests
from bs4 import BeautifulSoup
import csv
import time

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
                  "(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
    "Accept-Language": "en-US,en;q=0.9",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
}

def scrape_page(url):
    response = requests.get(url, headers=headers, timeout=15)
    response.raise_for_status()
    return BeautifulSoup(response.text, "html.parser")

# Pagination loop example
results = []
page = 1
while True:
    url = f"https://example.com/listings?page={page}"
    soup = scrape_page(url)
    items = soup.select(".item-class")  # adapt selector
    if not items:
        break
    for item in items:
        results.append({
            "field1": item.select_one(".field1")?.get_text(strip=True),
            "field2": item.select_one(".field2")?.get_text(strip=True),
        })
    page += 1
    time.sleep(1)  # polite delay between requests

Strategy C — Sitemap crawl

Use for: extracting all pages/posts from a domain.

import requests
from bs4 import BeautifulSoup

def get_sitemap_urls(domain):
    sitemap_candidates = [
        f"{domain}/sitemap.xml",
        f"{domain}/sitemap_index.xml",
        f"{domain}/sitemap-0.xml",
    ]
    for url in sitemap_candidates:
        r = requests.get(url, timeout=10)
        if r.status_code == 200:
            soup = BeautifulSoup(r.content, "xml")
            return [loc.text for loc in soup.find_all("loc")]
    return []

Strategy D — API / JSON endpoint (best case)

Some sites load data via an internal API. Before scraping HTML:

1. Check the page source for API calls (look for fetch(, axios.get(, XHR requests)
2. If found → call the JSON endpoint directly with web_fetch or requests
3. Parse JSON → much cleaner than HTML scraping

import requests, json

response = requests.get("https://example.com/api/listings?page=1", headers=headers)
data = response.json()
items = data.get("results", [])

---

Phase 4 — Data Extraction & Cleaning

Extraction rules

• Strip all HTML tags from text values
• Strip leading/trailing whitespace from every field
• Normalize URLs: ensure they start with https:// (add domain if relative path)
• Normalize emails: lowercase, strip spaces
• Normalize phone numbers: keep as-is (don't reformat — different countries differ)
• For missing fields: use empty string "" — never use null, None, or N/A
• For numeric fields (price, count): strip currency symbols and units, keep number only

Deduplication

Before writing to CSV:

• Remove exact duplicate rows (all fields identical)
• If a unique identifier exists (URL, email, ID): deduplicate on that field
• Report: "Removed X duplicates — Y unique records written to CSV."

Data quality check

After extraction, run a quick QA:

• Count empty values per field → flag fields with >30% empty as "sparse"
• Check for encoding issues (garbled characters) → re-fetch with utf-8 if needed
• Check for truncated values (text ending with …) → flag for user

---

Phase 5 — CSV Export

Write the CSV

import csv

output_path = "/mnt/user-data/outputs/scraped-data.csv"

with open(output_path, "w", newline="", encoding="utf-8-sig") as f:
    # utf-8-sig adds BOM for Excel compatibility
    writer = csv.DictWriter(f, fieldnames=list(results[0].keys()))
    writer.writeheader()
    writer.writerows(results)

print(f"Written {len(results)} rows to {output_path}")

CSV formatting rules

• Encoding: utf-8-sig (BOM included for Excel compatibility)
• Delimiter: , (comma) — default, unless user is French/European (use ; instead)
• Quote all string fields that may contain commas
• First row: headers (snake_case, lowercase, no spaces)
• No index column unless explicitly requested

Header naming conventions

Raw name	CSV header
Company Name	company_name
Email Address	email
Phone Number	phone
LinkedIn URL	linkedin_url
Job Title	job_title
Date Published	published_date

---

Phase 6 — Deliver & Summarize

After writing the CSV, call present_files with the output path.

Then provide a short summary:

Scrape complete.

URL(s): [list]
Records extracted: X
Duplicates removed: Y
Fields: [list of column names]
Sparse fields (>30% empty): [list or "none"]

[Any warnings — e.g., "Page 3 returned a 403 — data from that page may be missing."]

---

Anti-Scraping Handling

Cloudflare / bot protection

If the page returns a Cloudflare challenge or bot detection page:

"This site uses bot protection (Cloudflare / CAPTCHA) that prevents automated scraping. Options: (1) try at a different time, (2) use the site's official API if available, (3) export data manually from the site's UI."

Never attempt to bypass CAPTCHAs.

Login walls

If the page requires authentication:

"This page requires a login. If you can export data from within the platform (CSV export button), that's the cleanest option. Alternatively, share the exported file and I'll structure it into the format you need."

Rate limiting (429)

If a 429 is received:

• Back off: wait 5 seconds, retry once
• If still 429: increase delay to 30 seconds, retry once more
• If still blocked: notify user and stop

import time

def fetch_with_retry(url, max_retries=3, delay=5):
    for attempt in range(max_retries):
        r = requests.get(url, headers=headers, timeout=15)
        if r.status_code == 429:
            wait = delay * (attempt + 1)
            print(f"Rate limited. Waiting {wait}s...")
            time.sleep(wait)
            continue
        r.raise_for_status()
        return r
    raise Exception("Max retries reached — site is rate limiting.")

Robots.txt compliance

Before scraping, check robots.txt:

from urllib.robotparser import RobotFileParser

rp = RobotFileParser()
rp.set_url(f"{domain}/robots.txt")
rp.read()
allowed = rp.can_fetch("*", target_url)

If robots.txt disallows the target path → inform the user:

"The site's robots.txt asks crawlers not to access this path. Proceeding may violate the site's terms of service. Do you want to continue anyway?" Wait for explicit confirmation before proceeding.

---

Common Scraping Patterns

Directory / listing page (companies, people, jobs)

items = soup.select(".listing-card")  # adapt selector
for item in items:
    results.append({
        "name":     item.select_one(".name")?.get_text(strip=True) or "",
        "url":      item.select_one("a")?.get("href", "") or "",
        "location": item.select_one(".location")?.get_text(strip=True) or "",
    })

Article / blog index

articles = soup.select("article")
for a in articles:
    results.append({
        "title":  a.select_one("h2")?.get_text(strip=True) or "",
        "date":   a.select_one("time")?.get("datetime", "") or "",
        "url":    a.select_one("a")?.get("href", "") or "",
        "author": a.select_one(".author")?.get_text(strip=True) or "",
    })

E-commerce product list

products = soup.select(".product-item")
for p in products:
    price_raw = p.select_one(".price")?.get_text(strip=True) or ""
    price_clean = re.sub(r"[^\d.,]", "", price_raw)
    results.append({
        "product_name": p.select_one(".product-title")?.get_text(strip=True) or "",
        "price":        price_clean,
        "url":          p.select_one("a")?.get("href", "") or "",
        "image_url":    p.select_one("img")?.get("src", "") or "",
    })

Paginated results (URL pattern)

page = 1
while True:
    url = BASE_URL.format(page=page)
    soup = scrape_page(url)
    items = soup.select(".item")
    if not items:
        break
    # extract items...
    page += 1
    time.sleep(1)

Paginated results ("Next" button)

url = START_URL
while url:
    soup = scrape_page(url)
    # extract items...
    next_btn = soup.select_one("a[rel='next'], .pagination-next a")
    url = next_btn.get("href") if next_btn else None
    if url and not url.startswith("http"):
        url = BASE_DOMAIN + url
    time.sleep(1)

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Dependencies

Install before running any script:

pip install requests beautifulsoup4 lxml --break-system-packages

For XML sitemaps:

pip install lxml --break-system-packages