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Web Scraper As A Service

Build client-ready web scrapers with clean data output.

Rating: 4.3 (386 reviews)
Downloads: 834 downloads
Version: 1.0.0

Overview

Build client-ready web scrapers with clean data output.

✨Key Features

Analyze the Target

Build the Scraper

Data Cleaning

Client Deliverable Package

Output to User

Complete Documentation

View Source →

Web Scraper as a Service

Turn scraping briefs into deliverable scraping projects. Generates the scraper, runs it, cleans the data, and packages everything for the client.

How to Use

text

/web-scraper-as-a-service "Scrape all products from example-store.com — need name, price, description, images. CSV output."
/web-scraper-as-a-service https://example.com --fields "title,price,rating,url" --format csv
/web-scraper-as-a-service brief.txt

Scraper Generation Pipeline

Step 1: Analyze the Target

Before writing any code:

Fetch the target URL to understand the page structure
Identify:
Is the site server-rendered (static HTML) or client-rendered (JavaScript/SPA)?
What anti-scraping measures are visible? (Cloudflare, CAPTCHAs, rate limits)
Pagination pattern (URL params, infinite scroll, load more button)
Data structure (product cards, table rows, list items)
Total estimated volume (number of pages/items)
Choose the right tool:
Static HTML → Python + requests + BeautifulSoup
JavaScript-rendered → Python + playwright
API available → Direct API calls (check network tab patterns)

Step 2: Build the Scraper

Generate a complete Python script in scraper/ directory:

text

scraper/
  scrape.py           # Main scraper script
  requirements.txt    # Dependencies
  config.json         # Target URLs, fields, settings
  README.md           # Setup and usage instructions for client

scrape.py must include:

python

# Required features in every scraper:

# 1. Configuration
import json
config = json.load(open('config.json'))

# 2. Rate limiting (ALWAYS — be respectful)
import time
DELAY_BETWEEN_REQUESTS = 2  # seconds, adjustable in config

# 3. Retry logic
MAX_RETRIES = 3
RETRY_DELAY = 5

# 4. User-Agent rotation
USER_AGENTS = [
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36...",
    # ... at least 5 user agents
]

# 5. Progress tracking
print(f"Scraping page {current}/{total} — {items_collected} items collected")

# 6. Error handling
# - Log errors but don't crash on individual page failures
# - Save progress incrementally (don't lose data on crash)
# - Write errors to error_log.txt

# 7. Output
# - Save data incrementally (append to file, don't hold in memory)
# - Support CSV and JSON output
# - Clean and normalize data before saving

# 8. Resume capability
# - Track last successfully scraped page/URL
# - Can resume from where it left off if interrupted

Step 3: Data Cleaning

After scraping, clean the data:

Remove duplicates (by unique identifier or composite key)
Normalize text (strip extra whitespace, fix encoding issues, consistent capitalization)
Validate data (no empty required fields, prices are numbers, URLs are valid)
Standardize formats (dates to ISO 8601, currency to numbers, consistent units)
Generate data quality report:

text

Data Quality Report
   ───────────────────
   Total records: 2,487
   Duplicates removed: 13
   Empty fields filled: 0
   Fields with issues: price (3 records had non-numeric values — cleaned)
   Completeness: 99.5%

Step 4: Client Deliverable Package

Generate a complete deliverable:

text

delivery/
  data.csv                    # Clean data in requested format
  data.json                   # JSON alternative
  data-quality-report.md      # Quality metrics
  scraper-documentation.md    # How the scraper works
  README.md                   # Quick start guide

scraper-documentation.md includes:

What was scraped and from where
How many records collected
Data fields and their descriptions
How to re-run the scraper
Known limitations
Date of scraping

Step 5: Output to User

Present:

Summary: X records scraped from Y pages, Z% data quality
Sample data: First 5 rows of the output
File locations: Where the deliverables are saved
Client handoff notes: What to tell the client about the data

Scraper Templates

Based on the target type, use the appropriate template:

E-commerce Product Scraper

Fields: name, price, original_price, discount, description, images, category, sku, rating, review_count, availability, url

Real Estate Listings

Fields: address, price, bedrooms, bathrooms, sqft, lot_size, listing_type, agent, description, images, url

Job Listings

Fields: title, company, location, salary, job_type, description, requirements, posted_date, url

Directory/Business Listings

Fields: business_name, address, phone, website, category, rating, review_count, hours, description

News/Blog Articles

Fields: title, author, date, content, tags, url, image

Ethical Scraping Rules

Always respect robots.txt — check before scraping
Rate limit — minimum 2 second delay between requests
Identify yourself — use realistic but honest User-Agent
Don't scrape personal data (emails, phone numbers) unless explicitly authorized by the client AND the data is publicly displayed
Cache responses — don't re-scrape pages unnecessarily
Check ToS — note if the site's terms prohibit scraping and inform the client

Installation

Terminal bash


openclaw install web-scraper-as-a-service

Copied!

💻Code Examples

/web-scraper-as-a-service brief.txt

web-scraper-as-a-service-brieftxt.txt

## Scraper Generation Pipeline

### Step 1: Analyze the Target

Before writing any code:

1. **Fetch the target URL** to understand the page structure
2. **Identify**:
   - Is the site server-rendered (static HTML) or client-rendered (JavaScript/SPA)?
   - What anti-scraping measures are visible? (Cloudflare, CAPTCHAs, rate limits)
   - Pagination pattern (URL params, infinite scroll, load more button)
   - Data structure (product cards, table rows, list items)
   - Total estimated volume (number of pages/items)
3. **Choose the right tool**:
   - Static HTML → Python + `requests` + `BeautifulSoup`
   - JavaScript-rendered → Python + `playwright`
   - API available → Direct API calls (check network tab patterns)

### Step 2: Build the Scraper

Generate a complete Python script in `scraper/` directory:

# - Can resume from where it left off if interrupted

---can-resume-from-where-it-left-off-if-interrupted.txt

### Step 3: Data Cleaning

After scraping, clean the data:

1. **Remove duplicates** (by unique identifier or composite key)
2. **Normalize text** (strip extra whitespace, fix encoding issues, consistent capitalization)
3. **Validate data** (no empty required fields, prices are numbers, URLs are valid)
4. **Standardize formats** (dates to ISO 8601, currency to numbers, consistent units)
5. **Generate data quality report**:

example.txt

/web-scraper-as-a-service "Scrape all products from example-store.com — need name, price, description, images. CSV output."
/web-scraper-as-a-service https://example.com --fields "title,price,rating,url" --format csv
/web-scraper-as-a-service brief.txt

example.txt

scraper/
  scrape.py           # Main scraper script
  requirements.txt    # Dependencies
  config.json         # Target URLs, fields, settings
  README.md           # Setup and usage instructions for client

example.py

# Required features in every scraper:

# 1. Configuration
import json
config = json.load(open('config.json'))

# 2. Rate limiting (ALWAYS — be respectful)
import time
DELAY_BETWEEN_REQUESTS = 2  # seconds, adjustable in config

# 3. Retry logic
MAX_RETRIES = 3
RETRY_DELAY = 5

# 4. User-Agent rotation
USER_AGENTS = [
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36...",
    # ... at least 5 user agents
]

# 5. Progress tracking
print(f"Scraping page {current}/{total} — {items_collected} items collected")

# 6. Error handling
# - Log errors but don't crash on individual page failures
# - Save progress incrementally (don't lose data on crash)
# - Write errors to error_log.txt

# 7. Output
# - Save data incrementally (append to file, don't hold in memory)
# - Support CSV and JSON output
# - Clean and normalize data before saving

# 8. Resume capability
# - Track last successfully scraped page/URL
# - Can resume from where it left off if interrupted

example.txt

Data Quality Report
   ───────────────────
   Total records: 2,487
   Duplicates removed: 13
   Empty fields filled: 0
   Fields with issues: price (3 records had non-numeric values — cleaned)
   Completeness: 99.5%

example.txt

delivery/
  data.csv                    # Clean data in requested format
  data.json                   # JSON alternative
  data-quality-report.md      # Quality metrics
  scraper-documentation.md    # How the scraper works
  README.md                   # Quick start guide