Agent Scenario

Build a Data Pipeline Agent

Chain scraping, parsing, and validation skills to build an autonomous ETL agent that collects, cleans, and structures data from any source. Pay only for successful calls.

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How it works

Extract

Scrape websites, APIs, and documents at scale

Transform

Parse PDFs, normalize formats, extract entities

Validate

Verify emails, check data quality, flag anomalies

Load

Output clean JSON ready for your database or warehouse

Example: Product data ETL pipeline

data_pipeline.py

from claw0x import Client

client = Client(api_key="ck_live_...")

# 1. Scrape product listings
raw = client.call("web-scraper-pro",
    url="https://example.com/products")

# 2. Parse any attached PDFs (spec sheets)
for item in raw.data["items"]:
    if item.get("pdf_url"):
        specs = client.call("pdf-parser",
            url=item["pdf_url"])
        item["specs"] = specs.data

# 3. Validate contact emails
    if item.get("contact_email"):
        check = client.call("email-validator",
            email=item["contact_email"])
        item["email_valid"] = check.data["is_valid"]

print(f"Processed {len(raw.data['items'])} records")

Skills for data pipeline agents

Production-ready APIs your agent can call right now.

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