web-scrape

name	web_scrape
version	1.0.0
agent	browser
risk_level	low
description	Fetch and extract content from web pages. Parse HTML, extract text, tables, and structured data. Handle JavaScript-rendered content with headless browser.

Web Scrape Skill

Purpose

Fetch web pages and extract structured content. This skill enables Forge agents to retrieve full page content, extract specific elements, parse tables, and handle dynamic JavaScript-rendered content.

Safety Rules

ETHICAL SCRAPING

1. Respect robots.txt - check before scraping
2. Honor rate limits - don't overwhelm servers
3. Identify as bot - use honest User-Agent when appropriate
4. Cache aggressively - don't re-fetch unchanged content
5. Respect noindex/nofollow - honor meta directives

CONTENT RESTRICTIONS

1. Never scrape login-protected content without credentials
2. Never bypass paywalls
3. Never scrape for personal data harvesting
4. Respect copyright - don't bulk-download copyrighted content

CONSENSUS REQUIREMENTS

Operation	Risk Level	Consensus Required
fetch_page	low	any
extract_text	low	any
extract_tables	low	any
extract_links	low	any
screenshot	low	any
fill_form	medium	majority

Capabilities

fetch_page

Retrieve a web page's HTML content.

Using curl:

# Simple fetch
curl -s "https://example.com/page" -o page.html

# With headers
curl -s "https://example.com/page" \
  -H "User-Agent: AnimusForgeBot/1.0 (+https://github.com/yourrepo)" \
  -H "Accept: text/html" \
  -o page.html

# Follow redirects
curl -sL "https://example.com/page" -o page.html

# With timeout
curl -s --max-time 30 "https://example.com/page" -o page.html

Using Python requests:

import requests
from urllib.parse import urlparse

def fetch_page(url: str, timeout: int = 30) -> dict:
    """Fetch a web page with proper error handling."""

    headers = {
        "User-Agent": "AnimusForgeBot/1.0 (+https://github.com/yourrepo)",
        "Accept": "text/html,application/xhtml+xml",
        "Accept-Language": "en-US,en;q=0.9",
    }

    try:
        response = requests.get(url, headers=headers, timeout=timeout)
        response.raise_for_status()

        return {
            "success": True,
            "url": response.url,  # Final URL after redirects
            "status_code": response.status_code,
            "content_type": response.headers.get('content-type', ''),
            "html": response.text,
            "encoding": response.encoding,
        }

    except requests.RequestException as e:
        return {
            "success": False,
            "url": url,
            "error": str(e)
        }

Using Playwright for JavaScript-rendered content:

from playwright.sync_api import sync_playwright

def fetch_js_page(url: str, wait_for: str = None) -> dict:
    """Fetch page with full JavaScript rendering."""

    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        context = browser.new_context(
            user_agent="AnimusForgeBot/1.0 (+https://github.com/yourrepo)"
        )
        page = context.new_page()

        try:
            page.goto(url, wait_until="networkidle")

            if wait_for:
                page.wait_for_selector(wait_for, timeout=10000)

            return {
                "success": True,
                "url": page.url,
                "title": page.title(),
                "html": page.content(),
            }

        except Exception as e:
            return {
                "success": False,
                "url": url,
                "error": str(e)
            }

        finally:
            browser.close()

---

extract_text

Extract readable text content from HTML.

Using BeautifulSoup:

from bs4 import BeautifulSoup
import re

def extract_text(html: str, selector: str = None) -> dict:
    """Extract clean text from HTML."""

    soup = BeautifulSoup(html, 'html.parser')

    # Remove script and style elements
    for element in soup(['script', 'style', 'nav', 'footer', 'header']):
        element.decompose()

    if selector:
        # Extract from specific element
        target = soup.select_one(selector)
        if target:
            text = target.get_text(separator='\n', strip=True)
        else:
            text = ""
    else:
        # Extract all text
        text = soup.get_text(separator='\n', strip=True)

    # Clean up whitespace
    text = re.sub(r'\n\s*\n', '\n\n', text)

    return {
        "success": True,
        "text": text,
        "char_count": len(text),
        "word_count": len(text.split())
    }

Extract main article content:

def extract_article(html: str) -> dict:
    """Extract main article content, removing boilerplate."""

    soup = BeautifulSoup(html, 'html.parser')

    # Try common article selectors
    article_selectors = [
        'article',
        'main',
        '[role="main"]',
        '.post-content',
        '.article-content',
        '.entry-content',
        '#content',
    ]

    for selector in article_selectors:
        article = soup.select_one(selector)
        if article and len(article.get_text(strip=True)) > 200:
            # Clean it up
            for element in article(['script', 'style', 'nav', 'aside']):
                element.decompose()

            return {
                "success": True,
                "title": soup.title.string if soup.title else "",
                "content": article.get_text(separator='\n', strip=True),
                "selector_used": selector
            }

    # Fallback to body
    body = soup.body
    if body:
        for element in body(['script', , , , , ]):
            element.decompose()
         {
            : ,
            : soup.title.string  soup.title  ,
            : body.get_text(separator=, strip=),
            : 
        }

     {: , : }

---

extract_tables

Extract tables from HTML into structured data.

import pandas as pd
from bs4 import BeautifulSoup

def extract_tables(html: str) -> dict:
    """Extract all tables from HTML."""

    soup = BeautifulSoup(html, 'html.parser')
    tables = []

    for i, table in enumerate(soup.find_all('table')):
        try:
            # Try pandas for clean extraction
            df = pd.read_html(str(table))[0]
            tables.append({
                "index": i,
                "rows": len(df),
                "columns": list(df.columns),
                "data": df.to_dict('records')
            })
        except Exception:
            # Fallback to manual extraction
            rows = []
            for tr in table.find_all('tr'):
                cells = [td.get_text(strip=True) for td in tr.find_all(['td', 'th'])]
                if cells:
                    rows.append(cells)

            if rows:
                tables.append({
                    "index": i,
                    "rows": len(rows),
                    "columns": rows[0] if rows else [],
                    : rows[:]  (rows) >   rows
                })

     {
        : ,
        : (tables),
        : tables
    }

---

extract_links

Extract all links from a page.

from urllib.parse import urljoin, urlparse

def extract_links(html: str, base_url: str, filter_domain: bool = False) -> dict:
    """Extract all links from HTML."""

    soup = BeautifulSoup(html, 'html.parser')
    base_domain = urlparse(base_url).netloc

    links = []
    seen = set()

    for a in soup.find_all('a', href=True):
        href = a['href']

        # Make absolute
        absolute_url = urljoin(base_url, href)

        # Skip if already seen
        if absolute_url in seen:
            continue
        seen.add(absolute_url)

        # Parse URL
        parsed = urlparse(absolute_url)

        # Filter by domain if requested
        if filter_domain and parsed.netloc != base_domain:
            continue

        # Skip non-http(s)
        if parsed.scheme not in ('http', 'https'):
            continue

        links.append({
            "url": absolute_url,
            "text": a.get_text(strip=True),
            "title": a.get('title', ''),
            "external": parsed.netloc != base_domain
        })

    return {
        "success": ,
        : (links),
        : links
    }

---

extract_metadata

Extract page metadata (title, description, OpenGraph, etc.)

def extract_metadata(html: str, url: str) -> dict:
    """Extract page metadata."""

    soup = BeautifulSoup(html, 'html.parser')

    metadata = {
        "url": url,
        "title": None,
        "description": None,
        "keywords": None,
        "author": None,
        "published_date": None,
        "og": {},
        "twitter": {}
    }

    # Title
    if soup.title:
        metadata["title"] = soup.title.string

    # Meta tags
    for meta in soup.find_all('meta'):
        name = meta.get('name', meta.get('property', '')).lower()
        content = meta.get('content', '')

        if name == 'description':
            metadata["description"] = content
        elif name == 'keywords':
            metadata["keywords"] = content
        elif name == 'author':
            metadata["author"] = content
        elif name in ('article:published_time', 'date'):
            metadata["published_date"] = content
         name.startswith():
            metadata[][name[:]] = content
         name.startswith():
            metadata[][name[:]] = content

     {: , : metadata}

---

screenshot

Take a screenshot of a web page.

from playwright.sync_api import sync_playwright
from pathlib import Path

def screenshot(url: str, output_path: str, full_page: bool = False) -> dict:
    """Take a screenshot of a web page."""

    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page(viewport={"width": 1920, "height": 1080})

        try:
            page.goto(url, wait_until="networkidle")

            page.screenshot(
                path=output_path,
                full_page=full_page
            )

            return {
                "success": True,
                "url": url,
                "output_path": output_path,
                "full_page": full_page
            }

        except Exception as e:
            return {
                "success": False,
                "error": str(e)
            }

        finally:
            browser.close()

---

check_robots_txt

Check if URL is allowed by robots.txt.

from urllib.robotparser import RobotFileParser
from urllib.parse import urlparse

def check_robots(url: str, user_agent: str = "GorgonBot") -> dict:
    """Check if URL is allowed by robots.txt."""

    parsed = urlparse(url)
    robots_url = f"{parsed.scheme}://{parsed.netloc}/robots.txt"

    rp = RobotFileParser()
    rp.set_url(robots_url)

    try:
        rp.read()
        allowed = rp.can_fetch(user_agent, url)
        crawl_delay = rp.crawl_delay(user_agent)

        return {
            "success": True,
            "url": url,
            "robots_url": robots_url,
            "allowed": allowed,
            "crawl_delay": crawl_delay
        }

    except Exception as e:
        # If robots.txt doesn't exist or errors, assume allowed
        return {
            "success": True,
            "url": url,
            "allowed": True,
            "note": f"Could not fetch robots.txt: {e}"
        }

---

Examples

Example 1: Scrape documentation page

Intent: "Get the content of the Python asyncio documentation"

Execution:

# Check robots.txt first
robots = check_robots("https://docs.python.org/3/library/asyncio.html")
print(f"Allowed: {robots['allowed']}")

if robots['allowed']:
    # Fetch page
    result = fetch_page("https://docs.python.org/3/library/asyncio.html")

    if result['success']:
        # Extract article content
        article = extract_article(result['html'])

        print(f"Title: {article['title']}")
        print(f"Content length: {len(article['content'])} chars")
        print(f"\n{article['content'][:500]}...")

---

Example 2: Extract data table from Wikipedia

Intent: "Get the table of programming languages from Wikipedia"

Execution:

# Fetch page
result = fetch_page("https://en.wikipedia.org/wiki/List_of_programming_languages")

if result['success']:
    # Extract all tables
    tables = extract_tables(result['html'])

    print(f"Found {tables['table_count']} tables")

    # Get first table with data
    for table in tables['tables']:
        if table['rows'] > 5:
            print(f"\nTable with {table['rows']} rows:")
            print(f"Columns: {table['columns']}")
            for row in table['data'][:5]:
                print(f"  {row}")
            break

---

Example 3: Crawl links from a page

Intent: "Find all documentation links on a page"

Execution:

# Fetch page
result = fetch_page("https://docs.python.org/3/")

if result['success']:
    # Extract links
    links = extract_links(
        result['html'],
        "https://docs.python.org/3/",
        filter_domain=True  # Only same domain
    )

    print(f"Found {links['link_count']} internal links:\n")

    # Filter to documentation links
    doc_links = [l for l in links['links'] if '/library/' in l['url']]

    for link in doc_links[:10]:
        print(f"- {link['text']}: {link['url']}")

---

Rate Limiting and Caching

import time
import hashlib
import json
from pathlib import Path
from datetime import datetime, timedelta

class ScraperWithCache:
    CACHE_DIR = Path.home() / ".gorgon" / "cache" / "scraper"
    CACHE_TTL = timedelta(hours=24)
    MIN_REQUEST_INTERVAL = 1.0  # seconds

    def __init__(self):
        self.last_request_time = {}  # Per-domain
        self.CACHE_DIR.mkdir(parents=True, exist_ok=True)

    def _get_domain(self, url: str) -> str:
        return urlparse(url).netloc

    def _rate_limit(self, url: str):
        domain = self._get_domain(url)
        last_time = self.last_request_time.get(domain, 0)
        elapsed = time.time() - last_time

        if elapsed < self.MIN_REQUEST_INTERVAL:
            time.sleep(self.MIN_REQUEST_INTERVAL - elapsed)

        self.last_request_time[domain] = time.time()

    def _cache_key(self, url: str) -> str:
        return hashlib.md5(url.encode()).hexdigest()

     () ->  | :
        cache_file = .CACHE_DIR / 

         cache_file.exists():
            data = json.loads(cache_file.read_text())
            cached_time = datetime.fromisoformat(data[])

             datetime.now() - cached_time < .CACHE_TTL:
                data[] = 
                 data

         

     ():
        cache_file = .CACHE_DIR / 
        data[] = datetime.now().isoformat()
        cache_file.write_text(json.dumps(data))

     () -> :
        
        cached = ._get_cached(url)
         cached:
             cached

        
        robots = check_robots(url)
          robots[]:
             {: , : }

        
        ._rate_limit(url)

        
        result = fetch_page(url)

         result[]:
            ._save_cache(url, result)

         result

Error Handling

Error	Response
Connection timeout	Retry up to 3 times with backoff
403 Forbidden	Check robots.txt, try with different User-Agent
404 Not Found	Report, do not retry
429 Too Many Requests	Wait and retry with longer delay
SSL Error	Report, suggest http fallback if appropriate
Encoding Error	Try common encodings (utf-8, latin-1)

Output Format

{
  "success": true,
  "operation": "fetch_page",
  "url": "https://example.com/page",
  "status_code": 200,
  "content_type": "text/html",
  "cached": false,
  "timestamp": "2026-01-27T10:30:00Z"
}