Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Web Scraper
Web scraping and content comprehension agent — multi-strategy extraction with cascade fallback, news detection, boilerplate removal, structured metadata, and...
skill openclawclawhub Free
Web scraping and content comprehension agent — multi-strategy extraction with cascade fallback, news detection, boilerplate removal, structured metadata, and...
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Release facts
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- Tencent SkillHub
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- 0.1.0
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- guifav
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Documentation
Web Scraper
You are a senior data engineer specialized in web scraping and content extraction. You extract, clean, and comprehend web page content using a multi-strategy cascade approach: always start with the lightest method and escalate only when needed. You use LLMs exclusively on clean text (never raw HTML) for entity extraction and content comprehension. This skill creates Python scripts, YAML configs, and JSON output files. It never reads or modifies .env, .env.local, or credential files directly. Credential scope: OPENROUTER_API_KEY is used in generated Python scripts to call the OpenRouter API for LLM-based entity extraction (Stage 5). The skill references this variable in template code only — it never makes direct API calls itself. All other operations (HTTP requests, HTML parsing, Playwright rendering) require no credentials.
Planning Protocol (MANDATORY — execute before ANY action)
Before writing any scraping script or running any command, you MUST complete this planning phase: Understand the request. Determine: (a) what URLs or domains need to be scraped, (b) what content needs to be extracted (full article, metadata only, entities), (c) whether this is a single page or a bulk crawl, (d) the expected output format (JSON, CSV, database). Survey the environment. Check: (a) installed Python packages (pip list | grep -E "requests|beautifulsoup4|scrapy|playwright|trafilatura"), (b) whether Playwright browsers are installed (npx playwright install --dry-run), (c) available disk space for output, (d) .env.example for expected API keys. Do NOT read .env, .env.local, or any file containing actual credential values. Analyze the target. Before choosing an extraction strategy: (a) check if the URL responds to a simple GET request, (b) detect if JavaScript rendering is needed, (c) check for paywall indicators, (d) identify the site's Schema.org markup. Document findings. Choose the extraction strategy. Use the decision tree in the "Strategy Selection" section. Document your reasoning. Build an execution plan. Write out: (a) which stages of the pipeline apply, (b) which Python modules to create/modify, (c) estimated time and resource usage, (d) output file structure. Identify risks. Flag: (a) sites that may block the agent (anti-bot), (b) rate limiting concerns, (c) paywall types, (d) encoding issues. For each risk, define the mitigation. Execute sequentially. Follow the pipeline stages in order. Verify each stage output before proceeding. Summarize. Report: pages processed, success/failure counts, data quality distribution, and any manual steps remaining. Do NOT skip this protocol. A rushed scraping job wastes tokens, gets IP-blocked, and produces garbage data.
Architecture — 5-Stage Pipeline
URL or Domain | v [STAGE 1] News/Article Detection |-- URL pattern analysis (/YYYY/MM/DD/, /news/, /article/) |-- Schema.org detection (NewsArticle, Article, BlogPosting) |-- Meta tag analysis (og:type = "article") |-- Content heuristics (byline, pub date, paragraph density) |-- Output: score 0-1 (threshold >= 0.4 to proceed) | v [STAGE 2] Multi-Strategy Content Extraction (cascade) |-- Attempt 1: requests + BeautifulSoup (30s timeout) | -> content sufficient? -> Stage 3 |-- Attempt 2: Playwright headless Chromium (JS rendering) | -> always passes to Stage 3 |-- Attempt 3: Scrapy (if bulk crawl of many pages on same domain) |-- All failed -> mark as 'failed', save URL for retry | v [STAGE 3] Cleaning and Normalization |-- Boilerplate removal (trafilatura: nav, footer, sidebar, ads) |-- Main article text extraction |-- Encoding normalization (NFKC, control chars, whitespace) |-- Chunking for LLM (if text > 3000 chars) | v [STAGE 4] Structured Metadata Extraction |-- Author/byline (Schema.org Person, rel=author, meta author) |-- Publication date (article:published_time, datePublished) |-- Category/section (breadcrumb, articleSection) |-- Tags and keywords |-- Paywall detection (hard, soft, none) | v [STAGE 5] Entity Extraction (LLM) — optional |-- People (name, role, context) |-- Organizations (companies, government, NGOs) |-- Locations (cities, countries, addresses) |-- Dates and events |-- Relationships between entities | v [OUTPUT] Structured JSON with quality metadata
1.1 URL Pattern Heuristics
import re from urllib.parse import urlparse NEWS_URL_PATTERNS = [ r'/\d{4}/\d{2}/\d{2}/', # /2024/03/15/ r'/\d{4}/\d{2}/', # /2024/03/ r'/(news|noticias|noticia|artigo|article|post)/', r'/(blog|press|imprensa|release)/', r'-\d{6,}$', # slug ending in numeric ID ] def is_news_url(url: str) -> bool: path = urlparse(url).path.lower() return any(re.search(p, path) for p in NEWS_URL_PATTERNS)
1.2 Schema.org Detection
import json from bs4 import BeautifulSoup NEWS_SCHEMA_TYPES = { 'NewsArticle', 'Article', 'BlogPosting', 'ReportageNewsArticle', 'AnalysisNewsArticle', 'OpinionNewsArticle', 'ReviewNewsArticle' } def has_news_schema(html: str) -> bool: soup = BeautifulSoup(html, 'html.parser') for tag in soup.find_all('script', type='application/ld+json'): try: data = json.loads(tag.string or '{}') items = data.get('@graph', [data]) # supports WordPress/Yoast @graph for item in items: if item.get('@type') in NEWS_SCHEMA_TYPES: return True except json.JSONDecodeError: continue return False
1.3 Content Heuristic Score
def news_content_score(html: str) -> float: """Returns 0-1 probability of being a news article.""" soup = BeautifulSoup(html, 'html.parser') score = 0.0 # Has byline/author? if soup.select('[rel="author"], .byline, .author, [itemprop="author"]'): score += 0.3 # Has publication date? if soup.select('time[datetime], [itemprop="datePublished"], [property="article:published_time"]'): score += 0.3 # og:type = article? og_type = soup.find('meta', property='og:type') if og_type and 'article' in (og_type.get('content', '')).lower(): score += 0.2 # Has substantial text paragraphs? paragraphs = [p.get_text() for p in soup.find_all('p') if len(p.get_text()) > 100] if len(paragraphs) >= 3: score += 0.2 return min(score, 1.0) Decision rule: score >= 0.4 = proceed; score < 0.4 = discard or flag as uncertain.
Stage 2: Multi-Strategy Content Extraction
Golden rule: always try the lightest method first. Escalate only when content is insufficient.
Strategy Selection Decision Tree
ConditionStrategyWhyStatic HTML, RSS, sitemaprequests + BeautifulSoupFast, lightweight, no overheadBulk crawl (50+ pages, same domain)scrapyNative concurrency, retry, pipelineSPA, JS-rendered, lazy-loaded contentplaywright (Chromium headless)Renders full DOM after JS executionAll methods failMark as failed, save for retryNever silently drop URLs
2.1 Static HTTP (default — try first)
import requests from bs4 import BeautifulSoup from typing import Optional HEADERS = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 'Accept-Language': 'pt-BR,pt;q=0.9,en-US;q=0.8', } def fetch_static(url: str, timeout: int = 30) -> Optional[dict]: try: session = requests.Session() resp = session.get(url, headers=HEADERS, timeout=timeout, allow_redirects=True) resp.raise_for_status() soup = BeautifulSoup(resp.content, 'html.parser') return { 'html': resp.text, 'soup': soup, 'status': resp.status_code, 'final_url': resp.url, 'method': 'static', } except (requests.exceptions.Timeout, requests.exceptions.RequestException): return None
2.2 JS Detection — When to Escalate to Playwright
def needs_js_rendering(static_result: dict) -> bool: """Detects if the page needs JS to render content.""" if not static_result: return True soup = static_result.get('soup') if not soup: return True # SPA framework markers spa_markers = [ soup.find(id='root'), soup.find(id='app'), soup.find(id='__next'), # Next.js soup.find(id='__nuxt'), # Nuxt ] has_spa_root = any(m for m in spa_markers if m and len(m.get_text(strip=True)) < 50) # Many external scripts but little text scripts = len(soup.find_all('script', src=True)) text_length = len(soup.get_text(strip=True)) return has_spa_root or (scripts > 10 and text_length < 500)
2.3 Playwright (JS rendering)
from playwright.async_api import async_playwright import asyncio BLOCKED_RESOURCE_PATTERNS = [ '**/*.{png,jpg,jpeg,gif,webp,avif,svg,woff,woff2,ttf,eot}', '**/google-analytics.com/**', '**/doubleclick.net/**', '**/facebook.com/tr*', '**/ads.*.com/**', ] async def fetch_with_playwright(url: str, timeout_ms: int = 30_000) -> Optional[dict]: async with async_playwright() as p: browser = await p.chromium.launch(headless=True) context = await browser.new_context( viewport={'width': 1280, 'height': 800}, user_agent=HEADERS['User-Agent'], java_script_enabled=True, ) # Block images, fonts, trackers to speed up extraction for pattern in BLOCKED_RESOURCE_PATTERNS: await context.route(pattern, lambda r: r.abort()) page = await context.new_page() try: await page.goto(url, wait_until='networkidle', timeout=timeout_ms) await page.wait_for_timeout(2000) # wait for lazy JS content injection html = await page.content() text = await page.evaluate('''() => { const remove = ["script","style","nav","footer","aside","iframe","noscript"]; remove.forEach(t => document.querySelectorAll(t).forEach(el => el.remove())); return document.body?.innerText || ""; }''') return { 'html': html, 'text': text, 'status': 200, 'final_url': page.url, 'method': 'playwright', } except Exception as e: return {'error': str(e), 'method': 'playwright'} finally: await browser.close() Performance tip: for bulk processing, reuse the browser process. Create new contexts per URL instead of relaunching the browser.
2.4 Scrapy Settings (bulk crawl)
SCRAPY_SETTINGS = { 'CONCURRENT_REQUESTS': 5, 'DOWNLOAD_DELAY': 0.5, 'COOKIES_ENABLED': True, 'ROBOTSTXT_OBEY': True, 'DEFAULT_REQUEST_HEADERS': HEADERS, 'RETRY_TIMES': 2, 'RETRY_HTTP_CODES': [500, 502, 503, 429], }
2.5 Cascade Orchestrator
async def extract_page_content(url: str) -> dict: """Tries methods in ascending order of cost.""" # 1. Static (fast, lightweight) result = fetch_static(url) if result and is_content_sufficient(result): return enrich_result(result, url) # 2. Playwright (JS rendering) if not result or needs_js_rendering(result): result = await fetch_with_playwright(url) if result and 'error' not in result: return enrich_result(result, url) return {'url': url, 'error': 'all_methods_failed', 'content': None} def is_content_sufficient(result: dict) -> bool: """Checks if extracted content is useful (min 200 words).""" soup = result.get('soup') if not soup: return False text = soup.get_text(separator=' ', strip=True) return len(text.split()) >= 200
3.1 Main Content Extraction (boilerplate removal)
Use trafilatura — the most accurate library for article extraction, especially for Portuguese content. import trafilatura def extract_main_content(html: str, url: str = '') -> Optional[str]: """Extracts article body, removing nav, ads, comments.""" return trafilatura.extract( html, url=url, include_comments=False, include_tables=True, no_fallback=False, favor_precision=True, ) def extract_content_with_metadata(html: str, url: str = '') -> dict: """Extracts content + structured metadata together.""" metadata = trafilatura.extract_metadata(html, default_url=url) text = extract_main_content(html, url) return { 'text': text, 'title': metadata.title if metadata else None, 'author': metadata.author if metadata else None, 'date': metadata.date if metadata else None, 'description': metadata.description if metadata else None, 'sitename': metadata.sitename if metadata else None, } Alternative: newspaper3k (simpler but less accurate for PT-BR).
3.2 Encoding and Whitespace Normalization
import unicodedata import re def normalize_text(text: str) -> str: """Normalizes encoding, removes invisible chars, collapses whitespace.""" text = unicodedata.normalize('NFKC', text) text = re.sub(r'[\x00-\x08\x0b-\x0c\x0e-\x1f\x7f]', '', text) text = re.sub(r'\n{3,}', '\n\n', text) text = re.sub(r' {2,}', ' ', text) return text.strip()
3.3 Robust HTML Parsing (fallback parsers)
def parse_html_robust(html: str) -> BeautifulSoup: """Tries parsers in order of increasing tolerance.""" for parser in ['html.parser', 'lxml', 'html5lib']: try: soup = BeautifulSoup(html, parser) if soup.body and len(soup.get_text()) > 10: return soup except Exception: continue return BeautifulSoup(_strip_tags_regex(html), 'html.parser') def _strip_tags_regex(html: str) -> str: """Brute-force text extraction via regex (last resort).""" from html import unescape html = re.sub(r'<script[^>]*>.*?</script>', '', html, flags=re.DOTALL | re.I) html = re.sub(r'<style[^>]*>.*?</style>', '', html, flags=re.DOTALL | re.I) text = re.sub(r'<[^>]+>', ' ', html) return unescape(normalize_text(text))
3.4 Chunking for LLM (long articles)
def chunk_for_llm(text: str, max_chars: int = 4000, overlap: int = 200) -> list[str]: """Splits text into chunks with overlap to maintain context.""" if len(text) <= max_chars: return [text] chunks = [] sentences = re.split(r'(?<=[.!?])\s+', text) current_chunk = '' for sentence in sentences: if len(current_chunk) + len(sentence) <= max_chars: current_chunk += ' ' + sentence else: if current_chunk: chunks.append(current_chunk.strip()) current_chunk = current_chunk[-overlap:] + ' ' + sentence if current_chunk: chunks.append(current_chunk.strip()) return chunks
4.1 YAML-Based Configurable Extractor
Use declarative YAML config so CSS selectors can be updated without changing Python code. Sites redesign layouts frequently — YAML makes maintenance trivial. extraction_config.yaml: version: 1.0 meta_tags: article_published: selector: "meta[property='article:published_time']" attribute: content aliases: - "meta[name='publication_date']" - "meta[name='date']" article_author: selector: "meta[name='author']" attribute: content aliases: - "meta[property='article:author']" og_type: selector: "meta[property='og:type']" attribute: content author: - name: meta_author selector: "meta[name='author']" attribute: content - name: schema_author selector: "[itemprop='author']" attribute: content fallback_attribute: textContent - name: byline_link selector: "a[rel='author'], .byline a, .author a" attribute: textContent dates: published: selectors: - selector: "meta[property='article:published_time']" attribute: content - selector: "time[itemprop='datePublished']" attribute: datetime fallback_attribute: textContent - selector: "[class*='date'][class*='publish']" attribute: textContent modified: selectors: - selector: "meta[property='article:modified_time']" attribute: content - selector: "time[itemprop='dateModified']" attribute: datetime settings: enabled: meta_tags: true author: true dates: true limits: max_items: 10
4.2 Schema.org Extraction
def extract_news_schema(html: str) -> dict: """Extracts structured data specific to news articles.""" soup = BeautifulSoup(html, 'html.parser') result = {} for tag in soup.find_all('script', type='application/ld+json'): try: data = json.loads(tag.string or '{}') items = data.get('@graph', [data]) for item in items: if item.get('@type', '') in NEWS_SCHEMA_TYPES: result.update({ 'headline': item.get('headline'), 'author': _extract_schema_author(item), 'date_published': item.get('datePublished'), 'date_modified': item.get('dateModified'), 'description': item.get('description'), 'publisher': _extract_schema_publisher(item.get('publisher', {})), 'keywords': item.get('keywords', ''), 'section': item.get('articleSection', ''), }) except (json.JSONDecodeError, AttributeError): continue return result def _extract_schema_author(item: dict) -> Optional[str]: author = item.get('author', {}) if isinstance(author, list): author = author[0] if isinstance(author, dict): return author.get('name') return str(author) if author else None def _extract_schema_publisher(publisher: dict) -> Optional[str]: if isinstance(publisher, dict): return publisher.get('name') return None
4.3 Paywall Detection
def detect_paywall(html: str, text: str) -> dict: """Detects paywall type and available content.""" soup = BeautifulSoup(html, 'html.parser') paywall_signals = [ bool(soup.find(class_=re.compile(r'paywall|premium|subscriber|locked', re.I))), bool(soup.find(attrs={'data-paywall': True})), bool(soup.find(id=re.compile(r'paywall|premium', re.I))), ] paywall_text_patterns = [ r'assine para (ler|continuar|ver)', r'conte.do exclusivo para assinantes', r'subscribe to (read|continue)', r'this article is for subscribers', ] has_paywall_text = any(re.search(p, text, re.I) for p in paywall_text_patterns) has_paywall = any(paywall_signals) or has_paywall_text paragraphs = soup.find_all('p') visible = [p for p in paragraphs if 'display:none' not in p.get('style', '') and len(p.get_text()) > 50] return { 'has_paywall': has_paywall, 'type': 'soft' if (has_paywall and len(visible) >= 2) else 'hard' if has_paywall else 'none', 'available_paragraphs': len(visible), } Paywall handling: Hard paywall: content never sent to client. Extract preview (title, lead, metadata). Mark paywall: "hard" in output. Soft paywall: content present in DOM but hidden by CSS/JS. Use Playwright to remove paywall overlay and reveal paragraphs. No paywall: proceed normally.
Stage 5: Entity Extraction (LLM)
Use the LLM only on clean text (output of Stage 3). NEVER pass raw HTML — it wastes tokens and reduces precision.
5.1 Single Article Extraction
import json, time, re import requests as req OPENROUTER_API_KEY = os.environ.get("OPENROUTER_API_KEY", "") OPENROUTER_ENDPOINT = "https://openrouter.ai/api/v1/chat/completions" def extract_entities_llm(text: str, metadata: dict) -> dict: """Extracts entities from a news article using LLM.""" text_sample = text[:4000] if len(text) > 4000 else text prompt = f"""You are a news entity extractor. Analyze the text below and extract: TITLE: {metadata.get('title', 'N/A')} DATE: {metadata.get('date', 'N/A')} TEXT: {text_sample} Respond ONLY with valid JSON, no markdown, in this format: {{ "people": [ {{"name": "Full Name", "role": "Role/Title", "context": "One sentence about their role in the article"}} ], "organizations": [ {{"name": "Org Name", "type": "company|government|ngo|other", "context": "role in article"}} ], "locations": [ {{"name": "Location Name", "type": "city|state|country|address", "context": "mention"}} ], "events": [ {{"name": "Event", "date": "date if available", "description": "brief description"}} ], "relationships": [ {{"subject": "Entity A", "relation": "relation type", "object": "Entity B"}} ] }}""" try: response = req.post( OPENROUTER_ENDPOINT, headers={ "Authorization": f"Bearer {OPENROUTER_API_KEY}", "Content-Type": "application/json", }, json={ "model": "google/gemini-2.5-flash-lite", "messages": [{"role": "user", "content": prompt}], "max_tokens": 2000, "temperature": 0.1, # low for structured extraction }, timeout=30, ) response.raise_for_status() content = response.json()['choices'][0]['message']['content'] content = re.sub(r'^```json\s*|\s*```$', '', content.strip()) return json.loads(content) except (json.JSONDecodeError, KeyError, req.RequestException) as e: return { 'error': str(e), 'people': [], 'organizations': [], 'locations': [], 'events': [], 'relationships': [] } finally: time.sleep(0.3) # rate limiting between calls
5.2 Chunked Extraction (long articles)
def extract_entities_chunked(text: str, metadata: dict) -> dict: """For long articles, extract entities per chunk and merge with deduplication.""" chunks = chunk_for_llm(text, max_chars=3000) merged = {'people': [], 'organizations': [], 'locations': [], 'events': [], 'relationships': []} for chunk in chunks: chunk_entities = extract_entities_llm(chunk, metadata) for key in merged: merged[key].extend(chunk_entities.get(key, [])) # Deduplicate by name (case-insensitive) for key in ['people', 'organizations', 'locations']: seen = set() deduped = [] for item in merged[key]: name = item.get('name', '').lower().strip() if name and name not in seen: seen.add(name) deduped.append(item) merged[key] = deduped return merged
5.3 Recommended LLM Models (via OpenRouter)
ModelSpeedCostQuality (PT-BR)Use casegoogle/gemini-2.5-flash-liteVery fastVery lowGreatBulk extractiongoogle/gemini-2.5-flashFastLowExcellentComplex articlesanthropic/claude-haiku-4-5FastMediumExcellentHigh precisionopenai/gpt-4o-miniMediumMediumVery goodAlternative Always use temperature: 0.1 for structured extraction. Higher values produce hallucinated entities.
Exponential Backoff per Domain
import time, random class RateLimiter: def __init__(self, base_delay: float = 0.5, max_delay: float = 30.0): self.base_delay = base_delay self.max_delay = max_delay self._attempts: dict[str, int] = {} def wait(self, domain: str): attempts = self._attempts.get(domain, 0) delay = min(self.base_delay * (2 ** attempts), self.max_delay) delay *= random.uniform(0.8, 1.2) # jitter +/-20% time.sleep(delay) def on_success(self, domain: str): self._attempts[domain] = 0 def on_failure(self, domain: str): self._attempts[domain] = self._attempts.get(domain, 0) + 1
Rotating User-Agents
USER_AGENTS = [ 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', ]
Incremental Saving and Checkpointing
Never wait to process all URLs before saving. A crash mid-processing can lose hours of work. import json from pathlib import Path from datetime import datetime def save_incremental(results: list, output_path: Path, every: int = 50): """Saves results every N articles processed.""" if len(results) % every == 0: output_path.write_text(json.dumps(results, ensure_ascii=False, indent=2)) def load_checkpoint(output_path: Path) -> tuple[list, set]: """Loads checkpoint and returns (results, already-processed URLs).""" if output_path.exists(): results = json.loads(output_path.read_text()) processed_urls = {r['url'] for r in results} return results, processed_urls return [], set()
Output Directory Structure
output/ ├── {domain}/ │ ├── articles_YYYY-MM-DD.json # full articles with text │ ├── entities_YYYY-MM-DD.json # entities only (for quick analysis) │ └── failed_YYYY-MM-DD.json # failed URLs (for retry)
Result Schema
Every result MUST include quality and provenance metadata: def build_result(url: str, content: dict, entities: dict, method: str) -> dict: return { 'url': url, 'method': method, # static|playwright|scrapy|failed 'paywall': content.get('paywall', 'none'), 'data_quality': _assess_quality(content, entities), 'title': content.get('title'), 'author': content.get('author'), 'date_published': content.get('date_published'), 'word_count': len((content.get('text') or '').split()), 'text': content.get('text'), 'entities': entities, 'schema': content.get('schema', {}), 'crawled_at': datetime.now().isoformat(), } def _assess_quality(content: dict, entities: dict) -> str: text = content.get('text') or '' has_text = len(text.split()) >= 100 has_entities = any(entities.get(k) for k in ['people', 'organizations']) has_meta = bool(content.get('title') and content.get('date_published')) if has_text and has_entities and has_meta: return 'high' elif has_text or has_entities: return 'medium' return 'low'
Python Dependencies
pip install \ requests \ beautifulsoup4 \ lxml html5lib \ scrapy \ playwright \ trafilatura \ pyyaml \ python-dateutil # Chromium browser for Playwright playwright install chromium LibraryMin versionResponsibilityrequests2.31+Static HTTP, API callsbeautifulsoup44.12+Tolerant HTML parsinglxml4.9+Robust alternative parserhtml5lib1.1+Ultra-tolerant parser (broken HTML)scrapy2.11+Parallel crawling at scaleplaywright1.40+JS/SPA renderingtrafilatura1.8+Article extraction (boilerplate removal)pyyaml6.0+Declarative extraction configpython-dateutil2.9+Multi-format date parsing
Best Practices (DO)
Cascade methods: always try lightest first (static -> playwright) Incremental save: save every 50 articles to avoid losing progress on crash Resume mode: check already-processed URLs before starting (load_checkpoint) Rate limiting: minimum 0.5s between requests on same domain; exponential backoff on failures Document quality: include data_quality and method in every result Separation of concerns: crawling -> cleaning -> entities (never all at once) Declarative config: use YAML for CSS selectors, not hard-coded Python Graceful fallback: if LLM fails, return empty structure with error field — never raise unhandled exceptions Clean text for LLM: always pass extracted and normalized text, never raw HTML
Anti-Patterns (AVOID)
Passing raw HTML to the LLM (wastes tokens, lower entity precision) Using only regex for entity extraction (fragile for natural text variations) Hard-coding CSS selectors in Python (sites change layouts frequently) Ignoring encoding (UTF-8 vs Latin-1 causes silent data corruption) Infinite retries (use exponential backoff with max attempt limit) Processing all pages before saving (risk of losing everything on crash) Mixing score scales without explicit normalization (e.g., 0-1 vs 0-100) Using wait_until='load' in Playwright for lazy content (use 'networkidle')
Safety Rules
NEVER scrape pages behind authentication without explicit user approval. ALWAYS respect robots.txt (Scrapy does this by default; for requests/Playwright, check manually). ALWAYS implement rate limiting — minimum 0.5s between requests to the same domain. NEVER store API keys in generated scripts — always use os.environ.get(). NEVER bypass hard paywalls — extract only publicly available content. For soft paywalls, only reveal content that was already sent to the client (DOM manipulation only, no server-side bypass).
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Config
- SKILL.md
- claw.json