feat: sync full workspace including web modules, docs, and configurations to Gitea
Optimized the root .gitignore to exclude virtual environments, node modules, and temp folders to ensure clean and lightweight version tracking. Co-authored-by: Cursor <cursoragent@cursor.com>
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王冕
292
axhub-make/skills/third-party/deep-research/scripts/source_evaluator.py
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axhub-make/skills/third-party/deep-research/scripts/source_evaluator.py
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#!/usr/bin/env python3
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"""
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Source Credibility Evaluator
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Assesses source quality, credibility, and potential biases
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"""
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from dataclasses import dataclass
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from typing import List, Dict, Optional
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from urllib.parse import urlparse
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from datetime import datetime, timedelta
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import re
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@dataclass
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class CredibilityScore:
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"""Represents source credibility assessment"""
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overall_score: float # 0-100
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domain_authority: float # 0-100
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recency: float # 0-100
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expertise: float # 0-100
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bias_score: float # 0-100 (higher = more neutral)
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factors: Dict[str, str]
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recommendation: str # "high_trust", "moderate_trust", "low_trust", "verify"
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class SourceEvaluator:
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"""Evaluates source credibility and quality"""
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# Domain reputation tiers
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HIGH_AUTHORITY_DOMAINS = {
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# Academic & Research
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'arxiv.org', 'nature.com', 'science.org', 'cell.com', 'nejm.org',
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'thelancet.com', 'springer.com', 'sciencedirect.com', 'plos.org',
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'ieee.org', 'acm.org', 'pubmed.ncbi.nlm.nih.gov',
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# Government & International Organizations
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'nih.gov', 'cdc.gov', 'who.int', 'fda.gov', 'nasa.gov',
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'gov.uk', 'europa.eu', 'un.org',
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# Established Tech Documentation
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'docs.python.org', 'developer.mozilla.org', 'docs.microsoft.com',
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'cloud.google.com', 'aws.amazon.com', 'kubernetes.io',
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# Reputable News (Fact-check verified)
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'reuters.com', 'apnews.com', 'bbc.com', 'economist.com',
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'nature.com/news', 'scientificamerican.com'
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}
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MODERATE_AUTHORITY_DOMAINS = {
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# Tech News & Analysis
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'techcrunch.com', 'theverge.com', 'arstechnica.com', 'wired.com',
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'zdnet.com', 'cnet.com',
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# Industry Publications
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'forbes.com', 'bloomberg.com', 'wsj.com', 'ft.com',
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# Educational
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'wikipedia.org', 'britannica.com', 'khanacademy.org',
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# Tech Blogs (established)
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'medium.com', 'dev.to', 'stackoverflow.com', 'github.com'
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}
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LOW_AUTHORITY_INDICATORS = [
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'blogspot.com', 'wordpress.com', 'wix.com', 'substack.com'
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]
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def __init__(self):
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pass
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def evaluate_source(
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self,
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url: str,
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title: str,
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content: Optional[str] = None,
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publication_date: Optional[str] = None,
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author: Optional[str] = None
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) -> CredibilityScore:
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"""Evaluate source credibility"""
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domain = self._extract_domain(url)
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# Calculate component scores
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domain_score = self._evaluate_domain_authority(domain)
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recency_score = self._evaluate_recency(publication_date)
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expertise_score = self._evaluate_expertise(domain, title, author)
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bias_score = self._evaluate_bias(domain, title, content)
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# Calculate overall score (weighted average)
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overall = (
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domain_score * 0.35 +
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recency_score * 0.20 +
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expertise_score * 0.25 +
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bias_score * 0.20
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)
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# Determine factors
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factors = self._identify_factors(
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domain, domain_score, recency_score, expertise_score, bias_score
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)
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# Generate recommendation
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recommendation = self._generate_recommendation(overall)
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return CredibilityScore(
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overall_score=round(overall, 2),
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domain_authority=round(domain_score, 2),
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recency=round(recency_score, 2),
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expertise=round(expertise_score, 2),
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bias_score=round(bias_score, 2),
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factors=factors,
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recommendation=recommendation
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)
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def _extract_domain(self, url: str) -> str:
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"""Extract domain from URL"""
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parsed = urlparse(url)
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domain = parsed.netloc.lower()
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# Remove www prefix
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domain = domain.replace('www.', '')
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return domain
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def _evaluate_domain_authority(self, domain: str) -> float:
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"""Evaluate domain authority (0-100)"""
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if domain in self.HIGH_AUTHORITY_DOMAINS:
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return 90.0
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elif domain in self.MODERATE_AUTHORITY_DOMAINS:
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return 70.0
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elif any(indicator in domain for indicator in self.LOW_AUTHORITY_INDICATORS):
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return 40.0
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else:
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# Unknown domain - moderate skepticism
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return 55.0
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def _evaluate_recency(self, publication_date: Optional[str]) -> float:
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"""Evaluate information recency (0-100)"""
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if not publication_date:
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return 50.0 # Unknown date
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try:
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pub_date = datetime.fromisoformat(publication_date.replace('Z', '+00:00'))
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age = datetime.now() - pub_date
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# Recency scoring
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if age < timedelta(days=90): # < 3 months
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return 100.0
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elif age < timedelta(days=365): # < 1 year
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return 85.0
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elif age < timedelta(days=730): # < 2 years
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return 70.0
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elif age < timedelta(days=1825): # < 5 years
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return 50.0
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else:
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return 30.0
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except Exception:
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return 50.0
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def _evaluate_expertise(
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self,
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domain: str,
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title: str,
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author: Optional[str]
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) -> float:
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"""Evaluate source expertise (0-100)"""
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score = 50.0
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# Academic/research domains get high expertise
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if any(d in domain for d in ['arxiv', 'nature', 'science', 'ieee', 'acm']):
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score += 30
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# Government/official sources
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if '.gov' in domain or 'who.int' in domain:
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score += 25
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# Technical documentation
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if 'docs.' in domain or 'documentation' in title.lower():
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score += 20
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# Author credentials (if available)
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if author:
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if any(title in author.lower() for title in ['dr.', 'phd', 'professor']):
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score += 15
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return min(score, 100.0)
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def _evaluate_bias(
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self,
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domain: str,
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title: str,
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content: Optional[str]
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) -> float:
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"""Evaluate potential bias (0-100, higher = more neutral)"""
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score = 70.0 # Start neutral
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# Check for sensationalism in title
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sensational_indicators = [
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'!', 'shocking', 'unbelievable', 'you won\'t believe',
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'secret', 'they don\'t want you to know'
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]
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title_lower = title.lower()
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if any(indicator in title_lower for indicator in sensational_indicators):
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score -= 20
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# Academic sources are typically less biased
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if any(d in domain for d in ['arxiv', 'nature', 'science', 'ieee']):
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score += 20
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# Check for balance in content (if available)
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if content:
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# Look for balanced language
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balanced_indicators = ['however', 'although', 'on the other hand', 'critics argue']
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if any(indicator in content.lower() for indicator in balanced_indicators):
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score += 10
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return min(max(score, 0), 100.0)
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def _identify_factors(
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self,
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domain: str,
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domain_score: float,
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recency_score: float,
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expertise_score: float,
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bias_score: float
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) -> Dict[str, str]:
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"""Identify key credibility factors"""
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factors = {}
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if domain_score >= 85:
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factors['domain'] = "High authority domain"
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elif domain_score <= 45:
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factors['domain'] = "Low authority domain - verify claims"
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if recency_score >= 85:
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factors['recency'] = "Recent information"
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elif recency_score <= 40:
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factors['recency'] = "Outdated information - verify currency"
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if expertise_score >= 80:
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factors['expertise'] = "Expert source"
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elif expertise_score <= 45:
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factors['expertise'] = "Limited expertise indicators"
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if bias_score >= 80:
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factors['bias'] = "Balanced perspective"
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elif bias_score <= 50:
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factors['bias'] = "Potential bias detected"
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return factors
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def _generate_recommendation(self, overall_score: float) -> str:
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"""Generate trust recommendation"""
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if overall_score >= 80:
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return "high_trust"
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elif overall_score >= 60:
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return "moderate_trust"
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elif overall_score >= 40:
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return "low_trust"
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else:
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return "verify"
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# Example usage
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if __name__ == '__main__':
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evaluator = SourceEvaluator()
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# Test sources
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test_sources = [
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{
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'url': 'https://www.nature.com/articles/s41586-2025-12345',
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'title': 'Breakthrough in Quantum Computing',
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'publication_date': '2025-10-15'
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},
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{
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'url': 'https://someblog.wordpress.com/shocking-discovery',
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'title': 'SHOCKING! You Won\'t Believe This Discovery!',
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'publication_date': '2020-01-01'
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},
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{
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'url': 'https://docs.python.org/3/library/asyncio.html',
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'title': 'asyncio — Asynchronous I/O',
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'publication_date': '2025-11-01'
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}
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]
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for source in test_sources:
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score = evaluator.evaluate_source(**source)
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print(f"\nSource: {source['title']}")
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print(f"URL: {source['url']}")
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print(f"Overall Score: {score.overall_score}/100")
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print(f"Recommendation: {score.recommendation}")
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print(f"Factors: {score.factors}")
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