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https://github.com/K-Dense-AI/claude-scientific-skills.git
synced 2026-01-26 16:58:56 +08:00
Enhance citation management and literature review guidelines
- Updated SKILL.md in citation management to include best practices for identifying seminal and high-impact papers, emphasizing citation count thresholds, venue quality tiers, and author reputation indicators. - Expanded literature review SKILL.md to prioritize high-impact papers, detailing citation metrics, journal tiers, and author reputation assessment. - Added comprehensive evaluation strategies for paper impact and quality in literature_search_strategies.md, including citation count significance and journal impact factor guidance. - Improved research lookup scripts to prioritize results based on citation count, venue prestige, and author reputation, enhancing the quality of research outputs.
This commit is contained in:
Vinayak Agarwal
@@ -21,6 +21,7 @@ def format_response(result: Dict) -> str:
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response = result["response"]
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citations = result["citations"]
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sources = result.get("sources", [])
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# Format the output for Claude Code
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output = f"""🔍 **Research Results**
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@@ -28,6 +29,7 @@ def format_response(result: Dict) -> str:
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**Query:** {result['query']}
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**Model:** {result['model']}
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**Timestamp:** {result['timestamp']}
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**Note:** Results prioritized by citation count, venue prestige, and author reputation
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---
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@@ -35,15 +37,48 @@ def format_response(result: Dict) -> str:
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"""
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# Display API-provided sources with venue/citation info
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if sources:
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output += f"\n📚 **Sources ({len(sources)}):**\n"
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output += "_Prioritized by venue quality and citation impact_\n\n"
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for i, source in enumerate(sources, 1):
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title = source.get("title", "Untitled")
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url = source.get("url", "")
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date = source.get("date", "")
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snippet = source.get("snippet", "")
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# Format source entry with available metadata
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date_str = f" ({date})" if date else ""
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output += f"{i}. **{title}**{date_str}\n"
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# Add venue indicator if detectable from URL
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venue_indicator = _detect_venue_tier(url)
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if venue_indicator:
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output += f" 📊 Venue: {venue_indicator}\n"
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if url:
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output += f" 🔗 {url}\n"
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if snippet:
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output += f" _{snippet[:150]}{'...' if len(snippet) > 150 else ''}_\n"
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output += "\n"
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# Display extracted citations (DOIs, etc.)
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if citations:
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output += f"\n**Extracted Citations ({len(citations)}):**\n"
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for i, citation in enumerate(citations, 1):
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if citation.get("doi"):
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output += f"{i}. DOI: {citation['doi']}\n"
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elif citation.get("authors") and citation.get("year"):
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output += f"{i}. {citation['authors']} ({citation['year']})\n"
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else:
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output += f"{i}. {citation}\n"
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doi_citations = [c for c in citations if c.get("type") == "doi"]
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url_citations = [c for c in citations if c.get("type") == "url"]
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if doi_citations:
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output += f"\n🔗 **DOI References ({len(doi_citations)}):**\n"
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for i, citation in enumerate(doi_citations, 1):
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output += f"{i}. DOI: {citation.get('doi', '')} → {citation.get('url', '')}\n"
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if url_citations:
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output += f"\n🌐 **Additional URLs ({len(url_citations)}):**\n"
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for i, citation in enumerate(url_citations, 1):
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url = citation.get('url', '')
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venue = _detect_venue_tier(url)
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venue_str = f" [{venue}]" if venue else ""
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output += f"{i}. {url}{venue_str}\n"
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if result.get("usage"):
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usage = result["usage"]
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@@ -52,6 +87,65 @@ def format_response(result: Dict) -> str:
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return output
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def _detect_venue_tier(url: str) -> Optional[str]:
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"""Detect venue tier from URL to indicate source quality."""
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if not url:
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return None
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url_lower = url.lower()
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# Tier 1 - Premier venues
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tier1_indicators = {
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"nature.com": "Nature (Tier 1)",
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"science.org": "Science (Tier 1)",
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"cell.com": "Cell Press (Tier 1)",
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"nejm.org": "NEJM (Tier 1)",
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"thelancet.com": "Lancet (Tier 1)",
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"jamanetwork.com": "JAMA (Tier 1)",
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"pnas.org": "PNAS (Tier 1)",
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}
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# Tier 2 - High-impact specialized
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tier2_indicators = {
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"neurips.cc": "NeurIPS (Tier 2 - Top ML)",
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"icml.cc": "ICML (Tier 2 - Top ML)",
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"openreview.net": "Top ML Conference (Tier 2)",
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"aacrjournals.org": "AACR Journals (Tier 2)",
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"ahajournals.org": "AHA Journals (Tier 2)",
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"bloodjournal.org": "Blood (Tier 2)",
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"jci.org": "JCI (Tier 2)",
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}
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# Tier 3 - Respected academic sources
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tier3_indicators = {
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"springer.com": "Springer",
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"wiley.com": "Wiley",
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"elsevier.com": "Elsevier",
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"oup.com": "Oxford University Press",
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"arxiv.org": "arXiv (Preprint)",
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"biorxiv.org": "bioRxiv (Preprint)",
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"medrxiv.org": "medRxiv (Preprint)",
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"pubmed": "PubMed",
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"ncbi.nlm.nih.gov": "NCBI/PubMed",
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"ieee.org": "IEEE",
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"acm.org": "ACM",
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}
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for domain, label in tier1_indicators.items():
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if domain in url_lower:
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return label
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for domain, label in tier2_indicators.items():
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if domain in url_lower:
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return label
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for domain, label in tier3_indicators.items():
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if domain in url_lower:
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return label
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return None
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def main():
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"""Main entry point for Claude Code tool."""
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# Check for API key
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@@ -123,13 +123,34 @@ IMPORTANT INSTRUCTIONS:
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6. Include key findings, methodologies, and implications when relevant
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7. Note any controversies, limitations, or conflicting evidence
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PAPER QUALITY AND POPULARITY PRIORITIZATION (CRITICAL):
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8. ALWAYS prioritize HIGHLY-CITED papers over obscure publications:
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- Recent papers (0-3 years): prefer 20+ citations, highlight 100+ as highly influential
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- Mid-age papers (3-7 years): prefer 100+ citations, highlight 500+ as landmark
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- Older papers (7+ years): prefer 500+ citations, highlight 1000+ as foundational
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9. ALWAYS prioritize papers from TOP-TIER VENUES:
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- Tier 1 (highest priority): Nature, Science, Cell, NEJM, Lancet, JAMA, PNAS, Nature Medicine, Nature Biotechnology
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- Tier 2 (high priority): High-impact specialized journals (IF>10), top conferences (NeurIPS, ICML, ICLR for AI/ML)
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- Tier 3: Respected specialized journals (IF 5-10)
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- Only cite lower-tier venues if directly relevant AND no better source exists
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10. PREFER papers from ESTABLISHED, REPUTABLE AUTHORS:
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- Senior researchers with high h-index and multiple high-impact publications
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- Leading research groups at recognized institutions
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- Authors with recognized expertise (awards, editorial positions)
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11. For EACH citation, include when available:
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- Approximate citation count (e.g., "cited 500+ times")
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- Journal/venue tier indicator
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- Notable author credentials if relevant
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12. PRIORITIZE papers that DIRECTLY address the research question over tangentially related work
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RESPONSE FORMAT:
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- Start with a brief summary (2-3 sentences)
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- Present key findings and studies in organized sections
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- Rank papers by impact: most influential/cited first
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- End with future directions or research gaps if applicable
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- Include 5-8 high-quality citations at the end
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- Include 5-8 high-quality citations, emphasizing Tier-1 venues and highly-cited papers
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Remember: This is for academic research purposes. Prioritize accuracy, completeness, and proper attribution."""
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Remember: Quality over quantity. Prioritize influential, highly-cited papers from prestigious venues and established researchers."""
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def lookup(self, query: str) -> Dict[str, Any]:
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"""Perform a research lookup for the given query."""
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@@ -145,7 +166,22 @@ Remember: This is for academic research purposes. Prioritize accuracy, completen
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messages = [
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{
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"role": "system",
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"content": "You are an academic research assistant. Focus exclusively on scholarly sources: peer-reviewed journals, academic papers, research institutions, and reputable scientific publications. Prioritize recent academic literature (2020-2026) and provide complete citations with DOIs. Use academic/scholarly search mode."
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"content": """You are an academic research assistant specializing in finding HIGH-IMPACT, INFLUENTIAL research.
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QUALITY PRIORITIZATION (CRITICAL):
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- ALWAYS prefer highly-cited papers over obscure publications
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- ALWAYS prioritize Tier-1 venues: Nature, Science, Cell, NEJM, Lancet, JAMA, PNAS, and their family journals
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- ALWAYS prefer papers from established researchers with strong publication records
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- Include citation counts when known (e.g., "cited 500+ times")
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- Quality matters more than quantity - 5 excellent papers beats 10 mediocre ones
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VENUE HIERARCHY:
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1. Nature/Science/Cell family, NEJM, Lancet, JAMA (highest priority)
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2. High-impact specialized journals (IF>10), top ML conferences (NeurIPS, ICML, ICLR)
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3. Respected field-specific journals (IF 5-10)
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4. Other peer-reviewed sources (only if no better option exists)
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Focus exclusively on scholarly sources: peer-reviewed journals, academic papers, research institutions. Prioritize recent academic literature (2020-2026) and provide complete citations with DOIs. Always indicate paper impact through citation counts and venue prestige."""
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},
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{"role": "user", "content": research_prompt}
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]
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@@ -316,6 +352,7 @@ Remember: This is for academic research purposes. Prioritize accuracy, completen
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def main():
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"""Command-line interface for testing the research lookup tool."""
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import argparse
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import sys
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parser = argparse.ArgumentParser(description="Research Information Lookup Tool")
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parser.add_argument("query", nargs="?", help="Research query to look up")
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@@ -323,82 +360,113 @@ def main():
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parser.add_argument("--batch", nargs="+", help="Run multiple queries")
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parser.add_argument("--force-model", choices=["pro", "reasoning"],
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help="Force specific model: 'pro' for fast lookup, 'reasoning' for deep analysis")
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parser.add_argument("-o", "--output", help="Write output to file instead of stdout")
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parser.add_argument("--json", action="store_true", help="Output results as JSON")
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args = parser.parse_args()
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# Set up output destination
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output_file = None
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if args.output:
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output_file = open(args.output, 'w', encoding='utf-8')
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def write_output(text):
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"""Write to file or stdout."""
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if output_file:
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output_file.write(text + '\n')
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else:
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print(text)
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# Check for API key
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if not os.getenv("OPENROUTER_API_KEY"):
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print("Error: OPENROUTER_API_KEY environment variable not set")
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print("Please set it in your .env file or export it:")
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print(" export OPENROUTER_API_KEY='your_openrouter_api_key'")
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print("Error: OPENROUTER_API_KEY environment variable not set", file=sys.stderr)
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print("Please set it in your .env file or export it:", file=sys.stderr)
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print(" export OPENROUTER_API_KEY='your_openrouter_api_key'", file=sys.stderr)
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if output_file:
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output_file.close()
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return 1
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try:
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research = ResearchLookup(force_model=args.force_model)
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if args.model_info:
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print("Available models from OpenRouter:")
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write_output("Available models from OpenRouter:")
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models = research.get_model_info()
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if "data" in models:
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for model in models["data"]:
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if "perplexity" in model["id"].lower():
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print(f" - {model['id']}: {model.get('name', 'N/A')}")
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write_output(f" - {model['id']}: {model.get('name', 'N/A')}")
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if output_file:
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output_file.close()
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return 0
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if not args.query and not args.batch:
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print("Error: No query provided. Use --model-info to see available models.")
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print("Error: No query provided. Use --model-info to see available models.", file=sys.stderr)
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if output_file:
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output_file.close()
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return 1
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if args.batch:
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print(f"Running batch research for {len(args.batch)} queries...")
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print(f"Running batch research for {len(args.batch)} queries...", file=sys.stderr)
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results = research.batch_lookup(args.batch)
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else:
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print(f"Researching: {args.query}")
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print(f"Researching: {args.query}", file=sys.stderr)
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results = [research.lookup(args.query)]
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# Display results
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# Output as JSON if requested
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if args.json:
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write_output(json.dumps(results, indent=2, ensure_ascii=False))
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if output_file:
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output_file.close()
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return 0
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# Display results in human-readable format
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for i, result in enumerate(results):
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if result["success"]:
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print(f"\n{'='*80}")
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print(f"Query {i+1}: {result['query']}")
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print(f"Timestamp: {result['timestamp']}")
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print(f"Model: {result['model']}")
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print(f"{'='*80}")
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print(result["response"])
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write_output(f"\n{'='*80}")
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write_output(f"Query {i+1}: {result['query']}")
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write_output(f"Timestamp: {result['timestamp']}")
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write_output(f"Model: {result['model']}")
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write_output(f"{'='*80}")
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write_output(result["response"])
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# Display API-provided sources first (most reliable)
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sources = result.get("sources", [])
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if sources:
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print(f"\n📚 Sources ({len(sources)}):")
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write_output(f"\n📚 Sources ({len(sources)}):")
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for j, source in enumerate(sources):
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title = source.get("title", "Untitled")
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url = source.get("url", "")
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date = source.get("date", "")
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date_str = f" ({date})" if date else ""
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print(f" [{j+1}] {title}{date_str}")
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write_output(f" [{j+1}] {title}{date_str}")
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if url:
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print(f" {url}")
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write_output(f" {url}")
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# Display additional text-extracted citations
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citations = result.get("citations", [])
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text_citations = [c for c in citations if c.get("type") in ("doi", "url")]
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if text_citations:
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print(f"\n🔗 Additional References ({len(text_citations)}):")
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write_output(f"\n🔗 Additional References ({len(text_citations)}):")
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for j, citation in enumerate(text_citations):
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if citation.get("type") == "doi":
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print(f" [{j+1}] DOI: {citation.get('doi', '')} - {citation.get('url', '')}")
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write_output(f" [{j+1}] DOI: {citation.get('doi', '')} - {citation.get('url', '')}")
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elif citation.get("type") == "url":
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print(f" [{j+1}] {citation.get('url', '')}")
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write_output(f" [{j+1}] {citation.get('url', '')}")
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if result.get("usage"):
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print(f"\nUsage: {result['usage']}")
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write_output(f"\nUsage: {result['usage']}")
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else:
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print(f"\nError in query {i+1}: {result['error']}")
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write_output(f"\nError in query {i+1}: {result['error']}")
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if output_file:
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output_file.close()
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return 0
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except Exception as e:
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print(f"Error: {str(e)}")
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print(f"Error: {str(e)}", file=sys.stderr)
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if output_file:
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output_file.close()
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return 1
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