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https://github.com/K-Dense-AI/claude-scientific-skills.git
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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
@@ -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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