citation-chasing-mapping
Use when identifying seminal papers in a research field, mapping research lineage and intellectual heritage, discovering related work through reference tracking, or finding potential collaborators through co-citation analysis. Maps citation networks to trace research evolution, identify influential papers, and discover hidden connections in scientific literature. Supports systematic reviews, bibliometric analysis, and research planning through comprehensive citation tracking.
npx skills add majiayu000/claude-skill-registry --skill citation-chasing-mapping --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Scientific Citation Network and Knowledge Mapper ## When to Use This Skill - identifying seminal papers in a research field - mapping research lineage and intellectual heritage - discovering related work through reference tracking - finding potential collaborators through co-citation analysis - tracking citation patterns to identify research trends - building literature reviews with comprehensive coverage ## Quick Start ```python from scripts.main import CitationChasingMapping # Initialize the tool tool = CitationChasingMapping() from scripts.citation_mapper import CitationNetworkMapper mapper = CitationNetworkMapper(data_source="PubMed") # Build citation network from seed paper network = mapper.build_network( seed_paper={ "pmid": "12345678", "title": "Breakthrough Discovery in Immunotherapy" }, backward_depth=2, # references of references forward_depth=2, # citing papers of citing papers max_papers=500 ) # Identify seminal papers seminal_papers = mapper.identify_seminal_works( network=network, min_citations=100, centrality_threshold=0.8 ) print(f"Found {len(seminal_papers)} highly influential papers:") for paper in seminal_papers[:5]: print(f" - {paper.title} (cited {paper.citat
- When to Use This Skill
- Quick Start
- Core Capabilities
- 1. Build Comprehensive Citation Networks
- 2. Identify Seminal Works
- 3. Discover Research Clusters
- 4. Generate Interactive Visualizations
- Command Line Usage
- Best Practices
- Quality Checklist
- References
python scripts/main.py --seed-pmid 12345678 --depth 2 --max-papers 500 --output network.json --visualize
What does the citation-chasing-mapping skill do?
Use when identifying seminal papers in a research field, mapping research lineage and intellectual heritage, discovering related work through reference tracking, or finding potential collaborators through co-citation analysis. Maps citation networks to trace research evolution, identify influential papers, and discover hidden connections in scientific literature. Supports systematic reviews, bibliometric analysis, and research planning through comprehensive citation tracking.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill citation-chasing-mapping --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.