scientific-skills/Evidence Insights/crossref-database/SKILL.md
Access CrossRef metadata for scholarly works; use when you need to resolve a DOI or search CrossRef to retrieve bibliographic details, citation/reference counts, or funder information for research and citation management.
npx skillsauth add aipoch/medical-research-skills crossref-databaseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Run this minimal command first to verify the supported execution path:
python scripts/query_crossref.py --help
reference-count and is-referenced-by-count (cited-by).habanero (recommended: >=1.2.0)Install:
pip install "habanero>=1.2.0"
python scripts/query_crossref.py --doi "10.1371/journal.pone.0029797"
python scripts/query_crossref.py --query "climate change" --limit 5
habanero client.--doi: A DOI string to resolve to a single work record.--query: A free-text query used to search works (e.g., title/author keywords).--limit: Maximum number of results to return for searches.title, author, issued/publication date, container-title (journal/venue), publisher.DOI, URL.reference-count, is-referenced-by-count.funder entries when available.crossref_database_result.md unless the skill documentation defines a better convention.Run this minimal verification path before full execution when possible:
python scripts/query_crossref.py --help
Expected output format:
Result file: crossref_database_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any
tools
Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
development
Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
tools
Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.
testing
Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.