Agent skill · research science · k-dense-ai
primekg
Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.
Why this skill is useful
Provides specific Python scripts for querying the PrimeKG knowledge graph that the AI wouldn't generate on its own.
What it needs
Requires pandas installed locally. About 2k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
PrimeKG Knowledge Graph Skill Overview PrimeKG is a precision medicine knowledge graph that integrates over 20 primary databases and high-quality scientific literature into a single resource. It contains over 100,000 nodes and 4 million edges across 29 relationship types, including drug-target, disease-gene, and phenotype-disease associations. Key capabilities: Search for nodes (genes, proteins, drugs, diseases, phenotypes) Retrieve direct neighbors (associated entities and clinical evidence) Analyze local disease context (related genes, drugs, phenotypes) Identify drug-disease paths (potential repurposing opportunities) Data access: Programmatic access via queryprimekg.py. Data is stored at C:\Users\eamon\Documents\Data\PrimeKG\kg.csv. When to Use This Skill This skill should be used when: Knowledge-based drug discovery: Identifying targets and mechanisms for diseases. Drug repurposing: Finding existing drugs that might have evidence for new indications. Phenotype analysis: Understanding how symptoms/phenotypes relate to diseases and genes. Multiscale biology: Bridging the gap between molecular targets (genes) and clinical outcomes (diseases). Network pharmacology: Investigating the broader network effects of drug-target interactions. Core Workflow 1. Search for Entities Find identifiers for genes, drugs, or diseases. 2. Get Neighbors (Direct Associations) Retrieve all connected nodes and relationship types. 3. Analyze Disease Context A high-level function to summarize associations for a disease. Relationship Types in PrimeKG The graph contains several key relationship types including: proteinprotein: Physical PPIs drugprotein: Drug target/mechanism associations diseasegene: Genetic associations drugdisease: Indications and contraindications diseasephenotype: Clinical signs and symptoms gwas: Genome-wide association studies evidence Best Practices 1. Use specific IDs: When using getneighbors, ensure you have the correct ID from searchnodes. 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/primekg