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primekg

Query PrimeKG CSV data to retrieve biomedical entities and relationships.

74|5|Updated Dec 10, 2025
Search Tags:#knowledge graph#drug discovery#biomedical research#precision medicine#disease analysis#primekg
One-click install
npx skills add https://github.com/dralkh/seerai --skill primekg-dralkh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: primekg
Source: https://github.com/dralkh/seerai/tree/main/skills/primekg
Command: npx skills add https://github.com/dralkh/seerai --skill primekg-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, and includes scripts (resource) components.

What problem does it solve?

PrimeKG helps researchers quickly explore multiscale biomedical relationships without manually inspecting a massive knowledge graph. It turns a large CSV-based graph into searchable evidence for genes, drugs, diseases, phenotypes, and related biological associations.

Core Features & Use Cases

  • Node search: Find genes, drugs, diseases, and phenotypes by name and optional type.
  • Neighborhood exploration: Retrieve direct neighbors and relationship metadata for any entity.
  • Disease context summaries: Review associated genes, drugs, phenotypes, and related diseases in one view.
  • Path lookup: Check direct connections and limited short paths between two biomedical entities.
  • Use case: A researcher studying Alzheimer’s disease can identify candidate genes, linked drugs, and clinical phenotypes before moving to deeper literature review.

Quick Start

Ask the skill to find the PrimeKG node for a disease such as Alzheimer’s disease and summarize its associated genes, drugs, and phenotypes.

Frequently Asked Questions about primekg

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I search a knowledge graph for genes, drugs, and diseases associated with a specific condition?▼

You can search a knowledge graph for biomedical entities by querying a local PrimeKG CSV dataset with pandas to retrieve associated genes, drugs, and diseases for a specific condition. The skill uses deterministic graph retrieval scripts to find matching nodes and summarize their relationships.

What is the best way to explore disease context and find related phenotypes in a biomedical knowledge graph?▼

Exploring disease context in a biomedical knowledge graph involves retrieving direct neighbors and relationship metadata for the target disease. This approach summarizes associated genes, drugs, phenotypes, and related conditions into a single view for precision medicine research.

Do I need a local CSV dataset to perform biomedical path analysis and node search?▼

Yes, you need a local PrimeKG CSV dataset to perform biomedical path analysis and node search. The skill relies on pandas-based querying through local scripts to execute deterministic graph retrieval operations across the biomedical entities.

Can I use pandas to find direct connections and short paths between two biomedical entities?▼

Yes, you can use pandas to find direct connections and limited short paths between two biomedical entities. The skill queries the local dataset to check path lookup and direct connections across genes, drugs, diseases, and phenotypes.

How does neighborhood exploration work when retrieving relationship metadata for biomedical research?▼

Neighborhood exploration retrieves direct neighbors and their relationship metadata for any specified biomedical entity. It queries the local knowledge graph dataset to map the immediate connections of genes, drugs, diseases, or phenotypes for research analysis.

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