### Primekg
Works with
name: "primekg"
description: "Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionprimekgExecute the skills CLI command in your project's root directory to begin installation:
Fetches primekg from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate primekg. Access via /primekg in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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| name | primekg |
| description | Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more. |
| license | Unknown |
| metadata | version: "1.0" skill-author: K-Dense Inc. (PrimeKG original from Harvard MIMS) |
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:
Data access: Programmatic access via query_primekg.py. Data is stored at C:\Users\eamon\Documents\Data\PrimeKG\kg.csv.
This skill should be used when:
Find identifiers for genes, drugs, or diseases.
from scripts.query_primekg import search_nodes
# Search for Alzheimer's disease nodes
results = search_nodes("Alzheimer", node_type="disease")
# Returns: [{"id": "EFO_0000249", "type": "disease", "name": "Alzheimer's disease", ...}]
Retrieve all connected nodes and relationship types.
from scripts.query_primekg import get_neighbors
# Get all neighbors of a specific disease ID
neighbors = get_neighbors("EFO_0000249")
# Returns: List of neighbors like {"neighbor_name": "APOE", "relation": "disease_gene", ...}
A high-level function to summarize associations for a disease.
from scripts.query_primekg import get_disease_context
# Comprehensive summary for a disease
context = get_disease_context("Alzheimer's disease")
# Access: context['associated_genes'], context['associated_drugs'], context['phenotypes']
The graph contains several key relationship types including:
protein_protein: Physical PPIsdrug_protein: Drug target/mechanism associationsdisease_gene: Genetic associationsdrug_disease: Indications and contraindicationsdisease_phenotype: Clinical signs and symptomsgwas: Genome-wide association studies evidenceget_neighbors, ensure you have the correct ID from search_nodes.get_disease_context for a broad overview before diving into specific genes or drugs.relation_type filter in get_neighbors to focus on specific evidence (e.g., only drug_protein).OpenTargets for deeper genetic evidence or Semantic Scholar for the latest literature context.scripts/query_primekg.py: Core functions for searching and querying the knowledge graph./mnt/c/Users/eamon/Documents/Data/PrimeKG/kg.csvPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
google-deepmind/science-skills
google-deepmind/science-skills
Registry listing for primekg matched our evaluation — installs cleanly and behaves as described in the markdown.
primekg reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in primekg — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
primekg fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
primekg is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
primekg reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: primekg is the kind of skill you can hand to a new teammate without a long onboarding doc.
primekg is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
primekg has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for primekg matched our evaluation — installs cleanly and behaves as described in the markdown.
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