primekg

Overview of primekg skill

primekg is a knowledge-graph skill for querying PrimeKG, a precision medicine graph that connects genes, drugs, diseases, phenotypes, and related biomedical entities. It is most useful when you need a fast, evidence-oriented way to move from a disease or target name to nearby biology, instead of manually searching papers one by one.

The primekg skill is a strong fit for Academic Research, drug repurposing exploration, target/context lookups, and network pharmacology questions where relationships matter more than a single fact. The main value is not just “finding entities,” but seeing how they connect across clinical and molecular layers.

What primekg is best at

PrimeKG excels at local graph queries: direct neighbors, disease context, and plausible drug-disease or gene-disease paths. That makes it useful for early-stage hypothesis generation, background checks, and building a shortlist of biologically connected candidates.

When primekg is a good install

Install primekg if you regularly ask questions like “what genes sit near this disease?”, “what drugs are linked to this phenotype?”, or “what evidence connects this target to a clinical outcome?” It is less helpful for broad literature review, protocol writing, or tasks that need narrative synthesis rather than graph reasoning.

What blocks adoption

The skill assumes you want to work with a structured PrimeKG dataset and can tolerate graph-style output. If you need fully curated clinical guidance, exhaustive literature review, or simple one-line definitions, primekg will feel narrower than a general research prompt.

How to Use primekg skill

primekg install and setup

Use the repo skill in your usual skills workflow, then open the skill entry file first. For this repository, start with scientific-skills/primekg/SKILL.md, then inspect any linked code or companion documents the skill references. The repository signal is concentrated in that file, so there is no large support tree to browse.

A practical primekg install check is simple: confirm the skill can answer entity lookup questions and relationship questions using the PrimeKG graph model, not just summarize the source text.

How to phrase a strong request

The best primekg usage starts with a specific entity, a desired relationship, and a research purpose. Weak requests say “tell me about diabetes.” Strong requests say “find genes, drugs, and phenotypes directly linked to type 2 diabetes, then prioritize repurposing-relevant drug connections.”

Good prompt ingredients:

• the anchor entity: disease, gene, drug, or phenotype
• the relation you care about: neighbors, paths, associations, or context
• the use case: hypothesis generation, target review, repurposing, or background research
• optional filters: direction, confidence preference, or what to exclude

Recommended workflow

Start narrow, then widen. First ask for direct neighbors or the most relevant local context. Then ask for a second pass that groups results by entity type or by likely research utility. This keeps the output usable and reduces noisy graph wandering.

For example, a stronger primekg guide request looks like:

• “Using PrimeKG, show direct disease-gene-drug connections for Parkinson’s disease and flag repurposing-relevant drugs.”
• “For IL6, identify associated diseases and phenotypes in PrimeKG, then summarize the most research-useful connections.”
• “Map one-hop and two-hop links from obesity to candidate drug classes.”

primekg skill FAQ

Is primekg only for Academic Research?

No, but Academic Research is the clearest fit. The primekg skill is also useful for exploratory biotech, translational biology, and drug discovery work. It is not designed for patient-facing medical advice.

How is primekg different from a normal prompt?

A normal prompt may generate plausible biomedical associations from model memory. primekg is meant to anchor the answer in a graph-centered workflow, which is better when relationship tracing, entity neighborhoods, and repurposing links matter.

Do I need prior graph or bioinformatics experience?

No. Beginners can use primekg if they can name a target and describe the question clearly. The main learning curve is knowing whether you want direct neighbors, disease context, or pathway-like connections.

When should I not use primekg?

Skip primekg when you need the latest literature, formal clinical recommendations, wet-lab protocols, or a broad overview that does not depend on graph relationships. It is also a poor fit if your question has no clear anchor entity.

How to Improve primekg skill

Give the skill a tighter research frame

primekg improves when you say what decision the graph should support. “Find related entities” is vague; “find drug and phenotype context around Alzheimer’s disease for repurposing screening” gives the skill a useful boundary and makes the output easier to rank.

Ask for the right granularity first

One common failure mode is requesting too much at once. If the first answer is noisy, narrow to one entity type, one hop distance, or one disease/gene pair. Then expand only after the local neighborhood looks relevant.

Use the first pass to expose gaps

Treat the first primekg output as a map, not the final answer. If you see missing entity types, ask for a different slice: genes only, drugs only, or phenotype links only. If the result is too broad, ask it to prioritize by research value or mechanism relevance.

Improve primekg usage with better anchors

Better inputs usually include exact names and a research goal. Compare:

• Weak: “What connects obesity and drugs?”
• Strong: “Using primekg, list direct drug and phenotype neighbors for obesity and highlight the most plausible repurposing leads.”
• Weak: “Tell me about TP53.”
• Strong: “For TP53, return disease associations and nearby drugs relevant to cancer research.”

If you want the best primekg guide outcome, keep the task anchored, ask for graph relationships explicitly, and iterate from direct neighbors to broader paths only after the first response is useful.