Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-primekggit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-primekg)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-primekg"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-primekg/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-primekg"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-primekg.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.01648 |
| Opus 5 | $0.00040 | $0.00824 |
| Sonnet 5 | $0.00016 | $0.00330 |
| Haiku 4.5 | $0.00008 | $0.00165 |
Grade A, and why
alterlab-primekg scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PrimeKG Knowledge Graph Skill
Overview
PrimeKG (Chandak, Huang & Zitnik, Scientific Data 2023; mims-harvard/PrimeKG) is a precision medicine knowledge graph integrating 20 primary resources. It contains 129,375 nodes and 4,050,249 edges across 30 edge types and 10 node types, including drug-target, disease-gene, and disease-phenotype 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 scripts/query_primekg.py. Point the loader at the kg.csv released on Harvard Dataverse via the PRIMEKG_DATA_PATH environment variable (it defaults to ../data/kg.csv relative to the script). All functions operate on the x_*/y_*/relation/display_relation columns of 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
Run under uv run python from the skill directory (so scripts/ is importable),
or add the scripts/ dir to sys.path. Set PRIMEKG_DATA_PATH to your kg.csv.
1. Search for Entities
Find identifiers for genes, drugs, or diseases. Pass node_type using PrimeKG's
exact type strings (see node types below) — e.g. "gene/protein", not "gene".
from scripts.query_primekg import search_nodes
# Search for Alzheimer's disease nodes
results = search_nodes("Alzheimer", node_type="disease")
# Returns: [{"id": <MONDO id>, "type": "disease", "name": "...",
# "source": "MONDO" | "MONDO_grouped"}, ...]
# Disease ids are MONDO ids; PrimeKG groups diseases, so one name can map to
# several MONDO ids. Use the returned id with get_neighbors.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 134 lines · 80 tokens per session scan A 5c961ee2bf12
alterlab-primekg is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 6d ago), licensed MIT. It adds 80 tokens to every session and 1,648 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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