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 exon-research/genomi --skill drug-targetsgit clone --depth 1 https://github.com/exon-research/genomiWrote 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/exon-research/genomi/drug-targets)<a href="https://agentmods.dev/skills/exon-research/genomi/drug-targets"><img src="https://agentmods.dev/badge/skills/exon-research/genomi/drug-targets/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/exon-research/genomi/drug-targets"><img src="https://agentmods.dev/badge/skills/exon-research/genomi/drug-targets.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 | $0.00031 | $0.01018 |
| Opus 5 | $0.00015 | $0.00509 |
| Sonnet 5 | $0.00006 | $0.00204 |
| Haiku 4.5 | $0.00003 | $0.00102 |
Grade A, and why
drug-targets 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Drug Targets
Use this skill for disease-scoped clinical drug-target retrieval, direct drug-target records, PharmaProjects-style target context, ChEMBL mechanism genes, DrugBank target context, or candidate-gene review for a drug, drug class, or mechanism.
Contract
- Direct drug-target or mechanism evidence outranks target-disease association scores and GWAS-style association.
- ChEMBL, DrugBank, and PharmaProjects-style records can support direct target claims when the source supports both the gene and the drug, class, mechanism, or indication context.
- Open Targets association context is useful for review; direct drug-target evidence comes from source records that support the drug, class, or mechanism relationship.
- Open Targets disease drug and clinical candidate records can retrieve disease-scoped clinical drug-target genes when the drug target comes from a mechanism-of-action row.
- Treat returned rankings as source evidence. The agent decides whether the
drug-target prior matches the question. When using cross-source comparison,
use
prior_fitbefore reading a panel as task-relevant and auditdecision_evidencebefore answering.
Tool Flow
phenotype.retrieve_disease_drug_targetsretrieves Open Targets clinical drug candidate target genes for a supplied disease anchor.phenotype.compare_drug_target_evidencecompares candidate genes against direct drug-side context: drug, drug class, or mechanism.- If source support is missing, use
research.list_sourcesto choose direct target sources, review them, and store narrow findings withresearch.record. - Re-run the same selected tool after recording reviewed findings.
Example:
phenotype.retrieve_disease_drug_targetswith{"disease":"asthma","genes":["ADRB2","IL13"]}phenotype.compare_drug_target_evidencewith{"drug_class":"beta agonist","phenotype":"asthma","genes":["ADRB2","IL13"],"source_records":[{"genes":["ADRB2"],"drug_class":"beta agonist","verified_fields":{"genes":["ADRB2"],"drug_class":"beta agonist"},"support_spans":[{"field":"genes","text":"source-backed ADRB2 text"}]}]}
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 · 102 lines · 31 tokens per session scan A 05321c8b5018
drug-targets is a skill published in the GitHub repository exon-research/genomi (482 stars, last pushed 10d ago), licensed Apache-2.0. It adds 31 tokens to every session and 1,018 once invoked, about $0.0002 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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