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 nutrigenomicsgit 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/nutrigenomics)<a href="https://agentmods.dev/skills/exon-research/genomi/nutrigenomics"><img src="https://agentmods.dev/badge/skills/exon-research/genomi/nutrigenomics/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/nutrigenomics"><img src="https://agentmods.dev/badge/skills/exon-research/genomi/nutrigenomics.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 | $0.00061 | $0.01896 |
| Opus 5 | $0.00030 | $0.00948 |
| Sonnet 5 | $0.00012 | $0.00379 |
| Haiku 4.5 | $0.00006 | $0.00190 |
Grade A, and why
nutrigenomics 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 9d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nutrigenomics
Use this skill when the user asks how a germline variant affects nutrient metabolism, food tolerance, or taste perception within declared domains:
- folate metabolism
- vitamin D status
- iron storage
- lactose tolerance
- lipid diet response (APOE e2/e3/e4)
- obesity predisposition (single-marker context only)
Out of scope — refuse, do not approximate
Do NOT use this skill for, and DO surface as refusals:
- Macronutrient ratio prescriptions ("eat X% fat because of your APOE")
- Specific supplement dosing recommendations
- Weight-loss outcome prediction from genotype
- Diet-matching to genotype for fitness goals
- Microbiome-mediated dietary effects
- "Methylation cycle" prescriptions beyond folate marker context
- General health-outcome prediction from a small marker set
- "Detox capacity" framings
- Food allergy risk prediction
- Vitamin megadose prescriptions
The capability returns coverage_status: out_of_scope_for_input for these
domain ids. Treat the refusal literally — do not reach for adjacent records
that look similar.
Contract
- Reads public catalogue metadata only; does not read an Active Genome Index.
- For scanning an active genome, compose with
active_genome_index.classify_genotype_supportusing the variant coordinates carried in each record. - For stratified allele frequencies, call
gnomad.fetch_population_frequency. - For primary GWAS effect sizes, call
gwas.compare_variant_associationsusing thegwas_catalog_idcarried in each record'sdownstream_traits_with_gwas.
Convention: See
skills/conventions/context-routing.md. Convention: Seeskills/conventions/evidence-quality.md. Convention: Seeskills/_output-rules.md.
First Actions
- If the request is shaped like a diet prescription, supplement dosing, weight-loss prediction, or any item in the out-of-scope list, refuse first. Do not call retrieval tools.
- Use
nutrigenomics.list_domainsto confirm the relevant domain is declared and to inspect evidence-tier counts before drilling in. - Use
nutrigenomics.build_source_contextwhen grounding a discussion in provenance is needed — e.g. when a user asks where the records come from or why diet prescriptions are out of scope. - Use
nutrigenomics.retrieve_domain_markerswith the validateddomain_id. Defaultmin_evidence_tier="established". Loosen to"probable"only when the question explicitly invites less-replicated evidence. - Use
nutrigenomics.retrieve_variant_recordswhen the agent already has an rsID and wants to know which declared domains reference it.
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.
- 9d ago First seen · 170 lines · 61 tokens per session scan A 1c4df0e2481c
nutrigenomics is a skill published in the GitHub repository exon-research/genomi (482 stars, last pushed 8d ago), licensed Apache-2.0. It adds 61 tokens to every session and 1,896 once invoked, about $0.0003 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.
Other skills, from other repositories
experiment
Run a materials-science / ML compute job on Rockie GPU capacity. Trigger words "run experiment", "submit job", "/experiment", or requests to quote/approve GPU spend before an experiment. Picks the right GPU type and count from a natural-language description (DFT for QE/VASP/ABINIT, MD for GROMACS/LAMMPS/OpenMM…
release-doi
A release procedure for research repositories that publish versions with a DOI, a permanent identifier for scholarly work, through Zenodo.
autoresearch
Canonical around-the-clock research loop. Defines the agent's outer loop — read taste corpus + queue, pick the next experiment, mutate the explicitly-declared mutation surface, run the experiment under a hard time budget against a frozen metric, score, codify, repeat. Augmented with Karpathy's sharp primitives (frozen…
physics
Route physics simulation, modeling, validation, and research-compute requests across force fields, molecular dynamics, electronic structure, particle transport/collision, continuum multiphysics, plasma/PIC, nuclear/radiation, and astro/cosmology. Use open-source-first engines, refuse local heavyweight execution on the…
sota-delta
Track 3 quickstart wrapper that reproduces a paper or repository baseline on Rockie GPU, then extends it with a user-specified delta and compares baseline versus delta.
post-run-review
After an experiment finishes, structured review emits {isbug, failureclass, summary, metric, lowerisbetter}, auto-closes the journal node, emits a [LEARN] block when isbug=true, and files a [DEAD-END] when the failureclass is "bad-hypothesis". Use immediately after any training/eval run — the agent invokes this…