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 functional-genomicsgit 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/functional-genomics)<a href="https://agentmods.dev/skills/exon-research/genomi/functional-genomics"><img src="https://agentmods.dev/badge/skills/exon-research/genomi/functional-genomics/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/functional-genomics"><img src="https://agentmods.dev/badge/skills/exon-research/genomi/functional-genomics.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 | $0.00025 | $0.01570 |
| Opus 5 | $0.00013 | $0.00785 |
| Sonnet 5 | $0.00005 | $0.00314 |
| Haiku 4.5 | $0.00003 | $0.00157 |
Grade A, and why
functional-genomics 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Functional Genomics Perturbation Evidence
Retrieve functional-genomics perturbation evidence for a declared experimental context plus candidate genes. Screens are one supported perturbation experiment subtype, not the capability name.
Contract
Perturbation evidence comes from native public retrieval, user-provided local tables, reviewed stored research, or explicitly supplied source records. Generic gene biology can explain a result, but it should not outrank direct perturbation-source evidence.
Direct support is source-verified perturbation evidence. Source records carry verified fields or support spans for the requested cell line, perturbation, assay/readout, and candidate gene relationship; broader biology remains adjacent or plausibility-only evidence.
Native coverage currently includes BioGRID ORCS when a BioGRID ORCS access key is available, DepMap CRISPR gene-effect release tables when a CSV URL or path is configured, and bounded NCBI GEO metadata/table discovery. GEO's advantage is source discovery for public or published perturbation datasets: SeriesMatrix files, supplementary tables, and accession-indexed study records that curated screen APIs may not expose for the requested cell line, perturbation, assay, or readout. If native sources cannot be queried, the response makes that coverage state visible rather than weak ranking evidence.
Tool Flow
- Extract candidate gene symbols and the requested context: organism, cell line, perturbation, assay, phenotype, resistance, sensitivity, viability, or readout.
- Call
functional_genomics.compare_gene_perturbationfor the normal flow. It retrieves native public perturbation records when configured, verifies source records, and returns candidate evidence rows. - Call
functional_genomics.retrieve_perturbation_recordsonly for explicit native-source inspection, coverage debugging, or source availability review. - Call
functional_genomics.query_geowhen the advantage is public source discovery: the question mentions a published/public screen dataset, study accession, supplementary table, SeriesMatrix-style file, or compare has insufficient BioGRID/DepMap/stored evidence for a requested perturbation context that likely came from a public study. The user does not need to name GEO. GEO metadata alone is not direct evidence; direct support still requires table-derived, source-verified candidate gene and perturbation-context fields. - If the source is a local CSV/TSV result table, call
functional_genomics.import_perturbation_tablefirst. - Pass supplied, imported, or retrieved source records to
functional_genomics.compare_gene_perturbation; it verifies source records before comparing candidate genes. - Use verified perturbation-source evidence when the user asks for only the gene
symbol. Audit
decision_evidencebefore explaining the result.
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 · 139 lines · 25 tokens per session scan A 6de35fcd555d
functional-genomics is a skill published in the GitHub repository exon-research/genomi (482 stars, last pushed 8d ago), licensed Apache-2.0. It adds 25 tokens to every session and 1,570 once invoked, about $0.0001 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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