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 fmschulz/omics-skills --skill bio-viromicsgit clone --depth 1 https://github.com/fmschulz/omics-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/fmschulz/omics-skills/bio-viromics)<a href="https://agentmods.dev/skills/fmschulz/omics-skills/bio-viromics"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-viromics/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/fmschulz/omics-skills/bio-viromics"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-viromics.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 | $0.00052 | $0.02122 |
| Opus 5.5 | $0.00021 | $0.00849 |
| Sonnet 5.5 | $0.00010 | $0.00424 |
| Haiku 4.5 | $0.00005 | $0.00212 |
Grade A, and why
bio-viromics 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 23d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bio Viromics
Detect, quality-check, and classify viral contigs, then compare each viral genome against a literature-derived reference set.
Instructions
Tool guides, versions, and verified flags: docs/README.md.
Run geNomad, CheckV, GVClass, and vConTACT3 on a workstation or compute node, never on a login node. Pass an explicit thread count to each tool (-t). Without it, geNomad and CheckV use every CPU available to the process, and vConTACT3 uses every CPU on the host, even inside a smaller Slurm allocation. On HPC, submit through sbatch with CPUs matched to that thread count and follow the site's account and partition rules.
- Start from
/tracking-taxonomy-updatesQuickClade domain routing when assemblies, MAGs, genomes, or contigs have not been screened. Viral, virus-like, mixed, or low-confidence contigs enter this skill; bacterial, archaeal, and eukaryotic rows stay on their domain routes unless later evidence contradicts the triage. - Detect viruses with geNomad v1.12.0 (database v1.9) as the primary virus and plasmid classifier:
genomad end-to-end -t <n> contigs.fna out/ genomad_db. For prokaryotic-virus discovery, VirSorter2 v2.2.4 is a complementary detector; combine either detector with CheckV QC to remove false positives. - Run CheckV v1.1.1 with database v1.5 for completeness, contamination, and host-region trimming:
checkv end_to_end viral.fna out/ -d checkv-db-v1.5 -t <n>. - Infer the likely viral group from QuickClade, geNomad taxonomy, genome statistics, and marker or similarity evidence.
- Search the literature for that group (
/polars-dovmed) and writeanalysis_playbook.md: typical reference sets, markers, comparative analyses, genome features, plots, and outlier signals used by people who study the group. - Choose taxonomy, clustering, phylogenetic, and comparative methods from the playbook:
- Bacteriophages and other prokaryotic viruses: vConTACT3 gene-sharing networks for genus-to-order assignment. vConTACT3 replaces vConTACT2. The upstream source tag is 3.2.4; bioconda ships 3.1.6. Record the version you ran.
- Nucleocytoviricota, Mirusviricota, and Preplasmiviricota: GVClass v2.0.3 with resource bundle v2.0.0, plus marker-gene phylogenies of core genes through
/bio-phylogenomics. - RNA viruses, ssDNA viruses, and other groups that vConTACT3 does not cover well: group-specific markers, phylogenomics, and protein-family methods from the playbook. Do not force a phage-oriented workflow on them.
- For each viral genome or high-quality viral contig, call genes and annotate proteins when needed (
/bio-gene-calling,/bio-annotation), then inspect the annotation set according to the playbook rather than a fixed feature list. - Compare each query to the literature-supported reference set on the comparative axes in the repository AGENTS.md: marker census, per-family copy number, conserved neighborhoods, and ncRNA census. Write
marker_census.tsv,family_copy_number_comparison.tsv,conserved_neighborhoods.tsv, andncRNA_census.tsvto one comparison directory. Report what matches expectations, what is missing, expanded, or query-specific, and which patterns are likely artifacts. - Genome-size frontier: place each query's genome size and gene count in the distribution of close relatives and the literature-reported extremes for the group. State the percentile, the distance from the group median, and whether the query exceeds the known record size, and cite the paper that defines the record. A mid-distribution placement is still a result.
- Assemble the evidence bundle. The driver checksum-verifies the geNomad, CheckV, GVClass, and vConTACT3 database resources, checks the hypothesis register and reflections, copies the four comparative tables, and computes
genome_size_frontier.tsv:
What ships with it
16 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.
- docs/checkv-usage.md 5.0 KB
- docs/genomad-usage.md 4.6 KB
- docs/gvclass-usage.md 7.6 KB
- docs/README.md 3.8 KB
- docs/vcontact3-usage.md 4.6 KB
- fixtures/comparative/conserved_neighborhoods.tsv 68 B
- fixtures/comparative/family_copy_number_comparison.tsv 67 B
- fixtures/comparative/marker_census.tsv 61 B
- fixtures/comparative/ncRNA_census.tsv 103 B
- fixtures/database.fixture 26 B
- fixtures/hypotheses.tsv 539 B
- fixtures/metrics.tsv 278 B
- fixtures/reflections.tsv 568 B
- fixtures/resources.json 946 B
- schemas/evidence-bundle.schema.json 479 B
- scripts/build_viromics_evidence.py 7.9 KB runs code
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.
- 23d ago Changed · +2 lines · +17 tokens per session d6e197275de1
- 1mo ago Changed 22da94175213
- 1mo ago First seen · 90 lines · 35 tokens per session scan A 3cf083884091
bio-viromics is a skill published in the GitHub repository fmschulz/omics-skills (11 stars, last pushed 23d ago), licensed MIT. It adds 52 tokens to every session and 2,122 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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