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-interdomain-hgtgit 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-interdomain-hgt)<a href="https://agentmods.dev/skills/fmschulz/omics-skills/bio-interdomain-hgt"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-interdomain-hgt/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-interdomain-hgt"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-interdomain-hgt.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 | $0.00041 | $0.04279 |
| Opus 5.5 | $0.00016 | $0.01712 |
| Sonnet 5.5 | $0.00008 | $0.00856 |
| Haiku 4.5 | $0.00004 | $0.00428 |
Grade A, and why
bio-interdomain-hgt 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 24d 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bio Interdomain HGT
Detect, polarize, and confirm horizontal gene transfer between a query genome
(virus, MAG, isolate, or bin) and other domains of life. Built for the common
asymmetric case where the query is well annotated but the comparison set is
genome-only (proteins missing). Pairs with /bio-annotation (homology/taxonomy),
/bio-phylogenomics (trees), /bio-viromics (viral classification), and
/bio-fasta-database-curator (building the arbiter database).
Instructions
Run the steps in order; capture outputs and provenance at each step. Steps 0 (database gate) and 5 (frame-aware context guard on eukaryotic DNA) are the ones most often skipped and most often responsible for wrong conclusions.
Run every search and tree step on compute nodes with an explicit thread count
(diamond --threads, iqtree3 -T, mafft --thread); on HPC, submit through
sbatch with CPUs matched to those counts.
After Steps 0-7 have produced their normalized TSVs, apply the evidence gates with the driver:
uv run --script skills/bio-interdomain-hgt/scripts/run_hgt_evidence.py \
forward_hits.tsv --arbiter-hits arbiter_hits.tsv --reciprocal reciprocal.tsv \
--context context.tsv --trees trees.tsv --sampling-depth sampling_depth.tsv \
--databases databases.json --hypotheses hypotheses.tsv --reflections reflections.tsv \
--query-domain ncldv --out results/bio-interdomain-hgt
The driver checksum-verifies the arbiter, lineage labels, and comparison collection
listed in databases.json, applies the gates below, normalizes confirmed candidates
by lineage sampling depth, and writes run_manifest.json under
schemas/hgt-evidence.schema.json. Input
contracts (tab-separated, exact column order) are in fixtures/; the gates are:
| Gate | Pass rule in the driver |
|---|---|
| Homology | query and subject coverage >= 0.5 and e-value <= 1e-5 |
| Reciprocal best hit | forward_rank and reverse_rank both 1 |
| Direction | query's best arbiter domain equals the recipient domain: recipient_to_query; recipient locus best-matches --query-domain: query_to_recipient; otherwise ambiguous |
| Context | recipient_domain_fraction >= 0.6; eukaryotic recipients also need method diamond_blastx |
| Phylogeny | nesting_clade equals expected_clade and support >= 0.9 on a 0-1 scale |
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/database-availability.md 3.5 KB
- docs/README.md 2.3 KB
- fixtures/arbiter_hits.tsv 163 B
- fixtures/arbiter.dmnd 29 B
- fixtures/comparison.fna 27 B
- fixtures/context.tsv 136 B
- fixtures/databases.json 609 B
- fixtures/forward_hits.tsv 189 B
- fixtures/hypotheses.tsv 433 B
- fixtures/labels.tsv 77 B
- fixtures/reciprocal.tsv 82 B
- fixtures/reflections.tsv 642 B
- fixtures/sampling_depth.tsv 38 B
- fixtures/trees.tsv 128 B
- schemas/hgt-evidence.schema.json 511 B
- scripts/run_hgt_evidence.py 10 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.
- 24d ago Changed · +32 lines · -6 tokens per session a406cd665142
- 1mo ago First seen · 232 lines · 47 tokens per session scan A 85664f0a43a1
bio-interdomain-hgt is a skill published in the GitHub repository fmschulz/omics-skills (11 stars, last pushed 24d ago), licensed MIT. It adds 41 tokens to every session and 4,279 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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