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-gene-callinggit 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-gene-calling)<a href="https://agentmods.dev/skills/fmschulz/omics-skills/bio-gene-calling"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-gene-calling/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-gene-calling"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-gene-calling.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 | $0.00049 | $0.02336 |
| Opus 5.5 | $0.00020 | $0.00934 |
| Sonnet 5.5 | $0.00010 | $0.00467 |
| Haiku 4.5 | $0.00005 | $0.00234 |
Grade A, and why
bio-gene-calling 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bio Gene Calling
Call genes and annotate basic features for prokaryotes, viruses, and eukaryotes.
Instructions
Tool guides and versions: docs/README.md.
-
Validate the assembly and tool manifests, then generate a per-assembly execution plan:
uv run --script skills/bio-gene-calling/scripts/run_gene_calling.py \ assemblies.tsv --tool-manifest tool-manifest.json \ --out results/bio-gene-calling --threads 8 # Inspect run_manifest.json, then execute or resume the same plan # (inside an sbatch job on HPC; match --threads to --cpus-per-task): uv run --script skills/bio-gene-calling/scripts/run_gene_calling.py \ assemblies.tsv --tool-manifest tool-manifest.json \ --out results/bio-gene-calling --threads 8 --executeThe tool manifest must pin the BRAKER4 repository commit, Snakefile checksum, container-lock checksum, and every Rfam model checksum. The driver records input FASTA checksums, routes each assembly by domain, writes idempotent BRAKER4
samples.csvandconfig.inifiles, uses absolute covariance-model paths, and writes the default and relaxed ncRNA census rows.--threads(default 8) sets the thread count of every tool. Execution reuses non-empty declared outputs. A tRNAscan-SE run that exits 0 without a table counts as zero tRNAs and writestrnascan.tsv.done; an empty table without that marker is rerun. Execution replaces pending ncRNA counts with parsed tRNAscan-SE and Infernal counts. -
Select gene caller by organism class:
- Bacteria and Archaea: Pyrodigal v3.7+. Set
modetosinglefor an isolate genome (training needs at least 20 kbp of sequence) ormetagenomefor MAGs, short contigs, and mixed input. - Viruses, including giant and alternative-code viruses: the Prodigal-GV model set in metagenomic mode. The driver runs pyrodigal-gv v0.3+, which bundles the prodigal-gv 2.11.0 models. When results must match ViroSync or another pipeline that pins the standalone
prodigal-gv2.11.0 binary, use that pinned binary with-p metaand record which implementation produced the calls. ViroSync builds prodigal-gv 2.11.0 with a node-capacity patch throughpixi run setup-prodigal-gv. - Eukaryotes: BRAKER4 as the current upstream workflow. Pin the tested repository commit, workflow checksum, and container lock in provenance. BRAKER4 is a Snakemake workflow; do not invoke the legacy
braker.plentry point for a BRAKER4 run. Keep BRAKER3 only for a documented legacy reproduction.
- Bacteria and Archaea: Pyrodigal v3.7+. Set
-
For eukaryotic/protist drafts with ONT cDNA or other transcriptome reads, build a transcript evidence bundle before gene calling:
- Orient/filter full-length ONT cDNA reads with the
Pychopperguidance in/bio-reads-qc-mapping, including plain.fastqoutput handling and resume from existing classified reads after report-plotting failures. - Map transcript reads splice-aware to each candidate draft genome with minimap2 (
-ax splicefamily settings appropriate to the organism/data), sort/index BAMs, and compute a per-genome mapped fraction table. - Use the best-supported draft genome as the primary evidence target, but keep the full mapping table because it documents sample/genome assignment and cross-sample ambiguity.
- Produce StringTie long-read GTF/transcript FASTA and, when useful, a reference-free transcript assembly such as RNA-Bloom. Summarize these paths in a
gene_calling_evidence.tsvbundle with columns: sample_id, evidence_type, genome_id, path, notes. The bundle should be directly usable by BRAKER4 or another eukaryote-aware caller.
- Orient/filter full-length ONT cDNA reads with the
-
Run gene calling and produce per-assembly GFF/protein/CDS outputs. BRAKER4 emits compressed results under
output/{sample}/results/; Pyrodigal and pyrodigal-gv emit the normalized uncompressed paths recorded in the run manifest. -
Run tRNA and rRNA detection on every assembly and report counts per class. Zero hits at default and relaxed thresholds are required results; never leave ncRNA presence or absence unstated.
- tRNA: tRNAscan-SE v2.0.12+ with the domain flag (
-Bbacteria,-Aarchaea,-Eeukaryotes,-Gviruses). Without a flag it uses eukaryotic models. Use ARAGORN v1.2.41+ when tmRNA is needed. - rRNA: Infernal v1.1.5+
cmsearchagainst the relevant Rfam covariance models. Pick the model set by domain of life:- Bacteria: RF00177 (SSU 16S), RF02541 (LSU 23S), RF00001 (5S).
- Archaea: RF01959 (SSU 16S), RF02540 (LSU 23S), RF00001 (5S).
- Eukaryotes: RF01960 (SSU 18S), RF02543 (LSU 28S), RF00002 (5.8S), RF00001 (5S).
- Metazoan mitochondria, when applicable: RF02555 (12S), RF02546 (16S).
- Viruses: no domain-specific set. The census records an explicit "not screened" rRNA row; screen host-domain models when host contamination is suspected.
The default run is
cmsearch --rfam --cut_ga --nohmmonly. The relaxed run drops--cut_gaand counts only hits that pass the inclusion E-value (!in the tbloutinccolumn). Record both results.
- tRNA: tRNAscan-SE v2.0.12+ with the domain flag (
-
For viral or otherwise specialized genomes, choose the gene caller and mode from tool documentation and the literature-derived analysis playbook for the inferred group; record the rationale.
-
Summarize gene count, gene density, coding fraction, ORF length distribution, unusually long ORFs, overlapping genes, tRNAs, rRNAs, and other features that may affect downstream discovery.
-
Flag gene-calling anomalies relative to the inferred group and data type, including patterns that could hide interesting biology or indicate artifacts.
-
Keep
ncRNA_census.tsvwith columns: assembly, class (tRNA/rRNA/tmRNA/other), tool, model (Rfam accession when applicable), threshold (default/relaxed), count, notes. The driver writes it; add tmRNA or other ncRNA rows when you run those screens. The file is required even when all counts are zero.
What ships with it
22 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/braker.md 4.5 KB
- docs/infernal.md 2.3 KB
- docs/pyrodigal-gv.md 3.9 KB
- docs/pyrodigal.md 4.0 KB
- docs/README.md 4.1 KB
- docs/trnascan-se.md 4.3 KB
- fixtures/assemblies.tsv 140 B
- fixtures/bacterium.fna 30 B
- fixtures/braker4/container-lock.json 62 B
- fixtures/braker4/Snakefile 52 B
- fixtures/eukaryote.fna 30 B
- fixtures/rfam/RF00001.cm 33 B
- fixtures/rfam/RF00002.cm 33 B
- fixtures/rfam/RF00177.cm 33 B
- fixtures/rfam/RF01959.cm 33 B
- fixtures/rfam/RF01960.cm 33 B
- fixtures/rfam/RF02540.cm 33 B
- fixtures/rfam/RF02541.cm 33 B
- fixtures/rfam/RF02543.cm 33 B
- fixtures/tool-manifest.json 1.6 KB
- fixtures/virus.fna 26 B
- scripts/run_gene_calling.py 15 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 · +7 lines · -9 tokens per session e1c4e6646a1c
- 1mo ago Changed · +23 tokens per session e19de0668065
- 1mo ago First seen · 90 lines · 35 tokens per session scan A b4c3aec3667c
bio-gene-calling is a skill published in the GitHub repository fmschulz/omics-skills (11 stars, last pushed 23d ago), licensed MIT. It adds 49 tokens to every session and 2,336 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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