bio-gene-calling

bio-gene-calling is a skill for Claude Code from fmschulz/omics-skills. It costs 49 tokens per session (2,336 once invoked), scanned A, original, MIT.

A gene-calling workflow that predicts coding genes and basic sequence features in prokaryotic, viral, and eukaryotic assemblies. An assembly is a reconstructed DNA sequence from sequencing data.

In plain words
What is it for?
Planning and running gene prediction, generating annotation inputs, counting transfer RNAs and other non-coding RNAs, and resuming completed analyses.
Why use it?
It provides a repeatable, recorded process for choosing tools by organism type, checking inputs, and tracking exactly which data and models were used.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions AGENTS.md.

Part of the omics-skills plugin — 34 skills, 4 agents shipped together

Good fit Planning and running gene prediction, generating annotation inputs, counting transfer RNAs and other non-coding RNAs, and resuming completed analyses.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fmschulz/omics-skills/bio-gene-calling
Install

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.

Any agent
npx skills add fmschulz/omics-skills --skill bio-gene-calling
Clone the repo
git clone --depth 1 https://github.com/fmschulz/omics-skills

Made for: Claude Code.

Or install omics-skills, the plugin that ships this one along with the rest of its 34 skills, 4 agents.

Wrote 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.

agentmods badge for bio-gene-calling

README.md
[![agentmods](https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-gene-calling/github.svg)](https://agentmods.dev/skills/fmschulz/omics-skills/bio-gene-calling)
Your own site
<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.

agentmods 80×15 button for bio-gene-calling

Your own site · 80×15
<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>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,336 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 23d ago against content hash e1c4e6646a1c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/run_gene_calling.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/bio-gene-calling/SKILL.md · 97 lines

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.

  1. 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 --execute
    

    The 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.csv and config.ini files, 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 writes trnascan.tsv.done; an empty table without that marker is rerun. Execution replaces pending ncRNA counts with parsed tRNAscan-SE and Infernal counts.

  2. Select gene caller by organism class:

    • Bacteria and Archaea: Pyrodigal v3.7+. Set mode to single for an isolate genome (training needs at least 20 kbp of sequence) or metagenome for 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-gv 2.11.0 binary, use that pinned binary with -p meta and record which implementation produced the calls. ViroSync builds prodigal-gv 2.11.0 with a node-capacity patch through pixi 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.pl entry point for a BRAKER4 run. Keep BRAKER3 only for a documented legacy reproduction.
  3. 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 Pychopper guidance in /bio-reads-qc-mapping, including plain .fastq output handling and resume from existing classified reads after report-plotting failures.
    • Map transcript reads splice-aware to each candidate draft genome with minimap2 (-ax splice family 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.tsv bundle with columns: sample_id, evidence_type, genome_id, path, notes. The bundle should be directly usable by BRAKER4 or another eukaryote-aware caller.
  4. 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.

  5. 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 (-B bacteria, -A archaea, -E eukaryotes, -G viruses). Without a flag it uses eukaryotic models. Use ARAGORN v1.2.41+ when tmRNA is needed.
    • rRNA: Infernal v1.1.5+ cmsearch against 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_ga and counts only hits that pass the inclusion E-value (! in the tblout inc column). Record both results.
  6. 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.

  7. 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.

  8. Flag gene-calling anomalies relative to the inferred group and data type, including patterns that could hide interesting biology or indicate artifacts.

  9. Keep ncRNA_census.tsv with 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.

Read the full file on GitHub · 97 lines

Changes

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.

  1. 23d ago Changed · +7 lines · -9 tokens per session e1c4e6646a1c
  2. 1mo ago Changed · +23 tokens per session e19de0668065
  3. 1mo ago First seen · 90 lines · 35 tokens per session scan A b4c3aec3667c

Subscribe to this mod's changes

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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