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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-nf-core-sarekgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-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/alterlab-ieu/alterlab-academic-skills/alterlab-nf-core-sarek)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-nf-core-sarek"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-nf-core-sarek/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/alterlab-ieu/alterlab-academic-skills/alterlab-nf-core-sarek"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-nf-core-sarek.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 | $0.00283 | $0.02586 |
| Opus 5 | $0.00142 | $0.01293 |
| Sonnet 5 | $0.00057 | $0.00517 |
| Haiku 4.5 | $0.00028 | $0.00259 |
Grade A, and why
alterlab-nf-core-sarek 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 12d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nf-core/sarek — FASTQ-to-VCF Variant Calling
The workflow-runner entry point for raw-reads-to-variants: drive the
Nextflow nf-core/sarek pipeline (pinned -r 3.8.1)
to take germline or somatic short-read FASTQ through alignment, GATK4 duplicate
marking and base-quality recalibration, and SNV/indel calling, then hand the
resulting VCFs to the suite's database and parsing skills for interpretation.
This skill is the command-line / workflow counterpart to the suite's
Python-library bioinformatics skills. Use it for the raw-data-to-VCF leg;
use the library skills (alterlab-pysam, alterlab-tiledbvcf) once you hold a VCF.
When to Use This Skill
Trigger this skill when the user wants to:
- Go from FASTQ to VCF — call variants on whole-genome (WGS) or whole-exome (WES) short reads.
- Run germline SNV/indel calling (one or many normal samples).
- Run somatic / tumor-normal calling (matched tumor + normal, or tumor-only).
- Use nf-core/sarek specifically, or want a reproducible "GATK best-practices alignment-to-VCF" pipeline without hand-writing every step.
- Resume a run from an intermediate
--step(already have BAM/CRAM, only need recalibration or variant calling).
Does NOT Trigger — route adjacent requests here
| The request is really about… | Route to |
|---|---|
| Parsing / filtering / reading an existing VCF/BAM in Python (pysam/htslib) | alterlab-pysam |
| Storing / querying large multi-sample variant stores (TileDB-VCF arrays) | alterlab-tiledbvcf |
| Clinical significance of a called variant (pathogenic/benign) | alterlab-clinvar |
| Population allele frequencies for a called variant | alterlab-gnomad |
| Somatic mutation catalogue / cancer census lookup | alterlab-cosmic |
| RNA-seq transcript/gene quantification (salmon/kallisto), not DNA variants | alterlab-rnaseq-quant |
| 16S/ITS amplicon / microbiome FASTQ → feature table | alterlab-qiime2-amplicon |
| Sequence homology / similarity search (BLAST+, DIAMOND) | alterlab-blast |
| Spatial transcriptomics neighborhood/SVG analysis | alterlab-squidpy-spatial |
| Differential expression stats from counts | alterlab-pydeseq2 |
What ships with it
6 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.
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.
- 12d ago First seen · 165 lines · 283 tokens per session scan A fb464f2a233e
alterlab-nf-core-sarek is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 7d ago), licensed MIT. It adds 283 tokens to every session and 2,586 once invoked, about $0.0014 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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