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 agentlas-ai/agentlas-desktop --skill bulk-rnaseqgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/bulk-rnaseq)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/bulk-rnaseq"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/bulk-rnaseq/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/agentlas-ai/agentlas-desktop/bulk-rnaseq"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/bulk-rnaseq.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00218 | $0.04007 |
| Opus 5.5 | $0.00087 | $0.01603 |
| Sonnet 5.5 | $0.00044 | $0.00801 |
| Haiku 4.5 | $0.00022 | $0.00401 |
Grade A, and why
bulk-rnaseq 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 1mo 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.
This is a copy
94% identical to bulk-rnaseq — 60 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bulk RNA-seq
Overview
This skill orchestrates a complete, defensible bulk RNA-seq differential-expression study, from raw sequencing reads to enriched pathways and figures. It is a router, not a reimplementation: most stages already have dedicated skills in this repo, and this skill connects them in the right order, fills the one real gap (raw reads → a gene-level counts matrix), and enforces the design and QC decisions that determine whether the final result is trustworthy.
"Defensible" means three things, applied throughout:
- Reproducible — pinned pipeline/tool versions, containers where possible, recorded parameters, fixed random seeds.
- Quality-gated — QC is inspected and acted on before, during, and after quantification, not skipped.
- Statistically sound — adequate replication, a design that matches the biology, counts handled correctly, and FDR-controlled testing.
The pipeline is: FastQC/trim → align/quant (STAR/Salmon) → counts → DE (pydeseq2) → enrichment (pathway-enrichment) → figures.
When to Use This Skill
Use this skill when the user wants to:
- Go from FASTQ files (or a sequencing run) to differentially expressed genes and pathways.
- Run or configure
nf-core/rnaseq, or align/quantify with STAR, Salmon, or featureCounts. - Turn Salmon/STAR/featureCounts output into a counts matrix ready for DESeq2/PyDESeq2.
- Design or sanity-check a bulk RNA-seq experiment (replicates, batch, strandedness) before committing compute.
- Scope an end-to-end RNA-seq analysis and decide which tools and skills to chain.
This is bulk RNA-seq (samples = biological specimens). For single-cell/nuclei data use scanpy; for the DE statistics alone use pydeseq2; for enrichment alone use pathway-enrichment.
The Pipeline at a Glance
flowchart TD
fastq["Raw FASTQ + samplesheet"] --> qc["FastQC + MultiQC"]
qc --> trim["Trim: fastp / Trim Galore"]
trim --> align["Align + quant: STAR and/or Salmon"]
align --> counts["Gene-level counts matrix"]
counts --> de["Differential expression"]
de --> enrich["Pathway / GSEA enrichment"]
de --> fig["Figures"]
enrich --> fig
nfcore["nf-core/rnaseq via nextflow skill"] -.->|"path A"| align
manual["Standalone recipes (this skill)"] -.->|"path B"| align
bridge["build_counts_matrix.py (this skill)"] -.-> counts
pydeseq2skill["pydeseq2 skill"] -.-> de
pwskill["pathway-enrichment skill"] -.-> enrich
vizskill["scientific-visualization skill"] -.-> fig
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.
- 1mo ago First seen · 216 lines · 218 tokens per session scan A d1ec11577f56
bulk-rnaseq is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed today), licensed Apache-2.0. It adds 218 tokens to every session and 4,007 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to bulk-rnaseq, differing in 60 lines, and is treated as a copy.
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