Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill bulk-rnaseqgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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/k-dense-ai/scientific-agent-skills/bulk-rnaseq)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/bulk-rnaseq"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/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/k-dense-ai/scientific-agent-skills/bulk-rnaseq"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/bulk-rnaseq.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00111 | $0.04262 |
| Opus 5.5 | $0.00044 | $0.01705 |
| Sonnet 5.5 | $0.00022 | $0.00852 |
| Haiku 4.5 | $0.00011 | $0.00426 |
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 14d 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.
Copies of this mod
5 near-identical copies found in the catalogue:
- bulk-rnaseq — 100% identical, 79 lines differ
- bulk-rnaseq — 100% identical, 79 lines differ
- bulk-rnaseq — 100% identical, 79 lines differ
- bulk-rnaseq — 95% identical, 69 lines differ
- bulk-rnaseq — 94% identical, 60 lines differ
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bulk RNA-seq
Overview
This skill prepares bulk RNA-seq reads and quantification output for a reproducible gene-level comparison. It owns sample validation, reads-to-counts recipes, and the count/metadata handoff; downstream statistical fitting and enrichment remain in their specialist skills.
"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
7 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.
- 14d ago Changed · +13 lines · -107 tokens per session e945e9702685
- 15d ago Changed · +5 lines 9ef66e2ef202
- 1mo ago Changed · +17 lines d1ec11577f56
- 1mo ago First seen · 199 lines · 218 tokens per session scan A b39c5999e482
bulk-rnaseq is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (50,453 stars, last pushed today), licensed MIT. It adds 111 tokens to every session and 4,262 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
bio-prefect-dask-nextflow
Choose and scaffold Prefect+Dask or Nextflow pipelines. Use when designing a local, distributed, or Slurm-backed bioinformatics workflow.
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
rdkit-qsar-pharmacophore
Computes 2048-bit ECFP4 Morgan fingerprints from SMILES, trains LightGBM regressors for pIC50 prediction, and extracts SHAP feature attributions.
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bulk-rnaseq-counts-to-de-deseq2
Run differential expression analysis on bulk RNA-seq count data with DESeq2 (R). Covers DESeqDataSet construction from a count matrix, tximport (Salmon/Kallisto), featureCounts, or SummarizedExperiment; pre-filtering; design formulas (simple, batch, paired, interaction, multi-factor, LRT); result extraction by…
seurat-skill
Comprehensive Seurat v5 (R) guide for single-cell RNA-seq and multimodal analysis. Covers installation, standard workflows (Normalize/SCTransform), clustering, integration (CCA/RPCA/Harmony), differential expression (FindMarkers/FindAllMarkers), visualization (DimPlot/FeaturePlot/VlnPlot/DoHeatmap), spatial…