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-pydeseq2git 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-pydeseq2)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pydeseq2"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pydeseq2/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-pydeseq2"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pydeseq2.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 | $0.00088 | $0.02038 |
| Opus 5.5 | $0.00035 | $0.00815 |
| Sonnet 5.5 | $0.00018 | $0.00408 |
| Haiku 4.5 | $0.00009 | $0.00204 |
Grade A, and why
alterlab-pydeseq2 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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyDESeq2
Overview
PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. It supports complete workflows from data loading through result interpretation, including single-factor and multi-factor designs, Wald tests with multiple-testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData.
When to Use This Skill
Use this skill when:
- Analyzing bulk RNA-seq count data for differential expression
- Comparing gene expression between experimental conditions (e.g., treated vs control)
- Performing multi-factor designs accounting for batch effects or covariates
- Converting R-based DESeq2 workflows to Python
- Integrating differential expression analysis into Python-based pipelines
- Users mention "DESeq2", "differential expression", "RNA-seq analysis", or "PyDESeq2"
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| Single-cell differential expression or cluster marker genes | alterlab-scanpy (markers) or alterlab-scvi-tools (model-based DE) |
| Turning FASTQ into the count matrix (salmon/kallisto/STAR quantification, tximport) | alterlab-rnaseq-quant |
| Generic regression / GLM / mixed models on non-count data | alterlab-statsmodels |
| Somatic or germline variant calling from sequencing reads | alterlab-nf-core-sarek |
| Microbiome feature-table differential abundance | alterlab-qiime2-amplicon |
Installation and Requirements
uv pip install "pydeseq2>=0.5,<0.6"
System requirements (pydeseq2 0.5.x, current 0.5.4): Python ≥3.11; numpy ≥2.0, pandas ≥2.2,
scipy ≥1.12, scikit-learn ≥1.4, anndata ≥0.11, formulaic ≥1.0.2 and formulaic-contrasts ≥0.2
(parse the ~ design formula and build contrast vectors), matplotlib ≥3.9. These are pulled in
automatically as dependencies.
API note (0.4+): parallelism is configured through an inference object, not a bare n_cpus= kwarg:
from pydeseq2.default_inference import DefaultInference
inference = DefaultInference(n_cpus=8)
dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~condition", inference=inference)
ds = DeseqStats(dds, contrast=["condition", "treated", "control"], inference=inference)
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 Changed · +24 lines 60e7715051cb
- 1mo ago First seen · 114 lines · 88 tokens per session scan A 8aa9d5611b65
alterlab-pydeseq2 is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (78 stars, last pushed 4d ago), licensed MIT. It adds 88 tokens to every session and 2,038 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.
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