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 GPTomics/bioSkills --skill deseq2-basicsgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/deseq2-basics)<a href="https://agentmods.dev/skills/gptomics/bioskills/deseq2-basics"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/deseq2-basics/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/gptomics/bioskills/deseq2-basics"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/deseq2-basics.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 | $0.00155 | $0.06062 |
| Opus 5 | $0.00077 | $0.03031 |
| Sonnet 5 | $0.00031 | $0.01212 |
| Haiku 4.5 | $0.00015 | $0.00606 |
Grade A, and why
bio-differential-expression-deseq2-basics 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 8d 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 — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DESeq2 1.42+, apeglm 1.28+, ashr 2.2+, IHW 1.34+, tximport 1.30+, edgeR 4.0+ (for cross-comparison), PyDESeq2 0.5+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters - Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
DESeq2 Basics
"Find differentially expressed genes between conditions" -> Fit a negative-binomial GLM per gene with shared dispersion shrinkage, test the coefficient of interest (Wald) or the joint effect of a factor (LRT), and report a shrunken effect-size estimate for ranking.
The Single Most Important Modern Insight -- Shrunken LFC and the Wald p-value come from different models
lfcShrink() returns LFCs from a Bayesian posterior with apeglm/ashr/normal priors, BUT the p-value column it carries forward is still the unshrunken Wald p-value from results(). This is a deliberate design choice (Zhu, Ibrahim, Love 2019 Bioinformatics 35:2084) -- the shrunken estimate is for ranking and visualization; the p-value is for inference. Reporting "shrunken LFC = 0.4, padj = 1e-8" mixes two models, which is fine because both are correct for their stated purpose. What is NOT fine: using the shrunken LFC in a downstream filter and then claiming FDR control on that filter (it has none). For threshold-based FDR claims, use lfcThreshold= or TREAT (glmTreat in edgeR).
A second consequence: results(dds) with no name= or contrast= argument silently returns the last coefficient in resultsNames(dds) -- which depends on factor level order and design formula order. Always specify the contrast explicitly. Tutorials that hard-code results(dds) are setting an example that breaks the moment another factor is added.
What ships with it
4 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.
- 8d ago First seen · 338 lines · 155 tokens per session scan A 39e7fe31ee2d
bio-differential-expression-deseq2-basics is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 155 tokens to every session and 6,062 once invoked, about $0.0008 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 2026-09-03.
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