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 zongtingwei/Bioclaw_Skills_Hub --skill differential-expressiongit clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_HubWrote 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/zongtingwei/bioclaw_skills_hub/differential-expression)<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/differential-expression"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/differential-expression/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/zongtingwei/bioclaw_skills_hub/differential-expression"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/differential-expression.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 | $0.00031 | $0.00963 |
| Opus 5 | $0.00015 | $0.00481 |
| Sonnet 5 | $0.00006 | $0.00193 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
differential-expression 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 9d 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.
Differential Expression
Version Compatibility
Reference examples assume:
pydeseq20.4+pandas2.2+numpy1.26+matplotlib3.8+
Verify before use:
- Python:
python -c "import pydeseq2, pandas; print(pydeseq2.__version__, pandas.__version__)"
Overview
Use this skill for count-based DE from bulk RNA-seq or similar count matrices when the user needs:
- robust model fitting
- explicit contrasts
- ranked gene tables
- volcano and MA plots
- pathway-ready output tables
When To Use This Skill
- raw count matrix and sample metadata are available
- the task is condition, treatment, or genotype comparison
- batch or pairing terms may need explicit modeling
Quick Route
- no replicates: do not pretend formal DE is robust
- 2 replicates per group: possible but conservative interpretation
- 3 or more replicates per group: standard starting point
Progressive Disclosure
- Read technical_reference.md for design formulas, confounding checks, and contrast logic.
- Read commands_and_thresholds.md for PyDESeq2 code, recommended filters, and output file conventions.
Prerequisites
| Requirement | Recommendation |
|---|---|
| minimum replicates per group | >= 2 |
| preferred replicates per group | >= 3 |
| input values | raw integer counts |
Expected Inputs
- raw count matrix
- sample metadata
- explicit contrast such as treated vs control
Expected Outputs
results/de_results.tsvresults/de_ranked_genes.tsvfigures/volcano.pdffigures/ma_plot.pdfqc/sample_pca.pdf
Starter Pattern
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats
dds = DeseqDataSet(
counts=counts_df,
metadata=metadata_df,
design_factors=["condition", "batch"],
)
dds.deseq2()
stats = DeseqStats(dds, contrast=("condition", "treated", "control"))
stats.summary()
res = stats.results_df.sort_values("padj")
res.to_csv("results/de_results.tsv", sep="\t")
What ships with it
3 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.
- 9d ago First seen · 165 lines · 31 tokens per session scan A 97a37fce6dc1
differential-expression is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (26 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 963 once invoked, about $0.0002 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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