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 AndyZhuang/Opentest --skill tooluniverse-rnaseq-deseq2git clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/tooluniverse-rnaseq-deseq2)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-rnaseq-deseq2"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-rnaseq-deseq2/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/andyzhuang/opentest/tooluniverse-rnaseq-deseq2"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-rnaseq-deseq2.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 | $0.00124 | $0.04476 |
| Opus 5 | $0.00062 | $0.02238 |
| Sonnet 5 | $0.00025 | $0.00895 |
| Haiku 4.5 | $0.00012 | $0.00448 |
Grade A, and why
tooluniverse-rnaseq-deseq2 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 — 537 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RNA-seq Differential Expression Analysis (DESeq2)
Comprehensive differential expression analysis of RNA-seq count data using PyDESeq2, with integrated enrichment analysis (gseapy) and gene annotation via ToolUniverse.
BixBench Coverage: Validated on 53 BixBench questions across 15 computational biology projects covering RNA-seq, miRNA-seq, and differential expression analysis tasks.
Core Principles
- Data-first approach - Load and validate count data and metadata BEFORE any analysis
- Statistical rigor - Always use proper normalization, dispersion estimation, and multiple testing correction
- Flexible design - Support single-factor, multi-factor, and interaction designs
- Threshold awareness - Apply user-specified thresholds exactly (padj, log2FC, baseMean)
- Reproducible - Set random seeds, document all parameters, output complete results
- Question-driven - Parse what the user is actually asking and extract the specific answer
- Enrichment integration - Chain DESeq2 results into pathway/GO enrichment when requested
- English-first queries - Use English gene/pathway names in all tool calls
When to Use This Skill
Apply when users:
- Have RNA-seq count matrices and want differential expression analysis
- Ask about DESeq2, DEGs, differential expression, padj, log2FC
- Need dispersion estimates or diagnostics
- Want enrichment analysis (GO, KEGG, Reactome) on DEGs
- Ask about specific gene expression changes between conditions
- Need to compare multiple strains/conditions/treatments
- Ask about batch effect correction in RNA-seq
- Questions mention "count data", "count matrix", "RNA-seq", "transcriptomics"
Required Packages
# Core (MUST be installed)
import pandas as pd
import numpy as np
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats
# Enrichment (optional, for GO/KEGG/Reactome)
import gseapy as gp
# ToolUniverse (optional, for gene annotation)
from tooluniverse import ToolUniverse
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 · 537 lines · 124 tokens per session scan A 8aee4546f747
tooluniverse-rnaseq-deseq2 is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 124 tokens to every session and 4,476 once invoked, about $0.0006 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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