synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and 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 synthetic-sciences/openscience --skill bulk-rnaseqgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/bulk-rnaseq)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/bulk-rnaseq"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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/synthetic-sciences/openscience/bulk-rnaseq"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/bulk-rnaseq.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 | $0.00218 | $0.04235 |
| Opus 5.5 | $0.00087 | $0.01694 |
| Sonnet 5.5 | $0.00044 | $0.00847 |
| Haiku 4.5 | $0.00022 | $0.00424 |
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 19d 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.
This is a copy
95% identical to bulk-rnaseq — 69 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bulk RNA-seq
Overview
This skill orchestrates a complete, defensible bulk RNA-seq differential-expression study, from raw sequencing reads to enriched pathways and figures. It is a router, not a reimplementation: most stages already have dedicated skills in this repo, and this skill connects them in the right order, fills the one real gap (raw reads → a gene-level counts matrix), and enforces the design and QC decisions that determine whether the final result is trustworthy.
"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
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.
- 19d ago Changed · +2 lines 5a9b4f97cbc3
- 29d ago Changed · +5 lines 83dd1c80e3fb
- 1mo ago First seen · 218 lines · 218 tokens per session scan A 536e7071e591
bulk-rnaseq is a skill published in the GitHub repository synthetic-sciences/openscience (4,004 stars, last pushed 3d ago), licensed Apache-2.0. It adds 218 tokens to every session and 4,235 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to bulk-rnaseq, differing in 69 lines, and is treated as a copy.
Other skills, from other repositories
songsee
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
arxiv
Search arXiv papers by keyword, author, category, or ID.
research-paper-writing
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
knowledge-extension-ecosystem-lsp-code-intelligence
A codebase guide to LSP, the standard protocol behind editor features such as symbol queries, file diagnostics, and language-aware code information.