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 pathway-enrichmentgit 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/pathway-enrichment)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/pathway-enrichment"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/pathway-enrichment/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/pathway-enrichment"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/pathway-enrichment.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 | $0.00239 | $0.03670 |
| Opus 5.5 | $0.00096 | $0.01468 |
| Sonnet 5.5 | $0.00048 | $0.00734 |
| Haiku 4.5 | $0.00024 | $0.00367 |
Grade A, and why
pathway-enrichment 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.
This is a copy
94% identical to pathway-enrichment — 103 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pathway Enrichment
Overview
Enrichment analysis answers "what biology is over-represented in my genes?" It is the standard last step after differential expression, a screen, or clustering. There are two core methods, and choosing correctly is the single most important decision:
- ORA (over-representation analysis) — take a thresholded gene list (e.g., padj < 0.05) and test which gene sets it overlaps more than chance, using Fisher's exact / hypergeometric tests. Tools: Enrichr, g:Profiler.
- GSEA (gene set enrichment analysis) — take the whole ranked list of genes (no threshold) and test whether each gene set is concentrated toward the top or bottom. Preranked GSEA uses a per-gene score (e.g., the DESeq2
stat). Better when effects are broad and subtle.
This skill orchestrates these analyses, the gene-set databases behind them, and the interpretation pitfalls that make results wrong or unpublishable.
When to Use This Skill
Use this skill when the user wants to:
- Find enriched GO terms / KEGG / Reactome / WikiPathways / MSigDB Hallmark sets in a gene list.
- Run GSEA / preranked GSEA on DESeq2, edgeR, limma, or Scanpy
rank_genes_groupsoutput. - Score pathway activity per sample/cell (ssGSEA, GSVA).
- Interpret, deduplicate, and visualize enrichment results, or build a publication table/figure.
- Decide between ORA and GSEA, pick gene-set libraries, choose a background, or fix gene-ID problems.
For quick one-off Enrichr lookups the gget skill (gget enrichr) is lighter weight; for raw pathway/interaction APIs (Reactome, KEGG, STRING) see the database-lookup skill. Use this skill for full, defensible enrichment workflows.
Choosing the Right Method
| Situation | Method | Tool / entry point |
|---|---|---|
| You have a discrete hit list (DE genes, screen hits, cluster markers) | ORA | gp.enrichr(...) or g:Profiler |
| You have a full ranked list (every tested gene + a score) | Preranked GSEA | gp.prerank(...) |
| You have an expression matrix + class labels | GSEA | gp.gsea(...) |
| You want a pathway score per sample/cell | ssGSEA / GSVA | gp.ssgsea(...), gp.gsva(...) |
| You need a custom background or 500+ organisms | ORA with custom domain | g:Profiler (domain_scope='custom') |
| You want TF / signaling activity (PROGENy, DoRothEA) | activity inference | see references/databases-and-gene-sets.md (decoupler) |
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.
- 1mo ago Changed · +5 lines fa6d442f29bf
- 1mo ago First seen · 214 lines · 239 tokens per session scan A 239467a9b2d5
pathway-enrichment is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 239 tokens to every session and 3,670 once invoked, about $0.0010 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to pathway-enrichment, differing in 103 lines, and is treated as a copy.
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