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 agentlas-ai/agentlas-desktop --skill pathway-enrichmentgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/pathway-enrichment)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/pathway-enrichment"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/pathway-enrichment"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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 | $0.03601 |
| Opus 5.5 | $0.00096 | $0.01440 |
| Sonnet 5.5 | $0.00048 | $0.00720 |
| Haiku 4.5 | $0.00024 | $0.00360 |
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
100% identical to pathway-enrichment — 96 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 — 212 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 First seen · 212 lines · 239 tokens per session scan A 9738be9a8ba8
pathway-enrichment is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 239 tokens to every session and 3,601 once invoked, about $0.0010 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to pathway-enrichment, differing in 96 lines, and is treated as a copy.
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