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 TianGzlab/OmicsClaw --skill functional-enrichment-from-degsgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/functional-enrichment-from-degs)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/functional-enrichment-from-degs"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/functional-enrichment-from-degs/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/tiangzlab/omicsclaw/functional-enrichment-from-degs"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/functional-enrichment-from-degs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00011 | $0.05026 |
| Opus 5 | $0.00005 | $0.02513 |
| Sonnet 5 | $0.00002 | $0.01005 |
| Haiku 4.5 | $0.00001 | $0.00503 |
Grade A, and why
Functional Enrichment Analysis (GSEA + ORA) 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 12d 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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Functional Enrichment Analysis
Translate differential expression results into biological insights using GSEA and ORA.
When to Use This Skill
Use this skill after completing differential expression analysis to identify enriched pathways and biological processes.
Use when:
- ✅ You have DE results with fold changes and p-values
- ✅ Want to answer: "What pathways or processes are affected?"
- ✅ Need to interpret gene lists in biological context
- ✅ Preparing results for publication or validation
Two complementary methods: GSEA (primary, uses all ranked genes, detects coordinated changes) and ORA (secondary, uses significant gene list, validates GSEA). Default recommendation: Run GSEA unless user specifically requests ORA or has only a gene list (no fold changes).
See references/gsea_ora_comparison.md for detailed method comparison.
Quick Start (Example Data)
Test this skill with real DE results in ~2 minutes:
# Load example DE results from airway dataset (dexamethasone treatment)
source("scripts/load_example_data.R")
de_results <- load_airway_de_results() # Auto-installs packages (~1-2 min, ~40MB)
# Load required packages and scripts
library(clusterProfiler)
library(msigdbr)
source("scripts/prepare_gene_lists.R")
source("scripts/get_msigdb_genesets.R")
source("scripts/run_gsea.R")
source("scripts/generate_plots.R")
source("scripts/export_results.R")
# Run GSEA workflow
ranked_genes <- create_ranked_list(de_results)
term2gene <- get_msigdb_genesets("human", c("H")) # Hallmark pathways only for speed
gsea_result <- run_gsea(ranked_genes, term2gene, n_perm = 1000)
generate_all_plots(gsea_result)
export_all(gsea_result, ranked_genes, output_prefix = "quick_test")
What you get:
- Dataset: Human airway smooth muscle cells, dexamethasone treatment vs untreated
- Expected results: ~5-10 significant Hallmark pathways (Inflammatory Response, TNF-alpha signaling, Interferon response)
- Outputs: CSV results, SVG/PNG plots, RDS objects, markdown summary
What ships with it
14 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.
- references/database_guide.md 8.2 KB
- references/decision-guide.md 13 KB
- references/gsea_ora_comparison.md 6.8 KB
- references/gsea_ora_validation_framework.md 14 KB
- references/interpretation_guidelines.md 11 KB
- references/method-reference.md 18 KB
- scripts/export_results.R 7.1 KB
- scripts/generate_plots.R 7.2 KB
- scripts/get_msigdb_genesets.R 2.8 KB
- scripts/load_de_results.R 1.8 KB
- scripts/load_example_data.R 6.9 KB
- scripts/prepare_gene_lists.R 3.4 KB
- scripts/run_gsea.R 1.4 KB
- scripts/run_ora.R 1.9 KB
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.
- 12d ago First seen · 393 lines · 11 tokens per session scan A 1fc6f6e6dba5
Functional Enrichment Analysis (GSEA + ORA) is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 11 tokens to every session and 5,026 once invoked, about $0.0001 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 2026-08-30.
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