agentmods.dev

sc-enrichment

sc-enrichment is a skill for Claude Code, Codex from TianGzlab/OmicsClaw. It costs 71 tokens per session (2,403 once invoked), scanned A, original, Apache-2.0.

A pathway analysis for ranked gene lists from single-cell RNA sequencing, using collections of genes that represent biological processes. It checks whether selected pathways are unusually represented or concentrated near the top of a ranking.

In plain words
What is it for?
Use it for over-representation analysis, GSEA, or GSVA on groups of marker or differentially expressed genes.
Why use it?
It turns long marker or differential-expression gene lists into more interpretable biological themes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 161repo changed 19d ago A scan SkillSpector: warn 71 tokens original Apache-2.0

Good fit Use it for over-representation analysis, GSEA, or GSVA on groups of marker or differentially expressed genes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tiangzlab/omicsclaw/sc-enrichment
View source ↗ TianGzlab/OmicsClaw tiangzlab.github.io
Install

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.

Any agent
npx skills add TianGzlab/OmicsClaw --skill sc-enrichment
Clone the repo
git clone --depth 1 https://github.com/TianGzlab/OmicsClaw

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for sc-enrichment

README.md
[![agentmods](https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-enrichment/github.svg)](https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-enrichment)
Your own site
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-enrichment"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-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.

agentmods 80×15 button for sc-enrichment

Your own site · 80×15
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-enrichment"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-enrichment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,403 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Rogue Agent · line 3
    Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.
    Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00071 $0.02403
Opus 5.5 $0.00028 $0.00961
Sonnet 5.5 $0.00014 $0.00481
Haiku 4.5 $0.00007 $0.00240

Measured 19d ago against content hash 124b1032d911, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

sc-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 19d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (_api.py, examples/example_step.py, sc_enrichment.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/singlecell/scrna/sc-enrichment/SKILL.md · 185 lines

How it starts

The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sc-enrichment

When to use

Test named gene sets against per-group marker/DE rankings with ORA or GSEA. GSVA scores mean expression per group in R. For scores per cell use sc-pathway-scoring; for programmes discovered without gene sets use sc-gene-programs.

Steps

Start a step with python skills/_sdk/notebook/run.py new enrichment. Load enrichment = load_skill("sc-enrichment"), then call rank_groups, load_gene_sets (GMT, JSON or Enrichr library), and ora or gsea. Pass the complete tested gene universe as background for ORA; its API default is all genes in the supplied ranking. Write returned tables and Figures with write_output. See examples/example_step.py.

The default engine is Python. Explicit engine="auto" prefers R when its packages are available; engine="r" requires clusterProfiler. The legacy gsea_r method uses GO_BP/KEGG/Reactome annotation databases, not custom GMT. gsva accepts a gene-set mapping, or None for the GO_BP/KEGG R database route.

Inputs & Outputs

Rankings need gene/names and a score or effect, with an optional group column. rank_groups reads log-normalised X, never raw. For the processed PBMC demo, use .raw.to_adata() in new steps; the CLI retains its old scaled-X demo ranking for compatibility. The CLI accepts H5AD or an upstream output directory containing processed.h5ad and marker/DE tables.

ORA/GSEA CLI writes processed.h5ad, report.md, result.json, reproducibility/, and five tables: enrichment_results.csv, enrichment_significant.csv, group_summary.csv, ranking_input.csv, top_terms.csv. Python GSEA adds tables/gsea_running_scores.csv when running curves can be built. figure_data/ mirrors plot tables. Figures depend on available terms. GSVA uses tables/gsva_r_scores.csv and figures/gsva_r_heatmap.png instead. See references/output_contract.md for R-specific files.

Gotchas

  • result.json.summary.resolved_engine records the engine actually used. The default is now python; ask for --engine auto to retain auto-selection.
  • tables/enrichment_results.csv records Python GSEA's local fallback in engine, with the reason in result.json.summary.warnings. This is not interchangeable with gseapy's permutation implementation.
  • tables/enrichment_significant.csv retains the legacy fixed FDR 0.05 filter. --fdr-threshold controls group_summary.csv and the report summary.
  • _api.py:37: rank_groups does not infer a suitable matrix. Using scaled X can yield invalid log-fold changes; use a log-normalised snapshot instead.
  • _api.py:103: empty gene sets fail validation. Failed R annotation mapping raises an error; the R scripts no longer manufacture pathways to fill an empty result.
  • _api.py:205: GSVA's R database route supports GO_BP and KEGG, not Enrichr's Hallmark alias. For Hallmark, explicitly load gene sets and pass the mapping to gsva.

Read the full file on GitHub · 185 lines

Files

What ships with it

12 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.

Changes

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.

  1. 19d ago Changed · +27 lines 124b1032d911
  2. 1mo ago First seen · 158 lines · 71 tokens per session scan A 6e45c911674e

Subscribe to this mod's changes

sc-enrichment is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 20d ago), licensed Apache-2.0. It adds 71 tokens to every session and 2,403 once invoked, about $0.0003 per session on Opus 5.5. 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.

GSEAORAScientificbioinformaticsdecouplerenrichmentgsvaknowledge-graphllm-agentmulti-agentspathway-enrichmentscrnasinglecell
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