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sc-pathway-scoring

sc-pathway-scoring is a skill for Claude Code, Codex from TianGzlab/OmicsClaw. It costs 75 tokens per session (1,965 once invoked), scanned A, original, Apache-2.0.

A tool that scores the activity of known gene sets in each cell. A gene set is a group of genes linked to a biological pathway or process, such as a KEGG, Reactome, or Gene Ontology pathway.

In plain words
What is it for?
Calculating per-cell and group-level scores from a GMT gene-set file or supported built-in gene-set libraries.
Why use it?
It helps compare pathway activity between cells or groups instead of looking only at individual genes.

Skill for Claude CodeCodex

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

not rated 160repo 1mo ago A scan SkillSpector: warn 75 tokens original Apache-2.0

Good fit Calculating per-cell and group-level scores from a GMT gene-set file or supported built-in gene-set libraries.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tiangzlab/omicsclaw/sc-pathway-scoring
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-pathway-scoring
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-pathway-scoring

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-pathway-scoring"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-pathway-scoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,965 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 $0.00075 $0.01965
Opus 5 $0.00037 $0.00983
Sonnet 5 $0.00015 $0.00393
Haiku 4.5 $0.00007 $0.00197

Measured 5d ago against content hash b4b8d446834a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

sc-pathway-scoring 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 5d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (sc_pathway_scoring.py, tests/test_sc_pathway_scoring_methods.py, tests/test_sc_pathway_scoring.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-pathway-scoring/SKILL.md · 132 lines

How it starts

The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sc-pathway-scoring

When to use

The user has a normalised scRNA AnnData and a gene-set library (GMT file or one of the built-in DB aliases: hallmark, kegg, reactome, go_bp, ...) and wants per-cell scores quantifying how active each gene set is. Three methods:

  • aucell_r (default) — R-backed AUCell via decoupler-py-style bridge. Best statistical foundation; requires R env.
  • aucell_py — Python AUCell (--aucell-py-auc-threshold). Pure Python.
  • score_genes_py — Scanpy tl.score_genes per gene set. Lightest and fastest.

Output: tables/enrichment_scores.csv (cells × gene_sets), plus group-mean / group-high-fraction tables when --groupby is provided.

For bulk-style condition-vs-control GSEA / ORA on a DE table use sc-enrichment. For de-novo gene-program discovery use sc-gene-programs.

Inputs & Outputs

Inputs

  • Modalities: scrna
  • File types: .h5ad

Outputs

  • tables/aucell_scores.csv
  • tables/cell_metadata.csv
  • tables/enrichment_scores.csv
  • tables/expression_matrix.tsv
  • tables/gene_expression.csv
  • tables/gene_set_overlap.csv
  • tables/group_high_fraction.csv
  • tables/group_mean_scores.csv
  • tables/top_pathway_scores_long.csv
  • tables/top_pathways.csv
  • figures/r_pathway_violin.png
  • analysis_summary.txt
  • processed.h5ad
  • report.md
  • result.json
  • Processed AnnData (saves_h5ad)

Flow

  1. Load AnnData (--input) or build a demo.
  2. Load gene sets: parse --gene-sets GMT, OR fetch via --gene-set-db <alias> and write a resolved GMT.
  3. Validate: at least one gene-set member overlaps the input features (feature_label_source chosen from var_names / var["gene_symbol"] / etc.).
  4. Run preflight; resolve --groupby (auto-pick from leiden / louvain / cell_type if unset).
  5. Dispatch to method:
    • aucell_r: shell out to bundled R script via RScriptRunner.
    • aucell_py: AUCell-Python with --aucell-py-auc-threshold.
    • score_genes_py: Scanpy tl.score_genes per gene set.
  6. Compute group-aware aggregates if --groupby is set.
  7. Save tables, figures, processed.h5ad, report.md, result.json.

Read the full file on GitHub · 132 lines

Files

What ships with it

9 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. 5d ago First seen · 132 lines · 75 tokens per session scan A b4b8d446834a

Subscribe to this mod's changes

sc-pathway-scoring is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 75 tokens to every session and 1,965 once invoked, about $0.0004 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 2026-09-03.

Scientificaucellbioinformaticsdecouplergene-setsknowledge-graphllm-agentmulti-agentsmulti-omicspathway-scoringscanpy-score-genesscrnasinglecell
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