arboreto

arboreto is a skill for Claude Code, Codex from FridrichMethod/awesome-skills. It costs 66 tokens per session (2,106 once invoked), scanned A, original, no licence file.

A tool for inferring gene regulatory networks from gene-expression data. These networks estimate which transcription factors may influence which genes in bulk or single-cell RNA sequencing data.

In plain words
What is it for?
Use it to analyze bulk or single-cell RNA-sequencing data and identify likely transcription factor targets.
Why use it?
It helps turn large expression datasets into possible regulator-to-gene relationships. Distributed computation can help with transcriptomics datasets at single-cell scale.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

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.

agentmods
npx agentmods add skills/fridrichmethod/awesome-skills/arboreto
Any agent
npx skills add FridrichMethod/awesome-skills --skill arboreto
Clone the repo
git clone --depth 1 https://github.com/FridrichMethod/awesome-skills

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 arboreto

README.md
[![agentmods](https://agentmods.dev/badge/skills/fridrichmethod/awesome-skills/arboreto.svg)](https://agentmods.dev/skills/fridrichmethod/awesome-skills/arboreto)
Your own site
<a href="https://agentmods.dev/skills/fridrichmethod/awesome-skills/arboreto"><img src="https://agentmods.dev/badge/skills/fridrichmethod/awesome-skills/arboreto.svg" alt="Measured on agentmods" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,106 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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 $0.00066 $0.02106
Opus 5 $0.00033 $0.01053
Sonnet 5 $0.00013 $0.00421
Haiku 4.5 $0.00007 $0.00211

Measured 2d ago against content hash afeff219264e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

arboreto 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/basic_grn_inference.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/arboreto/SKILL.md · 268 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Files

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.

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. 2d ago First seen · 268 lines · 66 tokens per session scan A afeff219264e

Subscribe to this mod's changes

arboreto is a skill published in the GitHub repository FridrichMethod/awesome-skills (14 stars, last pushed 7d ago), with no licence file. It adds 66 tokens to every session and 2,106 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

analysis-workflow

Organize multi-step scientific analyses into reproducible, self-contained modules. Use for workflows such as QC→PCA→DEG→GSEA that produce scripts, inputs, figures, tables, and methods. Creates a stable module layout, records exact inputs/parameters/package and database versions in each module README, keeps large data…

xuzhougeng/wisp-science · 81 tokens

public-data-access

Plan, configure, validate, and document portable public-bioinformatics data acquisition. Use for GEO/GSE/GDS, SRA/ENA, TCGA/GDC, GTEx, DepMap, public expression matrices, raw reads, release files, manifests, resumable downloads, and reusable local caches. Keep the workflow provider-neutral: DepMap is one optional…

xuzhougeng/wisp-science · 83 tokens

bio-clip-seq-clip-deep-learning

Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when…

BioTender-max/awesome-bio-agent-skills · 123 tokens

bio-chipseq-chip-deep-learning

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input…

BioTender-max/awesome-bio-agent-skills · 214 tokens

bio-chipseq-visualization

Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM…

BioTender-max/awesome-bio-agent-skills · 156 tokens

bio-chipseq-chromatin-state-segmentation

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos…

BioTender-max/awesome-bio-agent-skills · 190 tokens