Arboreto

Arboreto is a Data skill for Claude Code, published by K-Dense-AI in scientific-agent-skills.

33.5K stars3.3K forkson K-Dense-AI/scientific-agent-skillsAdded 2026/08/14Repository updated 2026/08/13

agent-skillsai-scientistbioinformaticschemoinformaticsclaudeclaude-skillsclaudecodeclinical-researchcomputational-biologydata-analysisdrug-discoverygenomicsmaterials-sciencemetabolomicsproteomicsscientific-computingscientific-visualization

Install in seconds

Install Arboreto

Copy Arboreto into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/arboreto ~/.claude/skills/arboreto

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/arboreto/SKILL.md in K-Dense-AI/scientific-agent-skills
Installs to: ~/.claude/skills/arboreto
Collection: One of 25 skills cataloged from this repository
Category: Data — 668 skills

What Arboreto does

Arboreto infers gene regulatory networks from gene expression data using scalable ensemble regression algorithms (GRNBoost2, GENIE3). Use when analyzing bulk or single-cell RNA-seq data to identify transcription factor-target relationships and regulatory interactions.

Arboreto is cataloged under Data on DirSkills. Arboreto comes from a repository tagged agent-skills, ai-scientist, bioinformatics, chemoinformatics and claude.

Documentation

README

Excerpt

Arboreto

Overview

Arboreto is a Python library from Aerts Lab for inferring gene regulatory networks (GRNs) from gene expression data. It parallelizes tree-based ensemble regression (GRNBoost2, GENIE3) with Dask across local cores or remote clusters.

Core capability: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions).

Upstream: PyPI 0.1.6 (2021-02-09, latest). Docs: arboreto.readthedocs.io. Primary downstream consumer: pySCENIC.

Quick Start

Install arboreto:

uv pip install arboreto

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Arboreto

What else does K-Dense-AI publish alongside Arboreto?
Arboreto is one of 25 skills that DirSkills catalogs from K-Dense-AI/scientific-agent-skills, the repository it ships in. Its siblings there include Adaptyv Bio Foundry API, Aeon and Analytical Method Validation. Each one is a separate skill with its own page in this directory, installs the same way Arboreto does, and is maintained by K-Dense-AI in that same repository. The rest of the collection is listed on the K-Dense-AI/scientific-agent-skills page.
How does Arboreto compare to other Data skills?
Arboreto ranks #36 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Arboreto against them. Open each page to compare what they document and how they install.