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Arboreto

Arboreto is an agent skill (a SKILL.md file) from K-Dense-AI/scientific-agent-skills. Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. It works with Claude Code, Codex, Cursor, Gemini CLI and GitHub Copilot and has 47,813 GitHub stars across a repository of 7 listed skills.

github.com/K-Dense-AI/scientific-agent-skills/skills/arboreto (opens in a new tab)

View on GitHub (opens in a new tab)

Add this skill

Claude

Claude Code loads skills from ~/.claude/skills/ (all projects) or .claude/skills/ (one project):

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git
cp -r scientific-agent-skills/skills/arboreto ~/.claude/skills/arboreto   # personal, or .claude/skills in a project

In the Claude apps, zip the arboreto folder and upload it under Customize > Skills > + > Upload a skill (code execution must be on).

ChatGPT / Codex

Codex reads skills from .agents/skills/ in a repo or ~/.agents/skills/ for every project:

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git
cp -r scientific-agent-skills/skills/arboreto .agents/skills/arboreto   # repo; ~/.agents/skills for all projects

Standalone skills also load in the ChatGPT desktop app.

Cursor

Cursor loads skills from .cursor/skills/ (or ~/.cursor/skills/) and also reads .claude/skills/:

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git
cp -r scientific-agent-skills/skills/arboreto .cursor/skills/arboreto   # project; ~/.cursor/skills for all projects

Source (checked Oct 7, 2026): code.claude.com/docs/en/skills (opens in a new tab), support.claude.com/en/articles/12512180-using-skills-in-claude (opens in a new tab), learn.chatgpt.com/docs/build-skills (opens in a new tab), cursor.com/docs/context/skills (opens in a new tab)

What this skill does

Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks. Use Arboreto to rank candidate regulator-target associations from expression measurements. GRNBoost2 fits stochastic gradient boosting regressions; GENIE3 fits random forests.

When it triggers

  • Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.

More skills in K-Dense-AI/scientific-agent-skills

7 skills are listed from this repository.

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FAQ

Arboreto FAQ

Still curious? Email info@appsgit.com.

What is the Arboreto skill?

Arboreto is an agent skill (a SKILL.md file) from K-Dense-AI/scientific-agent-skills. Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. It works with Claude Code, Codex, Cursor, Gemini CLI and GitHub Copilot and has 47,813 GitHub stars across a repository of 7 listed skills. Its SKILL.md lives at github.com/K-Dense-AI/scientific-agent-skills/skills/arboreto.

How do I install the Arboreto skill?

Copy the arboreto folder (the one containing SKILL.md) into ~/.claude/skills/ for Claude Code, .agents/skills/ for Codex or .cursor/skills/ for Cursor. The agent picks it up automatically when a task matches its description.

Is the Arboreto skill free?

Yes. The repository is open source under the MIT license.

Is Arboreto maintained?

The repository's most recent commit was on Oct 5, 2026. Its latest release is v2.72.0. appsgit only lists skills from repositories with a commit in the last six months.