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Agent skill

Arboreto

by aipoch in aipoch/medical-research-skills

Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships.

MITAuto-check passedResearch & Science

Install Arboreto

skills CLI
$ npx skills add aipoch/medical-research-skills --skill arboreto -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills arboreto --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/arboreto' .claude/skills/arboreto && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
arboreto
GitHub stars
2k
Token cost
~802 tokens
SKILL.md length
311 words
Files
5 (incl. scripts, references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships.

  • Single-cell RNA-seq to identify TF→target regulatory relationships
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Runs Python scripts from its folder; calls python
  • Tasks that involve Bioinformatics

What it does

Arboreto is an agent skill from aipoch/medical-research-skills. Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships.

Its SKILL.md is about 800 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `arboreto_audit_result_v1.json`, `references/algorithms.md` and `references/distributed_computing.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Single-cell RNA-seq to identify TF→target regulatory relationships
  • Tasks that involve Bioinformatics

Example prompts

  • “/arboreto”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Arboreto loads about 802 tokens when it runs, and up to ~1k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 311 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~802
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 311 words, ~802 tokens.

Download SKILL.mdSave it as .claude/skills/arboreto/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
arboreto
description
Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You have a bulk RNA-seq expression matrix and want to infer transcription factor (TF) → target gene regulatory edges.
  • You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions.
  • You need GRN inference that can scale to large datasets using parallel/distributed execution.
  • You want to compare gradient-boosting–based GRN inference (GRNBoost2) versus random-forest–based inference (GENIE3).
  • You need a reproducible, scriptable pipeline to generate a ranked network edge list from expression data.

Key Features

  • GRN inference from gene expression data using GRNBoost2 (gradient boosting) or GENIE3 (random forest).
  • Scalable execution via Dask, from a single machine to multi-node clusters.
  • Command-line workflow for generating a GRN edge list from a tabular expression matrix.
  • Algorithm guidance and comparison: see references/algorithms.md.
  • Distributed setup notes: see references/distributed_computing.md.

Dependencies

  • arboreto
  • dask
  • distributed
  • pandas
  • scipy
  • scikit-learn

Example Usage

Run GRN inference from an expression matrix (TSV) and write the inferred network to an output file:

bash
python scripts/infer_network.py \
  --input expression_data.tsv \
  --output network.tsv \
  --algo grnboost2

To use the alternative algorithm:

bash
python scripts/infer_network.py \
  --input expression_data.tsv \
  --output network.tsv \
  --algo genie3

Implementation Details

  • Input/Output

    • Input: a gene expression matrix (e.g., TSV) where rows typically represent samples/cells and columns represent genes (exact expectations depend on scripts/infer_network.py).
    • Output: a ranked edge list representing inferred regulatory relationships (TF → target) with an importance/weight score.

  • Algorithms

    • GRNBoost2: uses gradient boosting to estimate feature importance of candidate regulators for each target gene; generally preferred for larger datasets due to speed and scalability.
    • GENIE3: uses random forests to compute regulator importance per target gene; a classic baseline for GRN inference.
    • For a detailed comparison and practical guidance, refer to references/algorithms.md.

  • Parallel/Distributed Execution

    • Computation is parallelized with Dask, enabling scaling from local multi-core execution to distributed clusters.
    • Cluster configuration and deployment considerations are documented in references/distributed_computing.md.

  • Key Parameters

    • --algo: selects the inference method (grnboost2 or genie3), affecting runtime and model behavior.
    • Additional runtime/cluster parameters (if exposed by the script) typically control Dask scheduling, worker counts, and resource usage.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in scientific-skills/Evidence Insight/arboreto of aipoch/medical-research-skills.

  • SKILL.md
  • arboreto_audit_result_v1.json
  • references/algorithms.md
  • references/distributed_computing.md
  • scripts/infer_network.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Arboreto next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Arboreto compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Arboreto this skillaipoch/medical-research-skills2k—~802Automated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

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Categories

Questions about Arboreto

What does Arboreto do?

Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships. Arboreto is an agent skill from aipoch/medical-research-skills. Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships.

When should I use Arboreto?

Arboreto fits situations like: single-cell RNA-seq to identify TF→target regulatory relationships; tasks that involve Bioinformatics.

How do I install Arboreto in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill arboreto -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/arboreto in aipoch/medical-research-skills) into .claude/skills/arboreto in your project. Claude Code loads it when a task matches its description.

How do I install Arboreto in Codex?

Run `npx skills add aipoch/medical-research-skills --skill arboreto -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/arboreto in aipoch/medical-research-skills) into .agents/skills/arboreto in your project. Codex loads it when a task matches its description.

Can I use Arboreto in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill arboreto -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/arboreto, .gemini/skills/arboreto, .github/skills/arboreto and .opencode/skills/arboreto in your project.

What does Arboreto need to run?

Going by SKILL.md and its folder, Arboreto needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Arboreto access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Arboreto safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Arboreto use?

Arboreto is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Arboreto use?

About 802 tokens (SKILL.md is roughly 3.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 233 tokens, read only when the agent opens those files.

What are the alternatives to Arboreto?

Skills that share tags, products or a category with Arboreto: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Arboreto?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,978 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.