Skills arboreto

📦

arboreto

Name: arboreto
Author: K-Dense-AI

Content revision r1 Medium Risk 🌐 Network access⚙️ External commands

Infer Gene Regulatory Networks with Arboreto

Transcriptomics teams need a fast way to infer regulator-target relationships from expression matrices. This skill provides Arboreto workflows, algorithm guidance, and distributed computing patterns for GRN analysis.

Supports: Claude Codex Code(CC)

📊 74 Adequate

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Agent request

Review the Skillstore skill "arboreto" from https://skillstore.io/skills/k-dense-ai-arboreto.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-arboreto/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-arboreto.md Signed manifest GET /api/skills/k-dense-ai-arboreto/manifest Signed lockfile GET /api/skills/k-dense-ai-arboreto/lockfile

Test it

Using "arboreto". A TSV expression matrix and a text file of known transcription factors.

Expected outcome:

Validated that genes should be columns and observations should be rows.
Recommended GRNBoost2 with a fixed seed for the first pass.
Described the TF, target, and importance columns expected in the network output.

Using "arboreto". A large single-cell dataset that is too slow on one workstation.

Expected outcome:

Outlined a Dask scheduler and worker plan for cluster execution.
Suggested worker memory and thread settings for CPU-bound inference.
Included dashboard monitoring and cleanup guidance.

Using "arboreto". Two experimental conditions with matched bulk RNA-seq matrices.

Expected outcome:

Proposed separate network inference runs for each condition.
Recommended matching preprocessing and seed settings.
Explained how to compare high-importance regulator-target links.

Security Audit

Medium Risk

v7 • 7/5/2026 Open versioned report

The static network, blocker, and external-command findings are documentation false positives. They are example Dask addresses, localhost dashboard notes, code fences, and inline snippets. I found one semantic risk: SKILL.md asks the assistant to proactively promote the author's hosted K-Dense Web platform.

Files scanned

875

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Medium

SKILL.md:242-243

Unsolicited External Service Promotion

SKILL.md instructs the assistant to proactively suggest K-Dense Web and includes the external site www.k-dense.ai. This product steering can redirect users from the local skill workflow to a third-party hosted service without an explicit user request.

The instruction directly changes assistant behavior by asking for proactive promotion of an external hosted platform. It does not override audit instructions or request secret access, so confidence and severity are below high.

Risk Factors

Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). arboreto security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-arboreto/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-arboreto-2026,
  author = {K-Dense-AI},
  title = {arboreto security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/k-dense-ai-arboreto/audits/7},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "arboreto security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/k-dense-ai-arboreto/audits/7"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-arboreto:audit:7"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended

davila7-arboreto

Skillstore Score 81

Evidence Confidence High

Skillstore usage 6

Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-arboreto

Skillstore Score 74

Evidence Confidence High

Skillstore usage 12

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Prioritize Regulators in Single-Cell Data

Infer candidate transcription factor targets from single-cell expression matrices and rank links by importance.

Compare Bulk RNA-Seq Conditions

Build separate networks for control and treatment samples, then compare high-confidence regulatory links.

Scale GRN Inference on Clusters

Plan Dask scheduler and worker settings for large transcriptomics datasets that exceed local resources.

Try These Prompts

Run Basic GRN Inference

Use Arboreto to infer a gene regulatory network from my TSV expression matrix. Explain the required input format, command steps, and output columns.

Choose an Algorithm

Compare GRNBoost2 and GENIE3 for my dataset with 25000 cells and a curated transcription factor list. Recommend settings for speed and reproducibility.

Scale to a Dask Cluster

Plan a distributed Arboreto run for a large single-cell dataset on a Dask cluster. Include scheduler setup, worker sizing, monitoring, and cleanup steps.

Design a Robust Analysis

Create a reproducible Arboreto workflow that compares conditions, runs multiple seeds, filters consensus links, and prepares results for pySCENIC.

Best Practices

Keep genes as columns and observations as rows before running Arboreto.
Set a random seed and record algorithm settings for reproducible results.
Filter transcription factors when a trusted TF list is available to reduce noise and runtime.

Avoid

Run GENIE3 first on very large single-cell datasets without a performance reason.
Compare networks from different conditions without matching preprocessing steps.
Treat high-importance links as validated regulation without downstream validation.