arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

Quality

93%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

87%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is lean, highly actionable, and well-structured with verified one-level-deep references. Its one weakness is workflow clarity: multi-step and consensus workflows lack explicit validation/verification checkpoints, leaving error-handling implicit.

Suggestions

Add an explicit validation/verification checkpoint to the multi-seed consensus workflow (e.g., assert the consensus DataFrame is non-empty and report how many TF-target links survived the importance threshold before saving).

For the comparative-analysis loop, add a brief validation note (e.g., verify each per-condition network has the expected TF/target columns and non-zero rows) so failure is caught early rather than silently producing empty outputs.

Consolidate the duplicate `uv pip install arboreto` (Quick Start and Installation sections) to remove redundancy and tighten token efficiency.

Dimension	Reasoning	Score
Conciseness	The body is code-forward and lean — terse bullets, executable snippets, minimal concept explanation — assuming Claude's competence; not 2 despite minor repetition of install commands because there is no padding of concepts Claude already knows.	3 / 3
Actionability	Fully executable, copy-paste-ready code with real signatures, `if __name__ == '__main__':` guards, and a concrete CLI invocation with flags; matches the top anchor.	3 / 3
Workflow Clarity	Sequences are clearly laid out (Quick Start, multi-seed consensus loop), but validation/verification checkpoints are only implicit and the consensus workflow lacks an explicit validate-fix-retry loop; not 3 because no explicit feedback loop, not 1 because steps are well sequenced.	2 / 3
Progressive Disclosure	Clear overview with one-level-deep, clearly signaled references to three verified reference files (basic_inference.md, algorithms.md, distributed_computing.md) and one ready-to-run script, with content appropriately split; matches the top anchor.	3 / 3
	Total	11 / 12 Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, well-triggered, complete, and distinctive — it names concrete actions, includes an explicit 'Use when' clause with natural domain terms, and occupies a clear niche. It is a strong, near-exemplar skill description.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Infer gene regulatory networks', 'identify transcription factor-target gene relationships and regulatory interactions', 'Supports distributed computation' — matching the highest anchor; not 2 because actions are named comprehensively rather than partially.	3 / 3
Completeness	Explicitly answers both 'what' (infer GRNs, identify TF-target relationships) and 'when' via a clear 'Use when analyzing transcriptomics data' trigger, matching the top anchor exactly.	3 / 3
Trigger Term Quality	Natural domain terms practitioners say appear directly — 'gene expression data', 'transcriptomics data', 'bulk RNA-seq', 'single-cell RNA-seq', 'GRNs' — giving good coverage; borderline but not 2 because these are the standard utterances of the target audience.	3 / 3
Distinctiveness Conflict Risk	A clear niche (GRN inference via GRNBoost2/GENIE3) with domain-specific triggers unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/arboreto/SKILL.md
Commit: 757b63b

Reviewed: about 12 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.