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?

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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 11 hours ago

Table of Contents

Discovery Implementation Validation

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