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

89%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

86%Weight 40%Scale 1-5

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

A highly actionable, well-organized skill body with excellent progressive disclosure and copy-paste-ready code. Tightening the repetitive use-case examples and adding a light output-validation checkpoint would push it to full marks.

Suggestions

Consolidate the three Common Use Cases examples (scRNA-seq, bulk RNA-seq with TF filtering, comparative analysis) to reduce repeated `if __name__ == '__main__':` boilerplate, or move the variations into a reference file.

Add an explicit verification checkpoint after inference, e.g. assert the output network is non-empty and `importance` is finite, so the workflow has a validate→fix feedback loop for this long-running batch operation.

The 'Citing Scientific Agent Skills' section embeds version-specific guidance that could become stale; consider trimming to a one-line citation pointer to keep the body lean.

Dimension	Reasoning	Score
Conciseness	Largely efficient and assumes competence, but the Common Use Cases section repeats the same `if __name__ == '__main__':` boilerplate and `grnboost2` patterns three times with minor variation, which could be tightened.	4 / 5
Actionability	Fully executable copy-paste code throughout, a ready-to-run script with concrete CLI invocation (`python scripts/basic_grn_inference.py ... --seed 777 --limit 5000`), and specific examples covering the common cases.	5 / 5
Workflow Clarity	Sequencing is clear with explicit critical guards and seed/reproducibility steps, but there are no explicit validation or verify-output checkpoints for the long-running inference (e.g., checking that the network file is non-empty or importance values are sane).	4 / 5
Progressive Disclosure	Clear overview with well-signaled one-level-deep references to real bundle files (`references/basic_inference.md`, `references/algorithms.md`, `references/distributed_computing.md`) and a ready-to-run script, with content appropriately split and easy to navigate.	5 / 5
	Total	18 / 20 Passed

Description

92%Weight 40%Scale 1-5

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

A strong, well-structured description that concretely states capabilities and provides explicit, natural trigger guidance. Minor expansion of trigger synonyms would make keyword coverage comprehensive.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Infer gene regulatory networks', 'identify transcription factor-target gene relationships and regulatory interactions', 'Supports distributed computation' — giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	Explicitly states the 'what' (infer GRNs via GRNBoost2/GENIE3, identify TF-target relationships) and a concrete 'when' clause ('Use when analyzing transcriptomics data...'), satisfying both halves with specific triggers.	5 / 5
Trigger Term Quality	Includes strong natural terms users would say ('gene regulatory networks', 'transcriptomics data', 'bulk RNA-seq', 'single-cell RNA-seq'), but omits common synonyms/extensions like 'GRNs', 'scRNA-seq', or '.tsv'.	4 / 5
Distinctiveness Conflict Risk	The GRN-inference-from-transcriptomics niche with named algorithms (GRNBoost2, GENIE3) is clearly distinct from generic bioinformatics or data skills, with minimal conflict risk.	5 / 5
	Total	19 / 20 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: 49c6e97

Reviewed: 2 days ago

Table of Contents

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