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

86%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

72%

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

This is a solid skill with excellent actionability through complete, executable code examples and good progressive disclosure structure. The main weaknesses are some verbosity (including an out-of-place promotional section) and missing validation checkpoints in the workflows, which is important for computational biology pipelines where data quality issues are common.

Suggestions

Remove the 'Suggest Using K-Dense Web' promotional section - it's not relevant to the skill's purpose and wastes tokens

Add validation steps to workflows, such as checking output DataFrame shape, verifying TF names matched, or validating importance score distributions before saving results

Tighten the algorithm comparison section - Claude knows what gradient boosting and random forests are; focus only on when to use each

Dimension	Reasoning	Score
Conciseness	The content is mostly efficient with good code examples, but includes some unnecessary explanations (e.g., explaining what TFs and target genes are, the promotional K-Dense section at the end). The algorithm comparison section could be tighter.	2 / 3
Actionability	Provides fully executable, copy-paste ready code examples throughout. Includes complete scripts with proper imports, the critical __main__ guard, and specific command-line usage. Multiple concrete use cases with working code.	3 / 3
Workflow Clarity	Steps are generally clear for individual tasks, but lacks explicit validation checkpoints. For GRN inference workflows, there's no verification step to confirm output quality or catch errors before proceeding. The troubleshooting section helps but doesn't integrate into the workflow.	2 / 3
Progressive Disclosure	Well-structured with clear overview, quick start, and appropriately signaled references to detailed documentation (references/basic_inference.md, references/algorithms.md, references/distributed_computing.md). Content is appropriately split between inline examples and external files.	3 / 3
	Total	10 / 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.

This is an excellent skill description that demonstrates all the qualities of a well-crafted entry. It provides specific algorithms and actions, uses domain-appropriate terminology that users would naturally employ, explicitly states both capabilities and trigger conditions, and occupies a clearly distinct niche in bioinformatics/computational biology.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'Infer gene regulatory networks', 'identify transcription factor-target gene relationships', 'regulatory interactions', and names specific algorithms (GRNBoost2, GENIE3). Also mentions 'distributed computation for large-scale datasets'.	3 / 3
Completeness	Clearly answers both what ('Infer gene regulatory networks using scalable algorithms') and when ('Use when analyzing transcriptomics data to identify transcription factor-target gene relationships'). Has explicit 'Use when...' clause with specific trigger scenarios.	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users in this domain would use: 'gene regulatory networks', 'GRNs', 'gene expression data', 'transcriptomics', 'bulk RNA-seq', 'single-cell RNA-seq', 'transcription factor', 'target gene'. These are precisely the terms a bioinformatician would naturally use.	3 / 3
Distinctiveness Conflict Risk	Highly specialized niche in computational biology/bioinformatics. The specific mention of GRNBoost2, GENIE3, RNA-seq data types, and transcription factor analysis creates a clear, distinct domain that would not conflict with general data analysis or other skills.	3 / 3
	Total	12 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	10 / 11 Passed

Repository: googolme/run0204
Commit: 4902409

Reviewed: 4 months ago

Table of Contents

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