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.
90
86%
Does it follow best practices?
Impact
100%
1.31xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
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 clearly defines a specific bioinformatics capability with concrete algorithms, data types, and use cases. It uses proper third-person voice, includes an explicit 'Use when' clause with natural domain-specific trigger terms, and occupies a highly distinctive niche that minimizes conflict risk with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Infer gene regulatory networks (GRNs)', 'scalable algorithms (GRNBoost2, GENIE3)', 'identify transcription factor-target gene relationships and regulatory interactions', 'distributed computation for large-scale datasets'. | 3 / 3 |
Completeness | Clearly answers both 'what' (infer GRNs using GRNBoost2/GENIE3, identify TF-target relationships, supports distributed computation) and 'when' (explicit 'Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships'). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms a bioinformatics user would say: 'gene regulatory networks', 'GRNs', 'gene expression data', 'GRNBoost2', 'GENIE3', 'transcriptomics', 'bulk RNA-seq', 'single-cell RNA-seq', 'transcription factor', 'regulatory interactions'. These are the exact terms domain users would use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche — gene regulatory network inference is a very specific bioinformatics task. The mention of specific algorithms (GRNBoost2, GENIE3) and specific data types (RNA-seq, scRNA-seq) makes it extremely unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
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—nearly all guidance is backed by executable code examples with proper context (the __main__ guard is well-emphasized). The progressive disclosure structure is well done with clear references to deeper materials. The main weaknesses are some content redundancy between Quick Start and Common Use Cases, and a lack of explicit validation steps in the workflows (e.g., checking data shape, verifying output is non-empty).
Suggestions
Remove the duplicate installation section and consolidate the Quick Start example with the Common Use Cases to reduce redundancy.
Add explicit validation checkpoints to workflows, such as verifying the expression matrix shape before inference and checking that the output network is non-empty after inference.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient but includes some redundancy—the Quick Start and Common Use Cases sections overlap significantly, and the algorithm comparison section explains things Claude could infer. The installation section appears twice. Some use cases (comparative analysis) add little beyond what's already shown. | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste ready Python code with proper imports, complete function calls, and concrete file I/O patterns. The CLI script invocation, distributed computing examples, and TF filtering examples are all specific and actionable. | 3 / 3 |
Workflow Clarity | While individual steps are clear, there are no explicit validation checkpoints in the workflows. For a data analysis pipeline that could produce empty or incorrect results, there's no guidance on verifying output quality, checking data format before running, or handling intermediate failures beyond brief troubleshooting notes at the end. | 2 / 3 |
Progressive Disclosure | The skill has a clear Quick Start section followed by well-organized capability sections, each pointing to one-level-deep reference files (references/basic_inference.md, references/algorithms.md, references/distributed_computing.md) and a ready-to-run script. Navigation is clearly signaled with bold labels. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
90%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
- K-Dense-AI/claude-scientific-skills
- Commit
b58ad7e
- Reviewed
Table of Contents
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.