tooluniverse-multi-omics-integration

Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine. Performs cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching. Coordinates ToolUniverse skills for expression data (RNA-seq), epigenomics (methylation, ChIP-seq), variants (SNVs, CNVs), protein interactions, and pathway enrichment. Use when analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers, or conducting systems biology research that requires coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.

Quality

82%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

65%

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

The body delivers strong, mostly executable code and a clearly sequenced 8-phase workflow, but is verbose (explains known concepts, padded stubs, a full sample report) and monolithic with no progressive file-based disclosure or explicit validation feedback loops.

Suggestions

Trim explanations of concepts Claude already knows (e.g., what MOFA+ does, expected correlation biology, the full Phase 8 sample report) and expand the empty 'Advanced Analysis Patterns' stubs or remove them.

Add explicit validate→fix→retry checkpoints for fragile operations (QC, batch-effect/ComBat normalization, clustering convergence) rather than listing steps linearly.

Move large detail blocks (per-phase code, the example report, pathway/clustering methods) into one-level-deep reference files (e.g., PHASES.md, REPORT_TEMPLATE.md) and keep SKILL.md as a concise overview with signaled links.

Dimension	Reasoning	Score
Conciseness	The ~700-line body explains concepts Claude already knows ('MOFA+ identifies latent factors that explain variation across omics', docstring biology primers, a fully written-out example report) and includes near-empty stub sections, so it is mostly efficient but padded and could be tightened.	2 / 3
Actionability	Provides multiple complete, executable functions with real imports (pandas, sklearn, scipy.stats) and concrete ToolUniverse calls (enrichr_enrich with KEGG_2021_Human), making the bulk copy-paste ready; minor gaps in MOFA+ (conceptual) and SNF (no code) keep it from flawless but the dominant examples are fully executable.	3 / 3
Workflow Clarity	The 8-phase sequence is clearly laid out with an ASCII flow and per-phase objectives, but validation checkpoints and explicit validate→fix→retry feedback loops for fragile batch/normalization operations are missing or only implicit, matching the 'sequence present but checkpoints missing' anchor.	2 / 3
Progressive Disclosure	Content is well-organized into clear sections but everything is inline in one ~700-line file with no local reference files to split out (only external DOIs and a pointer to other skills), so content that should be separate stays inline rather than being one level deep.	2 / 3
	Total	9 / 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 strong: third-person voice, concrete capabilities, broad natural trigger terms, and an explicit 'Use when' clause answering both what and when. It is clearly distinguishable from related single-omics skills.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching' plus skill coordination across named omics types — matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Explicitly answers both 'what' (Performs cross-omics correlation, clustering, integration, sample matching; Coordinates ToolUniverse skills) and 'when' via a clear 'Use when analyzing multi-omics datasets...' clause, satisfying the both-what-and-when anchor.	3 / 3
Trigger Term Quality	Covers natural user phrasing — 'analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers' — giving good coverage of terms a user would actually say.	3 / 3
Distinctiveness Conflict Risk	Targets a clear niche — coordinated multi-omics integration across specialized ToolUniverse skills — with distinct triggers unlikely to fire for single-omics skills, matching the 'clear niche with distinct triggers' anchor.	3 / 3
	Total	12 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (704 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: wu-yc/LabClaw
Path: skills/bio/tooluniverse-multi-omics-integration/SKILL.md
Commit: df37802

Reviewed: 2 days ago

Table of Contents

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