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tooluniverse-metagenomics-analysis

Microbiome and metagenomics analysis using MGnify, GTDB taxonomy, ENA sequencing data, and EuropePMC literature. Covers taxonomic classification, genome quality assessment, biome-clinical phenotype linkage, and pathway interpretation. Use for amplicon/shotgun metagenomics study analysis.

64

Quality

76%

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SecuritybySnyk

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tessl review fix ./plugin/skills/tooluniverse-metagenomics-analysis/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

75%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 dense, actionable body that sequences an eight-phase pipeline with concrete tool-call syntax and parameter gotchas, supported by fallback loops and edge-case guidance. Minor gaps in example code and explicit validation checkpoints keep it just short of the top anchor.

Suggestions

Add one copy-pasteable Python snippet under 'COMPUTE, DON'T DESCRIBE' (e.g. a pandas/scipy example over MGnify results) to match the actionability of the tool-call guidance.

Insert an explicit validation checkpoint between phases (e.g. 'confirm GTDB taxonomy returned before proceeding to quality assessment') to strengthen workflow feedback loops.

Consider moving the KEGG pathway table and MIMAG quality tiers into a references file to let the SKILL.md body breathe as an overview, improving progressive disclosure.

DimensionReasoningScore

Conciseness

Mostly lean with bullets, tables, and code blocks and no padding about concepts Claude already knows; the 'Guiding principles' list is mildly motivational but brief enough to keep efficiency high.

4 / 5

Actionability

Gives concrete tool calls with exact parameter syntax and gotchas (e.g. kegg_search_pathway(keyword=...) NOT query, hsa/ko prefixes, study_title="*IBD*"), but the 'COMPUTE, DON'T DESCRIBE' directive urges Python execution without providing any example code.

4 / 5

Workflow Clarity

A clear eight-phase sequence with per-phase notes and explicit fallback loops (ENA to MGnify, GMrepo MeSH/taxon-ID) is present, though there are no explicit result-validation checkpoints before proceeding to the next phase.

4 / 5

Progressive Disclosure

Well-organized sections with no nested references and all content inline at roughly 95 lines; with no bundle files and content concise enough inline, structure is good but exceeds the simple-skill threshold so it does not reach the top anchor.

4 / 5

Total

16

/

20

Passed

Description

78%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 specific, well-scoped description that clearly states what the skill does and when to use it, anchored by distinctive database names that set it apart from generic bioinformatics skills. Trigger-term coverage and the brevity of the 'when' clause leave minor room for improvement.

DimensionReasoningScore

Specificity

Lists several concrete actions (taxonomic classification, genome quality assessment, biome-clinical phenotype linkage, pathway interpretation) plus named data sources, but 'biome-clinical phenotype linkage' is jargony and literature search is implied rather than enumerated.

4 / 5

Completeness

Provides a clear 'what' (analysis scope plus four capabilities) and an explicit 'Use for amplicon/shotgun metagenomics study analysis' trigger, but the 'when' is a single thin clause that could be richer with more trigger phrases.

4 / 5

Trigger Term Quality

Natural domain terms ('microbiome', 'metagenomics', 'amplicon', 'shotgun metagenomics') are present, but common synonyms like 16S, MAGs, and taxonomic profiling are missing.

4 / 5

Distinctiveness Conflict Risk

Names distinctive databases (MGnify, GTDB, ENA, EuropePMC) combined with metagenomics/microbiome focus, carving an unmistakable niche with minimal conflict risk.

5 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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