ngs-bulk-rnaseq

Dispatch bulk RNA-seq requests to FASTQ-to-count QC or count-matrix differential-expression skills using nf-core/rnaseq, STAR, Salmon, featureCounts, MultiQC, and R/Bioconductor workflows.

Quality

91%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

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

A well-structured, executable dispatcher skill body that is concise, actionable, and sequences a multi-step pipeline with explicit validation and gating checkpoints.

Dimension	Reasoning	Score
Conciseness	The body is lean and assumes Claude's competence: organized sections with executable commands and no concept explanations of what RNA-seq or the tools are.	3 / 3
Actionability	Provides fully executable nextflow and python invocations with real flags, sample-sheet paths, and a concrete real-run skeleton that is copy-paste ready.	3 / 3
Workflow Clarity	Clear sequence (confirm inputs → preflight → kickoff → gate before DE) with explicit verification checkpoints (preflight --emit-install-plan, the Essential Inputs confirm list, and the 'only start DE after confirming' gating).	3 / 3
Progressive Disclosure	No bundle files are present and the content is self-contained across well-organized sections (Essential Inputs, Dispatch, Preflight, Kickoff, Downstream) with no nested references, satisfying the no-external-reference case.	3 / 3
	Total	12 / 12 Passed

Description

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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, tool-rich description with strong trigger terms and a distinct dispatcher niche, weakened only by the absence of an explicit 'Use when...' trigger clause.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when a request involves bulk RNA-seq FASTQ/BAM processing or count-matrix differential-expression analysis.', to lift completeness to 3.

Dimension	Reasoning	Score
Specificity	Names concrete dispatch actions ('FASTQ-to-count QC', 'count-matrix differential-expression') and a specific toolchain (nf-core/rnaseq, STAR, Salmon, featureCounts, MultiQC, R/Bioconductor), matching the multiple-specific-actions anchor.	3 / 3
Completeness	It clearly states what the skill does (dispatch to QC or DE sub-skills) but lacks an explicit 'Use when...' trigger clause, so completeness is capped at 2 per the judging guidelines.	2 / 3
Trigger Term Quality	Natural terms a bulk RNA-seq user would actually say ('bulk RNA-seq', 'FASTQ', 'count', 'differential-expression', 'QC') are well covered, not buried in jargon.	3 / 3
Distinctiveness Conflict Risk	It carves a clear niche as the bulk RNA-seq dispatcher routing to two named sub-skills, making conflicts with other skills unlikely.	3 / 3
	Total	11 / 12 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: openai/plugins
Path: plugins/ngs-analysis/skills/ngs-bulk-rnaseq/SKILL.md
Commit: 11c74d6

Reviewed: 17 days ago

Table of Contents

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