ngs-bulk-rnaseq-differential-expression

Run or plan bulk RNA-seq differential-expression analysis from count matrices with replicate, design formula, contrast, batch, normalization, QC plot, and result-table checks.

Quality

85%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

87%Weight 40%Scale 1-3

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

A concise, highly actionable skill body with clean section structure; the main gap is the lack of an explicit error-recovery feedback loop for batch operations.

Suggestions

Add an explicit feedback loop after preflight, e.g. 'If preflight reports missing packages, install per the plan and rerun preflight before --execute.'

Include a verification step for outputs, such as checking that each contrast produced a table or a .not_tested.tsv stub before declaring the run complete.

Dimension	Reasoning	Score
Conciseness	Lean bullet structure with concrete commands and flag documentation; avoids explaining concepts Claude already knows (e.g., what DESeq2 or RNA-seq is), so every token earns its place.	3 / 3
Actionability	Provides fully executable runner and preflight commands with documented flags (--method, --input-mode, --contrasts, --execute), copy-paste ready rather than pseudocode.	3 / 3
Workflow Clarity	Clear sequence with preflight and preconditions as explicit gates, but for a batch operation there is no explicit validate->fix->retry feedback loop, which caps workflow clarity at 2.	2 / 3
Progressive Disclosure	Self-contained SKILL.md with well-organized sections (Essential Inputs, Preconditions, Route, Decision Points, Outputs) and no nested or deep references; no external bundle files are needed.	3 / 3
	Total	11 / 12 Passed

Description

82%Weight 40%Scale 1-3

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 with strong natural trigger terms and low conflict risk, weakened only by the absence of an explicit 'Use when...' clause.

Suggestions

Append an explicit trigger clause, e.g. 'Use when the user has a count matrix and wants differential expression, contrasts, or QC plots from bulk RNA-seq data.'

Mention common user phrasings such as 'DESeq2', 'edgeR', 'volcano plot', or 'ranked gene list' to broaden natural trigger coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions and analytical components — 'Run or plan bulk RNA-seq differential-expression analysis', 'QC plot', 'result-table checks', 'normalization', 'contrast', 'batch' — matching the anchor that names several specific concrete actions.	3 / 3
Completeness	Clearly answers 'what' the skill does, but lacks any 'Use when...' clause or equivalent explicit trigger guidance, which per the judging guidelines caps completeness at 2.	2 / 3
Trigger Term Quality	Contains natural domain terms a user would actually say — 'bulk RNA-seq', 'differential expression', 'count matrices', 'QC plot', 'contrast', 'replicate' — giving good coverage of natural keywords.	3 / 3
Distinctiveness Conflict Risk	Targets a precise niche — bulk RNA-seq differential expression from count matrices — with domain-specific triggers that are unlikely to overlap with unrelated skills.	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-differential-expression/SKILL.md
Commit: 11c74d6

Reviewed: 17 days ago

Table of Contents

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