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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.

66

Quality

83%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

The body is an exemplarily lean, domain-expert-oriented checklist: concrete runner commands with flag semantics, explicit preconditions and decision points, and a precise output contract. Weaknesses are minor — no example input-file formats and no explicit failure/retry handling around the preflight and QC gates.

Suggestions

Add a one-line example of the expected contrasts.tsv and sample_metadata.tsv format so the runner command is fully copy-paste ready.

Specify what to do when the preflight install plan or QC checks fail (fix/re-run loop), closing the last workflow-validation gap.

State how to locate the plugin-owned scripts (e.g., relative to the plugin install directory) so the commands work outside the plugin checkout.

DimensionReasoningScore

Conciseness

The body is lean imperative bullets with zero re-teaching of concepts Claude already knows (no RNA-seq or DESeq2 tutorials), and no padding — every section carries operational content. Matches the 'lean and efficient; every token earns its place' anchor.

5 / 5

Actionability

Concrete, executable commands are provided (run_bulk_rnaseq_de.py with flags, preflight with --emit-install-plan) plus explicit decision points and output artifacts. Minor gaps keep it below copy-paste-ready 5: no example contrasts.tsv/metadata row, and script paths assume the plugin layout without stating how to locate them.

4 / 5

Workflow Clarity

The sequence is clear — confirm inputs, Preconditions gate (do not start until...), preflight, execute, outputs — with explicit validation checkpoints (replication, metadata matching, PCA outlier review) for a batch operation. The fix-and-retry loop when preflight or QC checks fail is only implicit, so it fits 'clear sequence with most checkpoints; minor validation gaps' rather than the 5 anchor with explicit feedback loops.

4 / 5

Progressive Disclosure

No bundle files exist and nothing inlined clearly belongs in a separate file; the body is a well-organized set of short sections (Essential Inputs, Preconditions, Route, Decision Points, Outputs) that serves as a complete overview. Per the rubric's simple-skill note, well-organized sections with no need for external references warrant a 5.

5 / 5

Total

18

/

20

Passed

Description

71%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.

The description is specific and keyword-rich, accurately scoping the DE-analysis workflow from count matrices with concrete check dimensions. Its main weakness is the absence of an explicit 'Use when...' trigger clause, which both caps completeness and slightly weakens when-to-fire guidance.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user has raw or normalized count matrices and wants differential expression, DE gene tables, contrasts, or QC plots for bulk RNA-seq."

Include the tool names users naturally say — DESeq2, edgeR, limma-voom — as trigger terms in the description.

Mention paired/batched designs or ranked/gene tables phrasing users commonly use to broaden natural trigger coverage.

DimensionReasoningScore

Specificity

"Run or plan bulk RNA-seq differential-expression analysis from count matrices" names the domain and action, and the enumerated checks (replicate, design formula, contrast, batch, normalization, QC plot, result-table) cover the workflow comprehensively. No minor gaps in coverage keep it below the comprehensive-coverage anchor.

5 / 5

Completeness

The 'what' is explicit and concrete (run/plan DE analysis from count matrices with listed checks), but there is no 'Use when...' clause or equivalent explicit trigger guidance; the when is only weakly implied by the enumeration. Per the rubric guideline, a missing 'Use when' clause caps completeness at 3.

3 / 5

Trigger Term Quality

Natural phrases like "bulk RNA-seq", "differential-expression", "count matrices", "contrast", "batch", and "QC plot" are present, but commonly said tool names (DESeq2, edgeR, limma-voom) and shorthand like "DE genes" are missing. This fits 'good keyword coverage; a few natural terms missing' rather than the fully comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

"bulk RNA-seq differential-expression" is a clear niche with distinct triggers, but as a plugin skill it has minor overlap risk with closely related NGS skills (single-cell RNA-seq, general preprocessing). Fits 'mostly distinct; minor overlap risk' rather than the clear-niche 5 anchor.

4 / 5

Total

16

/

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.

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

Reviewed

Table of Contents

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