differential-expression-analysis

Use when analyzing bulk RNA-seq or microarray expression data to identify differentially expressed genes between two biological groups (case vs control), with volcano plots and heatmap visualization. NOT for:single-cell RNA-seq, methylation analysis, non-expression data.

Quality

80%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

68%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.

Highly actionable body with excellent CLI examples and input/output specs, weakened by redundant content and a missing intermediate validation checkpoint in the batch workflow. A broken reference to tests/data/ and Methods duplication of algorithm.md slightly reduce the progressive-disclosure score.

Suggestions

Remove the 'Implementation Checklist' section — it documents the skill's own development rather than guiding execution, adding tokens with no actionable value.

Add an explicit validation checkpoint between Step 2 and Step 4 (e.g. verify DE results are non-empty and sane before generating plots) to satisfy the batch-operation workflow bar.

Trim the inlined 'Methods' section to one-line pointers to references/algorithm.md to avoid duplication, and either create the referenced tests/data/ directory or remove that row from the file-reading table.

Dimension	Reasoning	Score
Conciseness	Mostly efficient table-based content, but the 'Implementation Checklist' is development meta-padding irrelevant to executing the task, and the 'Methods' section duplicates detail already in references/algorithm.md.	3 / 5
Actionability	Fully executable, copy-paste-ready CLI commands with all arguments, three concrete usage examples, real CSV input snippets, and an error-code-to-solution table covering common cases.	5 / 5
Workflow Clarity	A clear four-step sequence exists with input validation in Step 1 and an error-handling feedback table, but this batch analysis lacks intermediate validation checkpoints between steps, so the batch-operation cap at 3 applies.	3 / 5
Progressive Disclosure	A well-signaled 'When to Read External Files' table gives one-level-deep navigation to real bundle files, but the referenced 'tests/data/' path does not exist and the inlined Methods section overlaps algorithm.md.	4 / 5
	Total	15 / 20 Passed

Description

92%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 strong, specific description that clearly states both the capability and the trigger conditions, with helpful negative boundary guidance. Only minor gap is missing common synonyms and file extensions in the trigger terms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'identify differentially expressed genes between two biological groups', 'volcano plots and heatmap visualization' — plus the data types (bulk RNA-seq, microarray), giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	Explicitly answers both 'what' (identify DE genes, volcano plots, heatmap) and 'when' ('Use when analyzing bulk RNA-seq or microarray expression data... between two biological groups'), with concrete trigger phrases and an explicit 'NOT for' boundary.	5 / 5
Trigger Term Quality	Good natural keyword coverage ('bulk RNA-seq', 'microarray', 'expression data', 'differentially expressed genes', 'volcano plots', 'heatmap') plus exclusion terms, but misses common synonyms (e.g. 'DEG', 'DE analysis') and file extensions that would push it to 5.	4 / 5
Distinctiveness Conflict Risk	Clear niche (bulk RNA-seq/microarray differential expression) with distinct triggers and an explicit 'NOT for: single-cell RNA-seq, methylation analysis, non-expression data' exclusion, minimizing conflict with adjacent skills.	5 / 5
	Total	19 / 20 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: awesome-med-research-skills/Data Analysis/differential-expression-analysis/SKILL.md
Commit: f5ef65b

Reviewed: ~11 hours ago

Table of Contents

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