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deg-screening-analysis

Use when screening differentially expressed genes from a bulk expression matrix between two user-specified groups, producing DEG tables, a volcano plot, and a clustered heatmap. Triggers include DEG analysis, volcano plot, clustered heatmap, limma-based two-group comparison, and case-vs-control screening. NOT for single-cell RNA-seq, multi-group contrasts, count-model workflows such as DESeq2/edgeR, or non-expression omics data.

72

Quality

89%

Does it follow best practices?

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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 a well-structured, actionable CLI reference with strong progressive disclosure and concrete validation in the workflow. The main gap is the absence of an explicit validate→fix→retry feedback loop for the batch analysis run.

Suggestions

Add an explicit post-run feedback loop to the Workflow section (e.g., on SKILL_EMPTY_DATA or SKILL_SAMPLE_MISMATCH: diagnose, fix inputs, rerun) to strengthen validation checkpoints for this batch operation.

De-duplicate the runnable command between the Usage and Testing sections, or have Testing reference the Usage command with only the differing output_dir, to trim redundant tokens.

Cross-link the Error Handling table's fixes back to the matching Workflow step so error recovery paths are explicit rather than implied.

DimensionReasoningScore

Conciseness

Lean body that assumes Claude's competence (no tutorials on limma/DEG/volcano plots) with every section earning its place, but the Usage block and Testing smoke-test command overlap somewhat and could be tightened.

4 / 5

Actionability

Copy-paste-ready Rscript commands, a complete argument table with types/defaults/required flags, an output-file table, and executable test commands fully cover the common cases.

5 / 5

Workflow Clarity

Four steps are clearly sequenced with a concrete validation-first Step 1 and an error-handling table, but there is no explicit post-run validate→fix→retry feedback loop, leaving a minor checkpoint gap for a batch operation.

4 / 5

Progressive Disclosure

A dedicated "When to Read External Files" table clearly signals one-level-deep references to real bundle files (algorithm.md, main.R, troubleshooting.md, cli-guide.md, tests/data/), with the body kept as an overview and detail split into referenced files.

5 / 5

Total

18

/

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.

The description is specific, well-triggered, and clearly bounded, explicitly covering what the skill does and when to use it while ruling out adjacent workflows. Only minor weakness is trigger-term breadth relative to the most comprehensive anchor.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ("screening differentially expressed genes", "producing DEG tables, a volcano plot, and a clustered heatmap") with a named method (limma), giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both what (DEG screening producing tables, volcano, heatmap) and when ("Use when... Triggers include...") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Strong natural trigger phrases ("DEG analysis", "volcano plot", "clustered heatmap", "case-vs-control screening") that researchers would actually say, but missing common synonyms or file-extension-style terms, so just below comprehensive.

4 / 5

Distinctiveness Conflict Risk

Clear niche (bulk two-group limma DEG) reinforced by an explicit NOT-for boundary (single-cell, multi-group, DESeq2/edgeR, non-expression omics), minimizing conflict risk.

5 / 5

Total

19

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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