Conciseness

The body is dominated by terse tables and bullets with no concept padding, but the Workflow section narrates internal script steps Claude does not perform ('compute empirical Bayes moderated statistics') and the Testing smoke-test command duplicates the Usage example. Minor trimmable content keeps it just below the lean anchor.

Actionability

Concrete, executable guidance throughout: a full CLI invocation, an Arguments table with types and defaults, Output Files and Error Handling tables. However, the example commands reference 'tests/data/oa_exp.csv', 'tests/data/oa_group.csv', and 'tests/run_tests.R', which do not exist in the bundle, so the commands are not copy-paste ready as shipped.

Workflow Clarity

Step 1 is explicit input validation (file existence, non-empty matrix, sample ID overlap, minimum two samples per group), the Error Handling table maps every SKILL_* code to a fix, and the Testing section lists expected outputs and edge cases (empty DEG.csv, skipped heatmap). Sequence, checkpoints, and recovery loops are all present.

Progressive Disclosure

The 'When to Read External Files' table clearly signals one-level-deep references, and references/algorithm.md, references/cli-guide.md, references/troubleshooting.md, and scripts/main.R all exist. But the table and Usage section also point to tests/data/ and tests/run_tests.R, which are absent from the bundle — a dangling reference preventing a top score.

Specificity

The description lists multiple concrete actions — 'screening differentially expressed genes from a bulk expression matrix between two user-specified groups, producing DEG tables, a volcano plot, and a clustered heatmap' — with comprehensive coverage of the skill's outputs. No generic or padded language appears, matching the comprehensive-actions anchor.

Completeness

It explicitly answers both 'what' (two-group bulk DEG screening producing tables, volcano plot, clustered heatmap) and 'when' ('Use when screening... Triggers include...'), and adds explicit NOT-for boundaries. Both halves are concrete and explicit.

Trigger Term Quality

Triggers include natural phrases users would say — 'DEG analysis, volcano plot, clustered heatmap, limma-based two-group comparison, and case-vs-control screening' — but common synonyms such as 'differential expression analysis' or 'bulk RNA-seq' are missing. Keyword coverage is good but not fully comprehensive.

Distinctiveness Conflict Risk

The niche is clearly staked out (limma-based two-group bulk DEG screening) and the exclusion list — 'NOT for single-cell RNA-seq, multi-group contrasts, count-model workflows such as DESeq2/edgeR, or non-expression omics data' — sharply reduces overlap with adjacent skills.