Mostly actionable with code, but the ~530-line body repeats the same Media+Strain example across Step 1.5, Step 2.5, and the multi-factor section, and includes explanatory asides ("Why this matters") and a long rpy2/R-clusterProfiler block that pad the token budget.

Core PyDESeq2 code blocks are executable and copy-paste ready, but the body imports from and links to bundle files (e.g. `from scripts.load_count_matrix import ...`, 9 references/*.md, 2 scripts/*.py) that do not exist, so following the skill breaks at those points.

A clear Step 1–7 sequence with explicit critical callouts ("CRITICAL FIRST STEP", "ALWAYS inspect metadata"), a decision tree, an error-handling table, and a validation checklist providing feedback loops for recovery.

References are clearly signaled and only one level deep, but no bundle files actually exist despite 11 inline links, and substantial detail that belongs in those separate files remains inline in the 530-line SKILL.md, leaving the disclosure structure partly non-functional.

Lists multiple concrete actions in third person ("Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering"; "Handles multi-factor designs, multiple contrasts, batch effects"), matching the multiple-specific-actions anchor.

Explicitly answers both what it does and when to use it via a "Use when analyzing RNA-seq count matrices, identifying DEGs..." clause, matching the full what-and-when anchor.

Covers natural user phrasing such as "RNA-seq count matrices", "identifying DEGs", "differential expression", and "gene expression changes"; the mild buzzword "statistical rigor" is outweighed by domain-natural terms.

The narrow RNA-seq/PyDESeq2 niche with specific triggers (DESeq2, DEGs, padj, log2FC) is clearly distinguishable and unlikely to fire for unrelated skills.