The ~840-line body contains executable code but is padded with verbose docstrings ('Load MaxQuant proteinGroups.txt file. Returns: DataFrame...'), explanatory comments ('# Assume missing = low abundance (MNAR assumption)'), and a ~65-line illustrative report template with hardcoded example numbers (e.g. '5,432 proteins', 'MYC (log2FC=3.2)') that do not earn their tokens; it is not score 3 because much could be tightened, and not score 1 because the core code is concrete and actionable rather than abstract filler.

Most phases give executable Python using real libraries (pandas, scipy, sklearn, statsmodels, networkx), but several blocks are incomplete stubs: `analyze_phosphosites` returns undefined `phospho_results` after a '# ... perform differential analysis ...' ellipsis, `protein_complex_enrichment` is a `pass` body, `integrate_with_multiomics` returns an undefined `integrated_analysis`, and `predict_kinases` calls a self-described 'hypothetical' tool; this matches score 2 (some concrete guidance but incomplete) and is not score 3 because key pieces are pseudocode/stubs.

The eight-phase pipeline is clearly sequenced with an ASCII workflow diagram, but explicit validation checkpoints are absent for a batch analytical operation — phases proceed with implicit assumptions (e.g. 'Should see similar median' guidance) and no 'validate QC before proceeding' gate; per the rubric's batch-operation guidance this caps the score at 2 rather than 3, and it is not 1 because the sequence itself is well laid out.

No bundle files exist (no references/, scripts/, or assets/) and all content lives inline in one monolithic 840-line SKILL.md, including the full report template and per-phase code that should be split into separate referenced files; it is not score 3 because there are no one-level-deep references to separate materials, and not score 1 because the body is well-organized into labeled sections rather than a poorly organized wall of text.

Lists multiple concrete actions across two sentences — 'protein quantification, differential expression, post-translational modifications (PTMs), and protein-protein interactions' plus 'normalization, statistical analysis, pathway enrichment, and integration with transcriptomics' and named platforms (MaxQuant, Spectronaut, DIA-NN), matching the score-3 anchor of several specific concrete actions; it is not score 2 because the action list is comprehensive rather than partial.

It explicitly answers both 'what' (analyze... processes... performs...) and 'when' via an explicit 'Use when' clause, matching the score-3 anchor for clear what-and-when with explicit triggers; it is not score 2 because the when-guidance is explicit rather than implied.

The 'Use when' clause enumerates natural user phrasings — 'analyzing proteomics data', 'comparing protein abundance', 'identifying PTM changes', 'studying protein complexes', 'integrating protein and RNA data', 'protein biomarkers', 'quantitative proteomics experiments' — giving good coverage of terms a user would actually say; it is not score 2 because it covers common variations rather than just a few relevant keywords.

The proteomics/MS niche with distinctive triggers ('MaxQuant', 'Spectronaut', 'DIA-NN', 'proteomics data', 'protein abundance') is clearly distinguishable from generic genomics or multi-omics skills and unlikely to fire for the wrong skill; it is not score 2 because the triggers are domain-specific rather than broadly overlapping.