Conciseness

The seaborn-specific sections (When to Use, Key Features, Example Usage, Implementation Details) are efficient and accurate, but a large tail of generic template sections — 'When Not to Use', 'Required Inputs', 'Deterministic Output Rules', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling', 'Completion Checklist', 'Scope Reminder' — is vague, repetitive padding that adds no seaborn-specific value and could be trimmed substantially, exceeding the 'minor' threshold for a 4.

Actionability

Provides a fully executable, copy-paste-ready example (load_dataset tips, set_theme, scatterplot with hue/style/size, catplot violin facets, correlation heatmap) covering the common relationship/categorical/correlation cases, plus specific parameter references (estimator=, errorbar=, bw_adjust, bins=, stat=, multiple=, sns.set_theme), matching the 5 anchor; the load_dataset internet caveat is flagged inline.

Workflow Clarity

A numbered 'Recommended Workflow' and 'Completion Checklist' exist, but the steps are generic template language ('Select the documented execution path', 'Produce the expected output using the documented file format') rather than a concrete seaborn-specific procedure (load data → choose axes- vs figure-level → set theme → map semantics → facet → export), and the validation checkpoints are boilerplate rather than real checkpoints tied to the actual plotting task.

Progressive Disclosure

The body has clear section structure and bulk content is appropriately split into separate files (references/examples.md, function_reference.md, objects_interface.md), but the SKILL.md body never references, links, or signals any of those bundle files, so a reader would not discover the 27KB API reference or 18KB examples — references are present in the bundle but not clearly signaled, matching the 3 anchor.

Specificity

Names concrete capabilities — 'fast EDA of distributions, relationships, and categorical comparisons', 'box/violin/pair plots and heatmaps', 'strong default aesthetics on top of matplotlib' — but coverage is example-driven within categories rather than a comprehensive action enumeration, so it sits below the 5 anchor.

Completeness

Explicitly answers both 'what' ('Statistical visualization library integrated with pandas') and 'when' ('use it when you need fast EDA of distributions, relationships, and categorical comparisons ...') with concrete trigger phrases, matching the 5 anchor; re-reading the 4 anchor, the 'when' here is sufficiently explicit rather than weakly implied.

Trigger Term Quality

Good natural keyword coverage ('distributions', 'relationships', 'categorical comparisons', 'box/violin/pair plots', 'heatmaps', 'EDA') that users would plausibly say, but it omits some synonyms (e.g., 'scatter', 'correlation matrix' is only implied) and any file/extension cues, falling short of comprehensive 5-level coverage.

Distinctiveness Conflict Risk

Mostly distinct niche (seaborn's statistical plots, semantic mappings, faceting) with specific plot-type triggers, but explicit ties to 'pandas' and 'matplotlib' plus the broad 'EDA/statistical visualization' framing create minor overlap risk with adjacent plotting skills, keeping it just below the 5 anchor.