The body is mostly efficient and operational (rules, anti-pattern tables, code patterns) but the 'Data Must Tell a Business Story' framing is restated across the key-principles, critical-rules, and worked planning-example sections, and the example plan narrates the story at length — tightening this repetition would reach level 3; it is above level 1 because it avoids explaining basics Claude already knows.

Provides fully executable, copy-paste-ready code (Databricks Connect serverless setup, pandas UDF, weighted-category and FK patterns), concrete partition counts by scale, exact output-format and install commands, and a specific common-issues table — matching the anchor for executable code with specific examples.

Sequences the generation workflow (Gather Requirements → Present Plan → Ask About Features) with an explicit catalog-confirmation gate, a pre-generation checklist with the checkpoint 'Do NOT proceed to code generation until user approves', and a post-generation validation step via get_volume_folder_details; it is above level 2 because checkpoints and checklists are explicit rather than implied.

SKILL.md is a clear overview with a well-signaled References table pointing to two one-level-deep leaf files (verified to contain no nested .md references); the only minor gap is that scripts/generate_synthetic_data.py is not linked from the body, but the overview-plus-references structure fits level 3 better than the poorly-signaled/inline level 2.

Enumerates multiple concrete capabilities — 'Generate realistic synthetic data using Spark + Faker', 'serverless execution, multiple output formats (Parquet/JSON/CSV/Delta)', 'scales from thousands to millions of rows', and 'generate locally and upload to volumes' — matching the anchor for listing multiple specific concrete actions; it is above level 2 because it goes beyond naming the domain to specify formats, execution modes, and scale.

Clearly answers both what (generation capabilities, formats, scale, execution modes) and when via an explicit 'Use when user mentions ...' clause, satisfying the anchor for both what AND when with explicit triggers.

Provides broad natural-language triggers a user would actually say — 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', 'sample data' — giving good coverage rather than the partial set of level 2.

Targets a clear niche — Databricks synthetic/test data generation with Spark + Faker — with distinctive triggers unlikely to fire for unrelated skills; voice is third person ('Generate', 'Supports') with no first/second-person penalty.