Conciseness

The body contains no concept explanations or padding — every section is a working SQL template, a brief instruction ("Run via `run_sql`"), or a useful checklist, and it includes practical error handling ("If the table name isn't fully qualified, search INFORMATION_SCHEMA.TABLES to locate it first"). Every token earns its place, matching anchor 5.

Actionability

Fully executable SQL covers the common cases — metadata lookup, row counts, per-type statistics for numeric/string/timestamp columns, cardinality with windowed percentages, and sampling — with placeholders like <table> and column_name serving as appropriate parameterization rather than pseudocode. This matches anchor 5's copy-paste-ready coverage.

Workflow Clarity

The 7-step sequence (metadata → size → column stats → cardinality → sample → quality assessment → output) is clearly ordered and includes an error-recovery branch for unqualified table names, but there are no explicit validation checkpoints on results before proceeding to the summary. Anchor 4 fits: clear sequence with minor validation gaps; anchor 5 requires explicit validate-and-retry loops that are absent.

Progressive Disclosure

The skill is a single well-organized file with clear per-step headers, and all SQL templates are needed on every invocation of this one cohesive workflow, so inline placement is appropriate — there are no bundle files and no buried or nested references. Navigation is easy, matching anchor 5.

Specificity

"Deep-dive data profiling for a specific table" names the domain plus a single action, and the remaining clauses are trigger scenarios rather than concrete capabilities (no mention of computing column statistics, cardinality analysis, or data-quality checks). This matches anchor 3 — it falls short of anchor 4, which requires several specific listed actions.

Completeness

It explicitly answers both what ("Deep-dive data profiling for a specific table") and when ("Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality..."), with concrete trigger phrases and an explicit parameter requirement ("Requires a table name"), matching anchor 5.

Trigger Term Quality

Natural user phrases are well covered — "profile a table", "statistics about a dataset", "data quality", "understand a table's structure and content" — but common synonyms like "summarize a table", "explore", or "describe the data" are missing, matching anchor 4 rather than the comprehensive anchor 5.

Distinctiveness Conflict Risk

"profiling a table" combined with "Requires a table name" carves a mostly distinct niche with clear triggers, but "statistics about a dataset" and "data quality" are broad enough to overlap with general data-analysis skills, fitting anchor 4 rather than the minimal-conflict anchor 5.