Conciseness

The body is lean bullet-style guidance that assumes Claude's competence ("Never use `SELECT *`", "Prefer `EXISTS` over `IN`") without explaining what SQL or a CTE is. Not 5 because the 10-item dialect list with cloud-provider parentheticals and the Tips section partially restate workflow guidance ("Mention your SQL dialect upfront" duplicates step 2), adding tokens that could be trimmed. Not 3 because padding is minor and there is no concept-teaching.

Actionability

Concrete, executable guidance throughout: a 6-step workflow, specific best-practice rules, and three realistic example invocations ("Count of orders by status for the last 30 days", cohort retention, 500M-row partitioned table). Not 5 because dialect-specific syntax — a core promise — is deferred without a path ("see `sql-queries` skill for details", which is not a bundle file), and no illustrative SQL snippet grounds the expected output format. Not 3 because the guidance is specific and directly executable, not high-level hints.

Workflow Clarity

A clear, well-sequenced 6-step process (understand request → determine dialect → discover schema → write → present → offer to execute) with a user-interaction checkpoint ("ask which they use") and an execution offer. Not 5 because there is no validation checkpoint on the produced SQL (e.g., dry-run/EXPLAIN against a connected warehouse, or re-reading the request against output columns) — validation is only implicit. Not 3 because the sequence is complete and explicit; the skill is also non-destructive, so the batch/destruction cap does not apply.

Progressive Disclosure

Good structure with clear section headers (Usage, Workflow, Examples, Tips) and appropriate inline length (~120 lines of overview-level guidance, no monolithic reference dump). Not 5 because no bundle files exist: dialect-specific detail is deferred to an unverifiable external `sql-queries` skill and a `../../CONNECTORS.md` path outside the bundle, rather than well-signaled one-level-deep references. Not 3 because the inline content is appropriately overview-level and navigation is easy.

Specificity

"Write optimized SQL for your dialect with best practices" plus concrete actions ("translating a natural-language data need into SQL", "building a multi-CTE query with joins and aggregations", "optimizing a query against a large partitioned table", "getting dialect-specific syntax for Snowflake, BigQuery, Postgres") gives multiple specific, comprehensive actions (anchor 5), but the second-person phrasing "for your dialect" triggers the 1-point voice penalty, landing at 4. Not 3 because coverage is clearly comprehensive, not merely 1-2 actions.

Completeness

Explicitly answers both: what ("Write optimized SQL for your dialect with best practices") and when ("Use when translating... building... optimizing... or getting dialect-specific syntax"), with four concrete trigger phrases — a direct match for the anchor-5 example. Not 4 because the 'when' clause is fully explicit and enumerated, not merely present.

Trigger Term Quality

Natural trigger phrases users would actually say are well covered: "translating a natural-language data need into SQL", "multi-CTE query", "joins and aggregations", "optimizing a query against a large partitioned table", plus dialect names (Snowflake, BigQuery, Postgres). Not 5 because common synonyms like "database query", "write a SELECT", "Redshift/Databricks/MySQL", or "SQL dialect" variations are missing.

Distinctiveness Conflict Risk

Clear niche (SQL query authoring/optimization) with distinct triggers including named dialects ("Snowflake, BigQuery, Postgres") and specific scenarios ("multi-CTE query", "large partitioned table"), giving minimal conflict risk with adjacent data-analysis skills. Not 4 because the triggers are specific enough that wrong-skill triggering is unlikely.