Conciseness

The body is dense with domain-specific commands and constraints and avoids explaining concepts Claude already knows, but the waiter plus fallback-polling pattern is repeated nearly verbatim across Auto Import, Convert Database, Assess Database, Convert Statement, and Work with Tree even though references/schema-conversion-operations.md already documents it. This is more than a single padded sentence but short of the 'several unnecessary explanations' of anchor 2, placing it at efficient-with-trimmable-repetition.

Actionability

The body gives copy-paste-ready AWS CLI commands with exact waiter names, filter syntax ('Name=schema-conversion-operation-id,Values=<RequestIdentifier>'), polling intervals, terminal statuses, parameter placeholders, and concrete value examples (e.g., server-name short IDs, 'statement-<timestamp>' model names). The one delegated input — selection-rules JSON — has its flexibility explicitly justified by the mandatory fetch-the-docs constraint, so commands remain fully executable.

Workflow Clarity

Every async workflow has an explicit validation checkpoint (waiter, then SUCCESS/FAILED check with fallback polling every 30 seconds), error recovery feedback loops (waiter failure → polling; any failure → troubleshooting.md), and the destructive Clean Up flow requires explicit customer confirmation with a stated dependency-ordered deletion sequence. This matches the anchor for clear sequencing, explicit validation, and feedback loops, exceeding anchor 4 which would leave checkpoints only implicit.

Progressive Disclosure

The body serves as an operational hub with five real, well-signaled references ([setup-wizard.md], [action-items.md], [cancel-operations.md], [schema-conversion-operations.md], [troubleshooting.md]) that exist in the bundle and are exactly one level deep — no reference chains into further references. The 822-line setup wizard is appropriately split out of the body, and the body retains only the main-loop operations, matching the clear-overview anchor.

Specificity

The description enumerates seven concrete actions ("creating migration projects", "converting database schemas to a target engine", "running compatibility assessments", "navigating metadata trees", "exporting converted DDL to S3", "applying schema changes to a target database", "converting SQL statements between database engines") in third person, comprehensively covering the skill's lifecycle. This matches the anchor for multiple specific concrete actions with comprehensive coverage, and is above anchor 4 only in that no coverage gaps remain relative to the skill body.

Completeness

The first sentence explicitly states what the skill does, and "Applies when migrating database schemas between heterogeneous engines using AWS DMS Schema Conversion" provides an explicit, concrete when-to-use trigger. Both what and when are clearly answered, matching anchor 5; anchor 4 would require a weaker or less explicit 'when' clause.

Trigger Term Quality

Natural phrases like "migrating database schemas", "heterogeneous engines", "compatibility assessments", and "AWS DMS Schema Conversion" are present, giving good keyword coverage. It falls short of anchor 5 because common synonyms and specifics users would actually say — engine names (SQL Server, PostgreSQL, Oracle, MySQL), "schema migration" — are missing.

Distinctiveness Conflict Risk

The description is anchored to a named AWS service ("AWS DMS Schema Conversion") with service-specific operations (metadata trees, DDL export to S3, migration projects), forming a clear niche with distinct triggers and minimal conflict risk with other skills.