Conciseness

The body is lean: phase goals are one-liners ("Goal: Collect table names, column names, column types, and constraints") and almost all prose is Wren-specific, non-obvious knowledge like the callout "These are Wren Engine's internal namespace — they are NOT the database's native catalog or schema". Not 5 because the Quick reference table restates commands already shown and a few driver-specific introspection bullets could be trimmed; not 3 because there is no explanation of concepts Claude already knows.

Actionability

Fully executable throughout: copy-paste-ready SQLAlchemy introspection code with real return shapes, runnable CLI commands ("wren utils parse-type --type \"character varying(255)\" --dialect postgres"), a complete model YAML example, and a concrete test query ("wren --sql \"SELECT * FROM <model_name> LIMIT 1\""). Not 4 because the examples already cover the common cases end-to-end with no gaps.

Workflow Clarity

Eight numbered phases each with an explicit goal, including a pre-flight check (Phase 0 existing-project detection with user decision), and a validate-fix-retry feedback loop: "If validation fails, fix the reported issues and re-run" with a list of common errors. This covers database and batch operations with explicit checkpoints, matching the 5 anchor; 4 would require minor validation gaps, which are absent.

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are absent), so the ~300-line body is self-contained: well-sectioned phases, a Quick reference table, and only one-level-deep external pointers ("wren skills get usage", a linked Cube guide). Not 5 because the detailed YAML model spec and per-datasource introspection reference material are inlined and could be split into separate files to shrink the always-loaded context; not 3 because what is inline is clearly signaled and organized, with no buried or nested references.

Specificity

Quotes multiple concrete actions covering the whole capability: "exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL)", "schema discovery, type normalization, and MDL YAML generation using the wren CLI". Together with the named tool families, coverage is comprehensive for this skill; not below 5 because no meaningful action is missing, and 4 would require minor gaps.

Completeness

Explicitly answers both: what — "Generate a Wren MDL project by exploring a database... Guides agents through schema discovery, type normalization, and MDL YAML generation"; when — a full "Use when:" clause with three concrete triggers. Matches the 5 anchor exactly; 4 would require the 'when' to be less explicit.

Trigger Term Quality

Good natural triggers: "create or set up a new MDL", "onboard a new data source", "scaffold a project from an existing database". Not 5 because there are no synonyms or file-extension variants (e.g., wren_project.yml, MDL file), and not 3 because several genuinely natural user phrasings are present rather than only some relevant keywords.

Distinctiveness Conflict Risk

"Wren MDL" is a clear niche with distinct triggers; overlap with generic database skills is minimal because the tool and format are uniquely named. Not 4, since even the secondary triggers ("onboard a new data source" for Wren) are anchored to MDL/wren context.