Conciseness

The body is dense with novel routing and policy instructions rather than concepts Claude already knows, and it stays at 71 lines while pushing detail to references ("Put detailed metric definitions, tables, query patterns, caveats, and evidence into linked references"). Not a 5 because of noticeable restatement: the route-to-`index` rule appears four times (lines 10, 12, 33, 71) and the weekly-refresh offer terms are duplicated between "Creating Or Updating The Layer" and "Output Contract".

Actionability

Most guidance is executable: exact destinations ("$CODEX_HOME/skills/<area>-semantic-layer", "~/.codex/skills/<area>-semantic-layer"), a three-branch decision table with explicit selection conditions, and named artifacts like "references/source-inventory.md". Not a 5: there are no runnable commands or examples, and instructions to "build and validate the same canonical semantic-layer skill package" stay abstract even though the bundle ships validation scripts (scripts/validate_data_context_contract.py, scripts/data_analytics_preflight.py) that are never linked.

Workflow Clarity

The sequence is discernible (routing/intake → destination selection → source inventory → crawl and synthesis from skill-template → refresh offer → output contract) with validation checkpoints named ("Build and validate the same canonical semantic-layer skill package before writing it to any destination", "Validate the written skill and verify it is discoverable in a new local Codex context"). Not a 5: steps are never presented as an ordered sequence, and the validation checkpoints specify that validation should happen but not how, leaving the fix-and-retry loop implicit.

Progressive Disclosure

The body keeps detail in clearly-signaled one-level-deep references that exist (references/semantic-layer/skill-template.md, references/semantic-layer/weekly-polling-automation.md), but measured against the actual bundle the index is incomplete: references/semantic-layer/connector-playbook.md, references/semantic-layer/source-intake.md, references/automation.md, and all three scripts/*.py files are never linked from SKILL.md, and "references/source-inventory.md" (referenced twice) does not exist in this bundle. Not a 4: half the bundle is orphaned from the entry point, which is more than a minor organization gap; not a 2 because the in-body content is well-sectioned and the references that are made are clear.

Specificity

"Create, update, inspect, or repair" lists several concrete lifecycle actions on a named artifact ("Data Analytics semantic layers"), which matches the anchor for several specific actions with minor gaps. It falls short of 5 because the actions stay at the verb level and never name concrete deliverables (e.g. metric definitions, tables, caveats) the way the 5-anchor example does.

Completeness

The description explicitly answers both questions with concrete trigger phrases: what ("Create, update, inspect, or repair Data Analytics semantic layers") and when ("Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite"), matching the top anchor's structure. It is not a 4 because the when-clause is already explicit and trigger-phrased rather than merely implied or generic.

Trigger Term Quality

"Use when the user asks to save data context or create a semantic layer" supplies two natural trigger phrases a user would plausibly say, matching the good-coverage anchor. Not a 5: common synonyms and variations ("document our metrics", "define the source of truth", "update the semantic layer", "inspect/repair" triggers) are absent from the when-clause.

Distinctiveness Conflict Risk

"Data Analytics semantic layers" and "save data context" carve out a clear niche with distinct triggers and minimal overlap risk with generic data-analysis or document skills. The body even disambiguates against the sibling `index` skill, reinforcing distinctiveness.