Lean and efficient: the overview motivates the gate with engine-specific behavior facts (Chromatic exits 1, Percy flags pending, Playwright fails) that are domain knowledge Claude lacks, and every section — inputs schema, decision rule, self-approve check, worked example — earns its place.

Fully executable code throughout: a complete Python `visual_gate` decision function, a runnable bash author-cannot-self-approve check, concrete JSON/YAML input schemas, and a pointer to a runnable `scripts/run_visual_gate.py` entrypoint in the reference.

A clear four-step 'How to use' sequence with explicit validation checkpoints — regression always blocks, intentional blocks until accepted, the self-approve check exits 1 on violation, and the referenced entrypoint fails closed when no classifications exist.

The body is an overview that defers the full markdown+JSON artifact template, CI entrypoint, and GitHub Actions wiring to a single one-level-deep, clearly signaled reference at references/artifact-and-ci-wiring.md, which exists as a real bundle file.

Lists multiple concrete actions: 'Consumes pre-classified visual-diff JSON and a reviewer-signed acceptance log to produce a single go/no-go CI verdict', 'Blocks when intentional baseline changes lack a non-author reviewer sign-off or when regressions are present', and 'emits a markdown + JSON artifact'.

Explicitly answers both what (produce go/no-go verdict, block on conditions, emit artifact) and when via the explicit 'Use this skill when...' clause, including a negative trigger ('not when the goal is fanning out to multiple engines').

Covers natural domain terms a user would say — 'visual regression', 'baseline changes', 'reviewer sign-off', 'CI verdict', 'go/no-go' — rather than abstract jargon.

Clear niche — visual-regression baseline gating with reviewer-approval enforcement — and explicitly contrasts against a 'multi-engine CI orchestrator', making a wrong-skill trigger unlikely.