Conciseness

The body is code-first with no basic-concept padding (it never explains what Vision or OCR is) and version-sensitive material is properly quarantined into modern/legacy sections and a Common Mistakes format. It falls short of a 5 because of trimmable material: placeholder-comment loops in the Document Scanning example ('/* structured table data */'), and some redundancy between the Review Checklist and Common Mistakes sections.

Actionability

Most guidance is executable Swift with imports, real property names, and complete call chains (the OCR function, face/landmark requests, tracking loop, DataScannerViewController quick start). It is not a 5 because of minor gaps: several snippets reference undefined context values (cgImage, framePixelBuffers) and the document-scanning and table/list sections use placeholder comments instead of concrete property access.

Workflow Clarity

The skill's workflow — choose an API generation by deployment target (decision table up front), configure the request for the use case (recognition/quality level guidance, symbology limiting, availability checks), perform it, then verify — is clearly sequenced, and the 13-item Review Checklist plus DON'T/DO/Why Common Mistakes provide explicit validation checkpoints. There are no destructive or batch operations, so no cap applies, and the single-pattern-per-request structure is unambiguous.

Progressive Disclosure

The body is a clear overview with a Contents TOC, and both referenced files exist and are one level deep, each link justified by context ('Load references/vision-requests.md for complete legacy request and handler patterns'). It stays at 4 rather than 5 because some content that belongs in the reference files is inlined: the full legacy instance-mask code block and the 14-row 'Other Request Types' table.

Specificity

The description enumerates multiple concrete capabilities ('text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning') and grounds them in specific APIs ('VisionKit DataScannerViewController', 'CoreMLRequest/VNCoreMLRequest', 'modern Swift-native Vision API (iOS 18+) and legacy VNRequest patterns'). Coverage is comprehensive across the skill's domain, matching the anchor for multiple specific concrete actions rather than the score-4 anchor's 'minor gaps'.

Completeness

Both questions are answered: the 'what' is concrete ('Implement computer vision features including... in iOS apps. Covers both the modern Swift-native Vision API... and legacy VNRequest patterns') and a 'when' clause exists ('Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision'). It sits at 4 rather than 5 because the trigger list omits several of the stated capabilities (image segmentation, object tracking, document scanning), making the 'when' slightly less exhaustive than the 'what'.

Trigger Term Quality

Natural trigger phrases are present ('OCR', 'face detection', 'barcode scanning', 'Core ML model inference with Vision'), which users would commonly say. A few natural variations are missing — e.g., 'QR code', 'text recognition', or 'pose detection' — so it falls at 'good keyword coverage; a few natural terms missing' rather than comprehensive synonym coverage.

Distinctiveness Conflict Risk

The description is firmly niched to iOS Vision/VisionKit with distinctive API names, so it is 'mostly distinct'. Minor overlap remains with a hypothetical coreml skill since it advertises 'custom Core ML model inference', keeping it below the clear-niche anchor.