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apple-hig-expert

Audits and designs iOS/macOS/watchOS/visionOS interfaces against the Apple Human Interface Guidelines, including the Liquid Glass design language (announced WWDC25, shipped with iOS 26/macOS Tahoe, Sept 2025). Use when reviewing an Apple-platform mockup or app for HIG compliance, checking contrast or tap-target sizes, or designing native-feeling Apple UI (e.g., 'audit my iOS app against the HIG', 'is this text readable on Liquid Glass?').

74

Quality

91%

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SecuritybySnyk

The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

An actionable, well-organized body with executable tooling and a clear audit workflow including validation. The main gaps are a missing templates/hig-audit-template.md file referenced in Mode 2 and some time-sensitive date/version prose that pads conciseness.

Suggestions

Add the missing templates/hig-audit-template.md file (or remove the Mode 2 instruction to fill it in) so the referenced bundle path resolves.

Move or trim the repeated Liquid Glass date/version detail (WWDC25, iOS 26, macOS Tahoe, Sept 2025) into a single short note or a deprecated/old-patterns section to reduce time-sensitive padding.

Make the audit feedback loop explicit after fixes, e.g. 're-run hig_checker.py batch and confirm score >= 90 before shipping'.

DimensionReasoningScore

Conciseness

Mostly lean and action-oriented, but the repeated time-sensitive date/version detail ('announced at WWDC25, June 2025; shipped Sept 2025 across iOS 26, iPadOS 26, macOS Tahoe, watchOS 26, tvOS 26, visionOS 26') is not in a deprecated section and slightly pads the body.

4 / 5

Actionability

Fully executable, copy-paste-ready commands ('python3 scripts/hig_checker.py contrast "#8E8E93" "#FFFFFF"'), a concrete batch JSON input shape, and a real worked example with actual output covering the common cases.

5 / 5

Workflow Clarity

Modes are sequenced and the batch audit includes validation checkpoints — the scorecard rubric (90-100 ship / 70-80 fix / <70 rework), confidence tagging, and a Findings→fixes loop — so the batch-operation cap does not apply; only a minor gap in that the re-run-after-fix step is not made explicit.

4 / 5

Progressive Disclosure

Good structure with well-signaled one-level-deep references to references/*.md and scripts/hig_checker.py, but Mode 2 directs the reader to 'fill in templates/hig-audit-template.md' and that template file does not exist in the bundle.

4 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that pairs concrete capabilities with explicit, natural-sounding trigger phrases and a clear niche. It clearly answers both what the skill does and when to invoke it.

DimensionReasoningScore

Specificity

Names the domain and multiple concrete actions — 'Audits and designs iOS/macOS/watchOS/visionOS interfaces against the Apple Human Interface Guidelines' plus 'checking contrast or tap-target sizes' — giving comprehensive coverage.

5 / 5

Completeness

Explicitly answers both what (audits and designs Apple-platform interfaces against the HIG) and when ('Use when reviewing an Apple-platform mockup or app for HIG compliance...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural triggers including quoted user utterances like 'audit my iOS app against the HIG' and 'is this text readable on Liquid Glass?', plus mockup/HIG-compliance/contrast phrasings users would naturally say.

5 / 5

Distinctiveness Conflict Risk

Clear niche (Apple HIG, Liquid Glass) with distinct, platform-specific triggers and minimal overlap risk with generic design skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
alirezarezvani/claude-skills
Reviewed

Table of Contents

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