Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
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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?').
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See how to installReview the instructions the agent will receive before you install.
Design and audit apps against the Apple Human Interface Guidelines (HIG, developer.apple.com/design/human-interface-guidelines), including the Liquid Glass design language. HIG content evolves with each OS release — when a claim matters, verify against the live HIG pages cited in references/.
If product-context.md or ios-design-context.md exists, read it before asking questions. Then gather:
references/platform-specifics.md), then apply typography and semantic color (references/visual-design.md).templates/hig-audit-template.md, run scripts/hig_checker.py on every measurable element, and deliver a scored report (see Worked example below).scripts/hig_checker.py (stdlib-only) has three subcommands:
# 1. Contrast ratio (WCAG formula; pass >= 4.5:1 for normal text)
python3 scripts/hig_checker.py contrast "#8E8E93" "#FFFFFF"
# -> Contrast Ratio: 3.26 [FAILED]
# 2. Tap-target size (pass >= 44x44 pt per HIG)
python3 scripts/hig_checker.py target 32 32
# -> Tap Target: 32x32 [FAILED]
# 3. Batch audit from JSON -> scorecard (starts at 100, -10 per violation)
python3 scripts/hig_checker.py batch audit.json
Batch input shape:
{
"checks": [
{"type": "contrast", "name": "caption-on-card", "fg": "#8E8E93", "bg": "#FFFFFF"},
{"type": "target", "name": "close-button", "w": 32, "h": 32}
]
}
Scorecard rubric: the batch score starts at 100 and subtracts 10 per failed check; violations are listed by element name. 90-100 = ship, 70-80 = fix before release, below 70 = systematic rework. Checks the tool cannot measure (VoiceOver labels, Dynamic Type behavior, Reduce Transparency) are assessed manually via the audit template and tagged with confidence.
Input: mockup with body text #1C1C1E and captions #8E8E93 on white cards, a 32x32 pt close button, and a 343x50 pt primary CTA.
Run:
python3 scripts/hig_checker.py batch audit.json
Output (real):
{
"score": 80,
"violations": [
"Contrast 3.26 fails for caption-on-card",
"Target 32x32 small for close-button"
]
}
Findings → fixes (bottom line first):
HIG score 80/100 — two fixes before release.
- Captions fail contrast (3.26 < 4.5). Use
.secondaryLabel(semantic color) instead of hardcoded#8E8E93, or darken to ≥#6E6E73on white. 🟢 verified by tool.- Close button is 32x32 pt (< 44x44 minimum). Keep the glyph small but expand the hit region to 44x44 with padding/
contentShape. 🟢 verified by tool.- Manual check: the card uses an ultra-thin material over a photo background — re-test caption contrast against the busiest underlying region and with Reduce Transparency on. 🟡 needs device test.
glassEffect view modifier; keep hierarchy between content and controls. See references/visual-design.md.references/accessibility.md.references/platform-specifics.md.Surface these WITHOUT being asked: low contrast over translucent layers; interactive elements under 44 pt; icon buttons with no accessibility label; density overload (no breathing room between glass layers).
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Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
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