Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bestagentkits/agency-skills --skill apple-hig-expertgit clone --depth 1 https://github.com/bestagentkits/agency-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bestagentkits/agency-skills/apple-hig-expert)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/apple-hig-expert"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/apple-hig-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/apple-hig-expert"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/apple-hig-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.01415 |
| Opus 5 | $0.00055 | $0.00707 |
| Sonnet 5 | $0.00022 | $0.00283 |
| Haiku 4.5 | $0.00011 | $0.00142 |
Grade A, and why
apple-hig-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apple HIG Expert
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/.
Before Starting
If product-context.md or ios-design-context.md exists, read it before asking questions. Then gather:
- Platform target: iOS, macOS, watchOS, or visionOS?
- Current state: new design or auditing an existing mockup/code?
- App category: utility, productivity, game, social, etc.
Modes
- Mode 1 — Design from scratch: pick the platform navigation paradigm and layout primitives first (see
references/platform-specifics.md), then apply typography and semantic color (references/visual-design.md). - Mode 2 — HIG audit: fill in
templates/hig-audit-template.md, runscripts/hig_checker.pyon every measurable element, and deliver a scored report (see Worked example below).
The Compliance Tool
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.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 109 lines · 109 tokens per session scan A 85c5aaa9653a
apple-hig-expert is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 109 tokens to every session and 1,415 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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