hig-review

hig-review is a skill for Claude Code, Codex from agentlas-ai/agentlas-desktop. It costs 65 tokens per session (948 once invoked), scanned A, original, Apache-2.0.

A design-review workflow based on Apple’s Human Interface Guidelines, Apple’s recommendations for accessible and consistent interface design. It reviews a real screen or code and maps findings to concrete changes in the project’s own technology.

In plain words
What is it for?
Use it to audit or improve a website, desktop app, mobile interface, screenshot, mockup, or UI code against Apple’s design guidance.
Why use it?
It replaces general design opinions with guideline-backed findings and considers accessibility, layout, color, typography, materials, interaction states, and wording.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: $skill-name invocation.

Good fit Use it to audit or improve a website, desktop app, mobile interface, screenshot, mockup, or UI code against Apple’s design guidance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agentlas-ai/agentlas-desktop/hig-review
Install

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.

Any agent
npx skills add agentlas-ai/agentlas-desktop --skill hig-review
Clone the repo
git clone --depth 1 https://github.com/agentlas-ai/agentlas-desktop

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for hig-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/hig-review/github.svg)](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/hig-review)
Your own site
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/hig-review"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/hig-review/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.

agentmods 80×15 button for hig-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/hig-review"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/hig-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 948 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00065 $0.00948
Opus 5 $0.00032 $0.00474
Sonnet 5 $0.00013 $0.00190
Haiku 4.5 $0.00006 $0.00095

Measured 7d ago against content hash 04a9b7daeee2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

hig-review 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 7d 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.

plugins/design/skills/hig-review/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill Purpose

Produces a grounded design review: each finding names the guideline that backs it, carries a severity that reflects consequence, and lands as a concrete change in the user's framework — not Apple's. Also runs in improvement mode, where the review becomes a sequenced work plan.

Preconditions

  • The surface under review is identified: a URL, a running app, a screenshot, a mockup, or code.
  • The target platform and stack are known or inferable. A wrong platform guess invalidates the review.
  • Guideline text is reachable — either the local cache (references/apple-hig/.cache/pages/) or the network. $hig-sources explains the cache contract and how to refill it.
  • Adhere to $hig-review-protocol and $critical-overrides.

Steps

  1. Frame it. Establish surface, platform/stack, product job, and what the user wants (full audit, one concern, or improvements). Ask only what cannot be inferred.
  2. Get evidence. For a live URL or app, capture the screens with @agentlas-browser or @playwright — the flow, not one screen. For code, read the components that render it. Never review from a verbal description when the real thing is reachable.
  3. Route to guidelines. Open $hig-lookup and pick 3–8 pages: accessibility, color, layout, and typography always, plus the pages matching what is actually on screen. Read the cached page; if absent, fetch its DocC JSON as the routing table describes.
  4. Audit. Work $hig-checklist in order — accessibility, platform conventions, layout, visual system, interaction states, content, privacy. Measure what can be measured (contrast, target size, text scaling) instead of estimating it.
  5. Translate. Convert every Apple noun into the user's stack with $hig-platform-translation, and drop the rules that do not bind their platform rather than reporting them as violations. For glass, blur, or vibrancy work, apply $liquid-glass.
  6. Grade and write. Assign severity by consequence and produce the report shape defined in $hig-review-protocol, including what already works and the limits of the review.
  7. Improvement mode (when asked to fix, not just judge). Rank findings by severity against effort, propose the concrete change for each, then sequence: accessibility first, platform conventions next, visual and polish last. Apply the changes only when the user asks for that.

Read the full file on GitHub · 62 lines

Changes

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.

  1. 7d ago First seen · 62 lines · 65 tokens per session scan A 04a9b7daeee2

Subscribe to this mod's changes

hig-review is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (5 stars, last pushed today), licensed Apache-2.0. It adds 65 tokens to every session and 948 once invoked, about $0.0003 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 2026-09-04.

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