adversarial-ios-design

adversarial-ios-design is a skill for Claude Code, Codex from pproenca/dot-skills. It costs 217 tokens per session (3,195 once invoked), scanned A, original, MIT.

A pass/fail review of iOS SwiftUI screens and user interactions, based on Apple's Human Interface Guidelines. It judges the rendered app using simulator screenshots and interaction recordings, including different themes and text sizes.

In plain words
What is it for?
Use it to review SwiftUI screens, features, or visual changes before release, with evidence from light mode, dark mode, and accessibility text sizes.
Why use it?
It finds design and usability problems that are easy to miss in source code, such as unsuitable navigation, lost form drafts, unclear alerts, or empty screens without guidance. It reports violations but does not fix them.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; mentions Codex.

Good fit Use it to review SwiftUI screens, features, or visual changes before release, with evidence from light mode, dark mode, and accessibility text sizes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/pproenca/dot-skills/adversarial-ios-design
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 pproenca/dot-skills --skill adversarial-ios-design
Clone the repo
git clone --depth 1 https://github.com/pproenca/dot-skills

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 adversarial-ios-design

README.md
[![agentmods](https://agentmods.dev/badge/skills/pproenca/dot-skills/adversarial-ios-design.svg)](https://agentmods.dev/skills/pproenca/dot-skills/adversarial-ios-design)
Your own site
<a href="https://agentmods.dev/skills/pproenca/dot-skills/adversarial-ios-design"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/adversarial-ios-design.svg" alt="Measured on agentmods" height="20"></a>
Per session 217 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,195 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Rogue Agent · line 36
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
  • medium Excessive Agency · line 55
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 $0.00217 $0.03195
Opus 5 $0.00109 $0.01597
Sonnet 5 $0.00043 $0.00639
Haiku 4.5 $0.00022 $0.00319

Measured 8d ago against content hash e5102d2dbd22, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

adversarial-ios-design 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 8d 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.

skills/.experimental/adversarial-ios-design/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adversarial iOS Design Gate

An iOS design-and-UX review gate — pass/fail: a single blind reviewer subagent judges the work against this gate's rules with an adversarial mandate, and the work passes only when every rule is PASS or N/A. This skill renders verdicts; it never fixes the work.

The gate judges rendered user interface, not source text. Capturing evidence — simulator screenshots and interaction recordings tiled into filmstrips — is a mandatory protocol step, and the reviewer works backwards from the pixels: observe the captures, suspect violations, then open code only to locate them. Code-only review is the gate's recorded failure mode: it turns the gate into a linter that prescribes animations it has never seen.

The rules are the decidable subset of what an Apple design review would flag — every rule names the evidence that decides it, drawn from the Human Interface Guidelines, WWDC design sessions, and Apple's own measured app conventions. The gate judges the failure modes agents and experienced-but-not-expert developers actually ship: hamburger drawers where tab bars belong, pushed forms that lose drafts on a swipe, alerts that say "Yes"/"No", blank empty states, whole-screen spinners, teleporting state changes, glass painted on content, and text that clips at accessibility sizes.

When to Apply

  • A user-facing SwiftUI screen, feature, or PR is about to merge and needs an objective PASS/FAIL on design and UX quality, not advisory feedback.
  • An agent (Claude, Codex) authored the UI and you want an independent check that it follows platform grammar — modality, navigation structure, alert discipline, feedback states — rather than web/Android idioms translated into Swift.
  • A screen "works" but feels non-native, and you want the decidable causes enumerated with locations instead of taste adjectives.
  • A design polish pass claims Apple-level quality and you want that claim tested against what the screen actually renders — screenshots and filmstrips — not against what the code implies.
  • A previous "polish" or gate-driven fix round made the UI busier — added animations, haptics, or decoration — and you want the restraint direction enforced: gratuitous motion fails, and every fix must be the minimal change with removal preferred over addition.
  • The diff changes gestures or interaction behavior (recognizers, drag/long-press/resize, custom ButtonStyle/PrimitiveButtonStyle implementations) — run this gate, with its capture protocol, before the sibling architecture gates: recognizer arbitration is invisible to code-only review, a single clean trial is not evidence (see the repeated-trials requirement in references/_evidence-capture.md), and sequencing this gate last is how interaction regressions ship looking reviewed.

Read the full file on GitHub · 85 lines

Files

What ships with it

60 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.

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. 8d ago First seen · 85 lines · 217 tokens per session scan A e5102d2dbd22

Subscribe to this mod's changes

adversarial-ios-design is a skill published in the GitHub repository pproenca/dot-skills (203 stars, last pushed 23d ago), licensed MIT. It adds 217 tokens to every session and 3,195 once invoked, about $0.0011 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 2026-08-30.

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