review

A workflow for reviewing software quality and accessibility, meaning how usable a product is for people with disabilities. It produces findings grouped by severity and a plan for fixing them.

In plain words
What is it for?
Use it for project quality reviews, accessibility audits, severity-based findings, structured Markdown reports, and fix planning, especially in iOS and SwiftUI projects.
Why use it?
It gives code reviews a consistent way to identify and prioritize quality or accessibility problems. It focuses on reporting and planning rather than making the changes itself.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/abdullah4ai/apple-developer-toolkit/review
Any agent
npx skills add Abdullah4AI/apple-developer-toolkit --skill review
Clone the repo
git clone --depth 1 https://github.com/Abdullah4AI/apple-developer-toolkit

Made for: Claude Code, Codex.

Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 234 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00061 $0.00234
Opus 5 $0.00030 $0.00117
Sonnet 5 $0.00012 $0.00047
Haiku 4.5 $0.00006 $0.00023

Measured 2d ago against content hash f0151d56b09a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 2d 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.

swiftship/internal/skills/data/always/review/SKILL.md · 23 lines

What it actually says

Review Workflow

Use this skill when reviewing generated project quality or running a focused accessibility audit.

Read the relevant guide for your task:

Scope boundaries:

  • This skill is for auditing/reporting, not implementation details.
  • For accessibility implementation patterns in SwiftUI, consult the generated accessibility skill when present.

Trigger Cues

Use this skill when requests mention: review, audit, accessibility, quality, findings.

Applicability

Primary targets: ios, swiftui.

Files

What ships with it

3 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. 2d ago First seen · 23 lines · 61 tokens per session scan A f0151d56b09a

Subscribe to this mod's changes

review is a skill published in the GitHub repository Abdullah4AI/apple-developer-toolkit (10 stars, last pushed 5d ago), licensed MIT. It adds 61 tokens to every session and 234 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 2026-08-31.

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