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App Store Preflight Skills

CommunityPopular
truongduy2611
app-store-preflight-skills

Scan an iOS/macOS Xcode project for common App Store rejection patterns before submission. Use when preparing an app for App Store review, after receiving a rejection from Apple, or when auditing metadata, subscriptions, privacy manifests, entitlements, or design compliance. Integrates with the asc CLI for metadata inspection.

Overview

Publishertruongduy2611
Repositoryapp-store-preflight-skills
Skill nameapp-store-preflight-skills
Stars
1.4K
Forks
70
Bundled files
24
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 24 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by truongduy2611 on GitHub. Read the source before you install it.

Installation

Install the App Store Preflight Skills AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/truongduy2611/app-store-preflight-skills.git \
  .claude/skills/app-store-preflight-skills
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable App Store Preflight Skills in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads App Store Preflight Skills on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so App Store Preflight Skills is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

App Store Preflight Skill

Run pre-submission checks on your iOS/macOS project to catch common App Store rejection patterns.

Prerequisites

Step 1: Identify App Type → Load Checklist

Determine which guidelines apply by loading the relevant checklist from references/guidelines/by-app-type/. Always start with all_apps.md, then add the app-type-specific one:

App TypeChecklist
Every appreferences/guidelines/by-app-type/all_apps.md
Subscriptions / IAPreferences/guidelines/by-app-type/subscription_iap.md
Social / UGCreferences/guidelines/by-app-type/social_ugc.md
Kids Categoryreferences/guidelines/by-app-type/kids.md
Health & Fitnessreferences/guidelines/by-app-type/health_fitness.md
Gamesreferences/guidelines/by-app-type/games.md
macOSreferences/guidelines/by-app-type/macos.md
AI / Generative AIreferences/guidelines/by-app-type/ai_apps.md
Crypto & Financereferences/guidelines/by-app-type/crypto_finance.md
VPNreferences/guidelines/by-app-type/vpn.md

Full guideline index: references/guidelines/README.md

Step 2: Pull Metadata for Inspection

Pull the latest App Store metadata using the asc CLI:

bash
# Pull canonical metadata JSON for the version you want to review
asc metadata pull --app "<APP_ID>" --version "<VERSION>" --dir ./metadata

asc metadata pull writes app info files to ./metadata/app-info/*.json and version-localization files to ./metadata/version/<VERSION>/*.json.

Most rule examples below assume the canonical JSON layout written by asc metadata pull.

If you already have metadata in another layout (for example fastlane metadata/), either adapt the file-path examples to that structure or pull the canonical asc layout first.

Step 3: Run Rejection Rule Checks

For each category, load the relevant rule files from references/rules/ and inspect. Each rule contains: What to Check, How to Detect, Resolution, and Example Rejection.

CategoryRule Files
Metadatareferences/rules/metadata/*.md
Subscriptionreferences/rules/subscription/*.md
Privacyreferences/rules/privacy/*.md
Designreferences/rules/design/*.md
Entitlementsreferences/rules/entitlements/*.md

Step 4: Report Findings

Produce a summary report using this template:

markdown
## Preflight Report

### ❌ Rejections Found (N)
- [GUIDELINE X.X.X] Description of issue
  - File: path/to/offending/file
  - Fix: What to do

### ⚠️ Warnings (N)
- [GUIDELINE X.X.X] Potential issue

### ✅ Passed (N)
- [Category] All checks passed

Order by severity: rejections first, then warnings, then passed.

Step 5: Autofix + Validate

Some issues can be auto-fixed:

  • Competitor terms → Suggest replacement text with competitor names removed
  • Metadata character limits → Show current vs. max length
  • Missing links → Generate template ToS/PP URLs

After applying any auto-fix, re-run the affected checks to confirm the fix resolved the violation. Only mark as resolved once the re-scan passes.

For issues requiring manual intervention (screenshots, UI redesign), provide clear instructions but do not auto-fix.

Gotchas

  • China storefront — Banned AI terms (ChatGPT, Gemini, etc.) are checked across ALL locales, not just zh-Hans. Apple checks every locale visible in the China storefront.
  • Privacy manifestsPrivacyInfo.xcprivacy is required even if your app doesn't call Required Reason APIs directly. Third-party SDKs (Firebase, Amplitude, etc.) that use UserDefaults or NSFileManager trigger this requirement transitively.
  • asc authasc metadata pull requires App Store Connect authentication. Run asc auth login first, or set ASC_KEY_ID, ASC_ISSUER_ID, and one of ASC_PRIVATE_KEY_PATH / ASC_PRIVATE_KEY / ASC_PRIVATE_KEY_B64. If you're unsure what asc is picking up, run asc auth doctor.
  • Subscription metadata — Apple requires ToS/PP links in BOTH the App Store description AND the in-app subscription purchase screen. Missing either one is a separate rejection.
  • macOS entitlements — Apple will ask you to justify every temporary exception entitlement (com.apple.security.temporary-exception.*). Remove entitlements you don't actively use.

Adding New Rules

Create a .md file in the appropriate references/rules/ subdirectory:

markdown
# Rule: [Short Title]
- **Guideline**: [Apple Guideline Number]
- **Severity**: REJECTION | WARNING
- **Category**: metadata | subscription | privacy | design | entitlements

## What to Check
## How to Detect
## Resolution
## Example Rejection

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the App Store Preflight Skills AI skill do?

Scan an iOS/macOS Xcode project for common App Store rejection patterns before submission. Use when preparing an app for App Store review, after receiving a rejection from Apple, or when auditing metadata, subscriptions, privacy manifests, entitlements, or design compliance. Integrates with the asc CLI for metadata inspection.

Why use App Store Preflight Skills on TypingMind?

Because you install it once and use it with any model. App Store Preflight Skills is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install App Store Preflight Skills in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/truongduy2611/app-store-preflight-skills/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use App Store Preflight Skills?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with App Store Preflight Skills?

As many as you like. As long as a model supports skills, you can use App Store Preflight Skills with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the App Store Preflight Skills AI skill free?

Yes. It is published on GitHub by truongduy2611 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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