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Swift Diagnostics

Community
johnrogers
swift-diagnostics

Use when debugging NavigationStack issues (not responding, unexpected pops, crashes), build failures (SPM resolution, "No such module", hanging builds), or memory problems (retain cycles, leaks, deinit not called). Systematic diagnostic workflows for iOS/macOS.

Overview

Publisherjohnrogers
Repositoryclaude-swift-engineering
Skill nameswift-diagnostics
Stars
232
Forks
21
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Swift Diagnostics 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.
    https://github.com/johnrogers/claude-swift-engineering/tree/main/plugins/swift-engineering/skills/swift-diagnostics
  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/johnrogers/claude-swift-engineering.git /tmp/claude-swift-engineering
mkdir -p .claude/skills
cp -r /tmp/claude-swift-engineering/plugins/swift-engineering/skills/swift-diagnostics .claude/skills/swift-diagnostics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Swift Diagnostics 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 Swift Diagnostics 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 Swift Diagnostics 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.

Swift Diagnostics

Systematic debugging workflows for iOS/macOS development. These patterns help identify root causes in minutes rather than hours by following structured diagnostic approaches.

Reference Loading Guide

ALWAYS load reference files if there is even a small chance the content may be required. It's better to have the context than to miss a pattern or make a mistake.

ReferenceLoad When
NavigationNavigationStack not responding, unexpected pops, deep link failures
Build IssuesSPM resolution, "No such module", dependency conflicts
MemoryRetain cycles, memory growth, deinit not called
Build PerformanceSlow builds, Derived Data issues, Xcode hangs
Xcode DebuggingLLDB commands, breakpoints, view debugging

Core Workflow

  1. Identify symptom category - Navigation, build, memory, or performance
  2. Load the relevant reference - Each has diagnostic decision trees
  3. Run mandatory first checks - Before changing any code
  4. Follow the decision tree - Reach diagnosis in 2-5 minutes
  5. Apply fix and verify - One fix at a time, test each

Key Principle

80% of "mysterious" issues stem from predictable patterns:

  • Navigation: Path state management or destination placement
  • Build: Stale caches or dependency resolution
  • Memory: Timer/observer leaks or closure captures
  • Performance: Environment problems, not code bugs

Diagnose systematically. Never guess.

Common Mistakes

  1. Skipping mandatory first checks — Jumping straight to code changes before running diagnostics (clean build, restart simulator, restart Xcode) means you'll chase ghosts. Always start with the mandatory checks.

  2. Changing multiple things at once — "Let me delete DerivedData AND restart simulator AND kill Xcode" means you can't isolate which fix actually worked. Change one variable at a time.

  3. Assuming you know the cause — "NavigationStack stopped working, must be my reducer" — actually it was stale DerivedData. Diagnostic trees prevent assumptions. Follow the tree, don't guess.

  4. Missing memory basics — Calling deinit not being called is a retain cycle, but beginners often blame architecture. Use Instruments to verify leaks before refactoring. Data, not intuition.

  5. Not isolating the problem — Testing with your whole app complicates diagnosis. Create a minimal reproducible example with just the problematic feature. Isolation reveals root causes.

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 Swift Diagnostics AI skill do?

Use when debugging NavigationStack issues (not responding, unexpected pops, crashes), build failures (SPM resolution, "No such module", hanging builds), or memory problems (retain cycles, leaks, deinit not called). Systematic diagnostic workflows for iOS/macOS.

Why use Swift Diagnostics on TypingMind?

Because you install it once and use it with any model. Swift Diagnostics 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 Swift Diagnostics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/johnrogers/claude-swift-engineering/tree/main/plugins/swift-engineering/skills/swift-diagnostics. 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 Swift Diagnostics?

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 Swift Diagnostics?

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

Is the Swift Diagnostics AI skill free?

Yes. It is published on GitHub by johnrogers 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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