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Logging Setup

Community
rshankras
logging-setup

Generates structured logging infrastructure using os.log/Logger to replace print() statements. Use when user wants to add proper logging, replace print statements, or set up app logging.

Overview

Publisherrshankras
Repositoryclaude-code-apple-skills
Skill namelogging-setup
Stars
744
Forks
70
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Logging Setup 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/rshankras/claude-code-apple-skills.git /tmp/claude-code-apple-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-apple-skills/skills/generators/logging-setup .claude/skills/logging-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Logging Setup 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 Logging Setup 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 Logging Setup 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.

Logging Setup Generator

Replace print() statements with Apple's structured logging system (os.log/Logger) for better debugging, privacy controls, and Console.app integration.

When This Skill Activates

Use this skill when the user:

  • Asks to "add logging" or "set up logging"
  • Wants to "replace print statements"
  • Mentions "os.log", "Logger", or "structured logging"
  • Asks about "debug logging" or "production logging"
  • Wants to audit print() usage in their codebase

Why Logger Over print()

print()Logger
Always executesDebug logs compiled out in Release
No filteringFilter by subsystem/category in Console.app
No privacy.private, .public, .sensitive annotations
String interpolation always runsDeferred evaluation (performance)
Not in Console.appFull system integration

Pre-Generation Checks

1. Project Context Detection

  • Check deployment target (Logger requires iOS 14+ / macOS 11+)
  • Search for existing Logger/os.log usage
  • Identify source file locations (Sources/, App/, etc.)

2. Conflict Detection

Search for existing logging:

Glob: **/*Logger*.swift
Grep: "import OSLog" or "os_log"

If found, ask user:

  • Extend existing logging?
  • Replace with new implementation?
  • Create separate logger?

Modes of Operation

Mode 1: Audit

Find all print() statements and report:

Grep: print\s*\(

Report format:

  • File:line - print statement
  • Severity: Info/Warning/Error (based on context)
  • Suggested Logger level

Mode 2: Generate

Create logging infrastructure from scratch.

Mode 3: Migrate

Convert existing print() to Logger with suggestions.

Configuration Questions

Ask user via AskUserQuestion:

  1. Categories needed?

    • Network, Auth, UI, Data (defaults)
    • Custom categories?
  2. Include migration helpers?

    • Extension on String for quick migration
    • Temporary print-to-log bridge

Generation Process

Step 1: Create AppLogger.swift

Read template from templates/AppLogger.swift and customize:

  • Set subsystem from Bundle.main.bundleIdentifier
  • Add user-specified categories
  • Include usage examples in comments

Step 2: Determine File Location

Check project structure:

  • If Sources/ exists → Sources/Logging/AppLogger.swift
  • If App/ exists → App/Logging/AppLogger.swift
  • Otherwise → Logging/AppLogger.swift

Step 3: Provide Migration Guidance

Show examples of converting common print patterns:

swift
// Before
print("User logged in: \(email)")

// After
AppLogger.auth.info("User logged in: \(email, privacy: .private)")

Output Format

After generation, provide:

Files Created

  • [Path]/Logging/AppLogger.swift

Integration Steps

  1. Import in files: import OSLog (not needed if using AppLogger)
  2. Replace print() calls with AppLogger.[category].level
  3. Add privacy annotations for sensitive data

Privacy Annotations Guide

  • .public - Safe to log (IDs, counts, non-sensitive)
  • .private - Redacted in release (emails, names)
  • .sensitive - Always redacted (passwords, tokens)

Console.app Usage

  1. Open Console.app
  2. Filter by subsystem: com.yourapp
  3. Filter by category: Network, Auth, etc.

Testing Instructions

  1. Add a test log: AppLogger.general.debug("Test log")
  2. Run app, check Xcode console
  3. Open Console.app, filter by your app's subsystem

References

  • logger-patterns.md - Best practices and privacy levels
  • migration-guide.md - Converting print() to Logger
  • templates/AppLogger.swift - Template file

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 Logging Setup AI skill do?

Generates structured logging infrastructure using os.log/Logger to replace print() statements. Use when user wants to add proper logging, replace print statements, or set up app logging.

Why use Logging Setup on TypingMind?

Because you install it once and use it with any model. Logging Setup 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 Logging Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rshankras/claude-code-apple-skills/tree/main/skills/generators/logging-setup. 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 Logging Setup?

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 Logging Setup?

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

Is the Logging Setup AI skill free?

Yes. It is published on GitHub by rshankras 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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