Application Logging

Community

aj-geddes

application-logging

Implement structured logging across applications with log aggregation and centralized analysis. Use when setting up application logging, implementing ELK stack, or analyzing application behavior.

Overview

Publisher	aj-geddes
Repository	`useful-ai-prompts`
Skill name	`application-logging`
Stars	340
Forks	55
Bundled files	8
License	MIT
Links	Skill source Repository

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.
8 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by aj-geddes on GitHub. Read the source before you install it.

Installation

Install the Application Logging AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/aj-geddes/useful-ai-prompts.git /tmp/useful-ai-prompts
mkdir -p .claude/skills
cp -r /tmp/useful-ai-prompts/skills/application-logging .claude/skills/application-logging

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Application Logging 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 Application Logging 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 Application Logging 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.

Overview

Implement comprehensive structured logging with proper levels, context, and centralized aggregation for effective debugging and monitoring.

When to Use

Application debugging
Audit trail creation
Performance analysis
Compliance requirements
Centralized log aggregation

Quick Start

Minimal working example:

javascript
// logger.js
const winston = require("winston");

const logFormat = winston.format.combine(
  winston.format.timestamp({ format: "YYYY-MM-DD HH:mm:ss" }),
  winston.format.errors({ stack: true }),
  winston.format.json(),
);

const logger = winston.createLogger({
  level: process.env.LOG_LEVEL || "info",
  format: logFormat,
  defaultMeta: {
    service: "api-service",
    environment: process.env.NODE_ENV || "development",
  },
  transports: [
    new winston.transports.Console({
      format: winston.format.combine(
        winston.format.colorize(),
        winston.format.simple(),
      ),
    }),
    new winston.transports.File({
      filename: "logs/error.log",
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide	Contents
Node.js Structured Logging with Winston	Node.js Structured Logging with Winston
Express HTTP Request Logging	Express HTTP Request Logging
Python Structured Logging	Python Structured Logging
Flask Integration	Flask Integration
ELK Stack Setup	ELK Stack Setup
Logstash Configuration	Logstash Configuration

Best Practices

✅ DO

Use structured JSON logging
Include request IDs for tracing
Log at appropriate levels
Add context to error logs
Implement log rotation
Use timestamps consistently
Aggregate logs centrally
Filter sensitive data

❌ DON'T

Log passwords or secrets
Log at INFO for every operation
Use unstructured messages
Ignore log storage limits
Skip context information
Log to stdout in production
Create unbounded log files

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 Application Logging AI skill do?

Implement structured logging across applications with log aggregation and centralized analysis. Use when setting up application logging, implementing ELK stack, or analyzing application behavior.

Why use Application Logging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aj-geddes/useful-ai-prompts/tree/main/skills/application-logging. 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 Application Logging?

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 Application Logging?

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

Is the Application Logging AI skill free?

Yes. It is published on GitHub by aj-geddes 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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Application Logging

Overview