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Logging

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elie222
logging

How to do backend logging

Overview

Publisherelie222
Repositoryinbox-zero
Skill namelogging
Stars
12.5K
Forks
1.6K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by elie222 on GitHub. Read the source before you install it.

Installation

Install the Logging 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/elie222/inbox-zero/tree/main/.claude/skills/logging
  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/elie222/inbox-zero.git /tmp/inbox-zero
mkdir -p .claude/skills
cp -r /tmp/inbox-zero/.claude/skills/logging .claude/skills/logging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Logging

We use a centralized, request-scoped logging pattern where loggers are created by middleware and passed through the request/function chain.

API Route Logging (Primary Pattern)

Use middleware wrappers that automatically create loggers with request context:

typescript
import { withError, withAuth, withEmailAccount, withEmailProvider } from "@/utils/middleware";

// Basic route with error handling and logging
export const POST = withError("my-route", async (request) => {
  const logger = request.logger;
  logger.info("Processing request");
  // ...
});

// Authenticated route - logger includes userId
export const GET = withAuth("my-route", async (request) => {
  request.logger.info("User action"); // Already has userId context
  // ...
});

// Email account route - logger includes emailAccountId, email
export const POST = withEmailAccount("my-route", async (request) => {
  request.logger.info("Email action"); // Has userId, emailAccountId, email
  // ...
});

// Email provider route - same as email account, plus provides emailProvider
export const GET = withEmailProvider("my-route", async (request) => {
  request.logger.info("Provider action");
  const emails = await request.emailProvider.getMessages();
  // ...
});

The middleware automatically adds:

  • requestId - Unique ID for request tracing
  • url - Request URL
  • userId - For authenticated routes
  • emailAccountId, email - For email account routes

Enriching Logger Context

Add additional context within your route handler:

typescript
export const POST = withEmailAccount("digest", async (request) => {
  let logger = request.logger;
  
  const body = await request.json();
  logger = logger.with({ messageId: body.messageId });
  
  logger.info("Processing message");
  // ...
});

Helper Function Logging

Helper functions called from routes should receive the logger as a parameter instead of creating their own:

typescript
import type { Logger } from "@/utils/logger";

export async function processEmail(
  emailId: string,
  logger: Logger,
) {
  logger = logger.with({ emailId });
  logger.info("Processing email");
  // ...
}

Then call from your route:

typescript
export const POST = withEmailAccount("process", async (request) => {
  await processEmail(body.emailId, request.logger);
});

Server Action Logging

Server actions using actionClient receive the logger through context, similar to route middleware:

typescript
import { actionClient } from "@/utils/actions/safe-action";

export const createRuleAction = actionClient
  .metadata({ name: "createRule" })
  .inputSchema(createRuleBody)
  .action(
    async ({
      ctx: { emailAccountId, logger, provider },
      parsedInput: { name, actions },
    }) => {
      logger.info("Creating rule", { name });
      // ...
    },
  );

The actionClient context provides:

  • logger - Scoped logger with request context
  • emailAccountId - Current email account
  • provider - Email provider type

When to Use createScopedLogger

Use createScopedLogger only for code that doesn't run within a middleware chain (route or action):

typescript
import { createScopedLogger } from "@/utils/logger";

// Standalone scripts
const logger = createScopedLogger("script/migrate");

// Tests
const logger = createScopedLogger("test");

Don't use .with() for a global/file-level logger. Only use within a specific function.

Field Names Must Be Fixed

Axiom turns every nested key of a logged object into a permanent dataset field, and each dataset has a plan-dependent field limit. Once it is full, events that add a new field name are silently dropped.

Never log an object whose keys come from data (rule names, label names, sender addresses, IDs, header names). Log an array instead, since arrays are stored as one field:

typescript
// Bad: one new field per rule name
logger.info("Scores", { scores: { Newsletter: 0.6, Receipts: 0.4 } });

// Good: one field
logger.info("Scores", {
  scores: [{ rule: "Newsletter", score: 0.6 }, { rule: "Receipts", score: 0.4 }],
});

Frequently asked questions

What does the Logging AI skill do?

How to do backend logging

Why use Logging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elie222/inbox-zero/tree/main/.claude/skills/logging. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 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 Logging?

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

Is the Logging AI skill free?

It is published on GitHub by elie222. Check the repository for licensing terms. 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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