logging-patterns

Guidance for writing Java application logs with SLF4J, JSON records, and request-tracking IDs. SLF4J is a common Java logging interface, while structured logs store details in named fields.

In plain words
What is it for?
Use it to add or improve Java logging, trace requests across services, inspect application flow, and prepare logs for AI-assisted debugging.
Why use it?
It helps replace hard-to-search text logs with consistent records that are easier to filter, follow through a request, and analyze when debugging.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/decebals/claude-code-java/logging-patterns
Any agent
npx skills add decebals/claude-code-java --skill logging-patterns
Clone the repo
git clone --depth 1 https://github.com/decebals/claude-code-java

Made for: Claude Code, Codex.

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,283 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 88% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00050 $0.03283
Opus 5 $0.00025 $0.01641
Sonnet 5 $0.00010 $0.00657
Haiku 4.5 $0.00005 $0.00328

Measured 2d ago against content hash f9768c9c4503, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

logging-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

88% identical to logging-patterns — 274 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/logging-patterns/SKILL.md · 524 lines

How it starts

The opening of the file, as written. The whole thing — 524 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Logging Patterns Skill

Effective logging for Java applications with focus on structured, AI-parsable formats.

When to Use

  • User says "add logging" / "improve logs" / "debug this"
  • Analyzing application flow from logs
  • Setting up structured logging (JSON)
  • Request tracing with correlation IDs
  • AI/Claude Code needs to analyze application behavior

AI-Friendly Logging

Key insight: JSON logs are better for AI analysis - faster parsing, fewer tokens, direct field access.

Why JSON for AI/Claude Code?

# Text format - AI must "interpret" the string
2026-01-29 10:15:30 INFO OrderService - Order 12345 created for user-789, total: 99.99

# JSON format - AI extracts fields directly
{"timestamp":"2026-01-29T10:15:30Z","level":"INFO","orderId":12345,"userId":"user-789","total":99.99}
Aspect Text JSON
Parsing Regex/interpretation Direct field access
Token usage Higher (repeated patterns) Lower (structured)
Error extraction Parse stack trace text exception field
Filtering grep patterns jq queries

Recommended Setup for AI-Assisted Development

# application.yml - JSON by default
logging:
  structured:
    format:
      console: logstash  # Spring Boot 3.4+

# When YOU need to read logs manually:
# Option 1: Use jq
# tail -f app.log | jq .

# Option 2: Switch profile temporarily
# java -jar app.jar --spring.profiles.active=human-logs

Log Format Optimized for AI Analysis

{
  "timestamp": "2026-01-29T10:15:30.123Z",
  "level": "INFO",
  "logger": "com.example.OrderService",
  "message": "Order created",
  "requestId": "req-abc123",
  "traceId": "trace-xyz",
  "orderId": 12345,
  "userId": "user-789",
  "duration_ms": 45,
  "step": "payment_completed"
}

Key fields for AI debugging:

  • requestId - group all logs from same request
  • step - track progress through flow
  • duration_ms - identify slow operations
  • level - quick filter for errors

Read the full file on GitHub · 524 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 524 lines · 50 tokens per session scan A f9768c9c4503

Subscribe to this mod's changes

logging-patterns is a skill published in the GitHub repository decebals/claude-code-java (722 stars, last pushed 4d ago), licensed MIT. It adds 50 tokens to every session and 3,283 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to logging-patterns, differing in 274 lines, and is treated as a copy.

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