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Java logging best practices with SLF4J, structured logging (JSON), and MDC for request tracing. Includes AI-friendly log formats for Claude Code debugging.
Passed 21 of 22 test cases with the skill, and 15 of 22 without it.
and 5 more in the full report
6 fixed15 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am investigating a bug reported for request ID req-abc123. The server logs are output as line-delimited JSON objects in app.log. What jq command line query should I run to isolate all log entries matching this specific request ID?
| Checked | Without skill | With skill |
|---|---|---|
| The command uses jq with the expression select(.requestId == "req-abc123"). | Missed | Passed |
The instructions your model reads when the skill loads.
Effective logging for Java applications with focus on structured, AI-parsable formats.
Key insight: JSON logs are better for AI analysis - faster parsing, fewer tokens, direct field access.
# 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 |
# application.yml - JSON by default
logging:
structured:
test cases passed, vs 15 / 22 without
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