logging-patterns
Java logging best practices with SLF4J, structured logging (JSON), and MDC for request tracing. Includes AI-friendly log formats for Claude Code debugging. Use when user asks about logging, debugging application flow, or analyzing logs.
86
82%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly communicates its purpose, lists specific technologies and capabilities, and includes an explicit 'Use when' clause with natural trigger terms. It is well-scoped to Java logging with specific frameworks (SLF4J, MDC), making it highly distinctive. The description is concise without being vague, and uses proper third-person voice throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and technologies: SLF4J, structured logging (JSON), MDC for request tracing, and AI-friendly log formats for Claude Code debugging. These are concrete, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (Java logging best practices with SLF4J, structured logging, MDC) and 'when' with an explicit 'Use when' clause covering logging, debugging application flow, and analyzing logs. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'logging', 'debugging', 'SLF4J', 'structured logging', 'JSON', 'MDC', 'request tracing', 'analyzing logs', 'application flow'. Good coverage of both general and specific terms. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche: Java logging specifically with SLF4J, MDC, and structured JSON logging. The combination of Java + logging + specific frameworks makes it unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides solid, actionable Java logging guidance with executable code examples covering multiple Spring Boot versions and profile-based switching. Its main weaknesses are verbosity in the AI-friendly logging rationale section (explaining things Claude already knows), a lack of validation checkpoints after setup, and all content being inline despite being long enough to benefit from progressive disclosure. The content also appears truncated at the end (SLF4J Basics section is incomplete).
Suggestions
Remove or significantly trim the 'Why JSON for AI/Claude Code?' section including the comparison table — Claude already understands JSON parsing advantages. Keep just the recommended setup.
Add a validation step after configuration, e.g., 'Verify: Run the app and confirm JSON output appears in console. If not, check that the profile is active and logback-spring.xml is on the classpath.'
Split the Spring Boot < 3.4 setup (logback-spring.xml, Logstash encoder details) into a separate reference file like LEGACY_SETUP.md, and link to it from the main skill.
Complete the truncated SLF4J Basics section or remove it if it's not essential to the skill's purpose.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill includes some unnecessary explanation (e.g., the AI-friendly logging comparison table, the 'AI can then:' bullet list explaining what AI can do with JSON). The 'Why JSON for AI/Claude Code?' section over-explains a concept Claude already understands. However, most code examples are lean and useful. | 2 / 3 |
Actionability | Provides fully executable code examples throughout: complete pom.xml dependencies, logback-spring.xml configurations, application.yml settings, Java code with imports, and bash commands for log analysis. All examples are copy-paste ready. | 3 / 3 |
Workflow Clarity | The skill covers multiple setup paths clearly (Spring Boot 3.4+ vs < 3.4, JSON vs human-readable profiles), but lacks explicit validation steps. There's no guidance on verifying the logging setup works correctly after configuration, and no error recovery steps if structured logging isn't producing expected output. | 2 / 3 |
Progressive Disclosure | Content is reasonably structured with clear sections and headers, but it's quite long (~200 lines) with all content inline. The Spring Boot < 3.4 setup and the full logback-spring.xml could be split into a separate reference file. No external file references are used despite the content length warranting it. The SLF4J Basics section at the end also appears cut off. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- piomin/claude-ai-spring-boot
- Commit
d87e7a3
- Reviewed
Table of Contents
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