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
Pending
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 identifies its domain (Java logging), lists specific technologies and practices (SLF4J, structured JSON logging, MDC), and provides explicit trigger guidance. It uses proper third-person voice and includes natural keywords that users would employ when seeking help with logging or debugging.
| 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' (explicit 'Use when user asks about logging, debugging application flow, or 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 is a solid, actionable skill with excellent executable code examples covering multiple Spring Boot versions and logging configurations. Its main weaknesses are moderate verbosity in the AI-friendly logging rationale section (explaining things Claude already knows), a missing verification workflow after setup, and the content being somewhat monolithic without progressive disclosure to separate files. The SLF4J basics section appears truncated.
Suggestions
Add a verification step after each setup path (e.g., 'Run the app and confirm: `curl localhost:8080/health | jq .` then check console for JSON output')
Trim the 'Why JSON for AI/Claude Code?' section significantly - Claude doesn't need to be convinced why JSON is easier to parse than text
Split detailed configurations (logback-spring.xml, MDC patterns) into separate reference files and link from the main skill
Complete the truncated SLF4J Basics section or remove it if it's covered elsewhere
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient but 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, the code examples themselves are lean. | 2 / 3 |
Actionability | Provides fully executable code examples throughout: complete YAML configs, XML configurations, Java code with imports, bash commands for log analysis, and Maven dependencies. Everything is copy-paste ready with specific versions and class names. | 3 / 3 |
Workflow Clarity | The skill covers multiple setup paths clearly (Spring Boot 3.4+ vs older) with profile-based switching, but lacks explicit validation steps. There's no guidance on verifying the logging setup works correctly (e.g., 'run the app and confirm JSON output appears'). For a configuration-heavy skill, a verification checkpoint would be valuable. | 2 / 3 |
Progressive Disclosure | Content is reasonably structured with clear sections and headers, but it's quite long and monolithic. The SLF4J basics section is cut off mid-code-block, suggesting incomplete content. Advanced topics like MDC for request tracing (mentioned in the description) could be split into separate files. No references to external files for deeper topics. | 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
301ea7a
- Reviewed
Table of Contents
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