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.
88
86%
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 well-crafted skill description that excels across all dimensions. It provides specific technologies (SLF4J, MDC, JSON), concrete use cases (request tracing, debugging), and explicit trigger guidance. The Java-specific focus with named frameworks makes it highly distinctive and easy for Claude to select appropriately.
| 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 for request tracing, AI-friendly log formats') and when ('Use when user asks about logging, debugging application flow, or analyzing logs'). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'logging', 'debugging', 'application flow', 'analyzing logs'. Also includes technical terms like 'SLF4J', 'JSON', 'MDC' that Java developers would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with Java-specific logging focus, mentioning SLF4J, MDC, and structured JSON logging. The combination of Java + logging + specific frameworks creates a clear niche unlikely to conflict with general debugging or other language skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, actionable skill with excellent code examples covering multiple Spring Boot versions and use cases. The main weaknesses are some verbosity in explaining AI/JSON benefits (which Claude already understands) and missing validation steps to confirm logging configuration is working correctly. The structure and progressive disclosure are well done.
Suggestions
Add a validation step after setup sections (e.g., 'Verify by running the app and checking: `curl localhost:8080/actuator/health | jq .` should show JSON output')
Trim the 'Why JSON for AI/Claude Code?' section - Claude doesn't need convincing about JSON parsing benefits; just state the recommendation
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is generally efficient but includes some unnecessary explanations (e.g., the comparison table for text vs JSON, explaining why JSON is better for AI). Some sections could be tightened, though most content earns its place. | 2 / 3 |
Actionability | Excellent executable code throughout - complete pom.xml dependencies, full logback-spring.xml configurations, working Java code with imports, and ready-to-use bash commands. All examples are copy-paste ready. | 3 / 3 |
Workflow Clarity | Steps are listed clearly for setup (Spring Boot 3.4+ vs older versions), but lacks explicit validation checkpoints. No verification steps after configuration changes - user isn't told how to confirm logging is working correctly. | 2 / 3 |
Progressive Disclosure | Well-organized with clear sections progressing from quick setup to advanced topics. References to related skills at the end are one-level deep. Content is appropriately structured with a table of contents via headers. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (518 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- piomin/claude-ai-spring-boot
- Commit
d9fda23
- Reviewed
Table of Contents
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