Use when you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. This should trigger for requests such as Verify performance fix; Verify the performance; Verify the memory; Verify the thread
SkillScore86Maintained
Automatic
◆Coding pack
Last verified 8/20/2026
What it changes
Passed 18 of 21 test cases with the skill, and 13 of 21 without it.
Without skill64%
With skill86%
Checks it passed that the model alone missed
Refers to references/164-java-profiling-verify.md
Requires recording the exact load patterns and test scenarios in the report
Asks a clarifying question regarding the ambiguous request scope before applying changes
and 2 more in the full report
5 fixed14 passed either way3 still failing0 broke
Pass-rate lift
+23 pp
Tokens
-20%
Agent turns
0%
Response time
-52%
Model gemini-3.6-flashMeasured 8/20/2026v119 of 22 cases passed with skill · 21 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
We want to generate the final comparison docs. What is the mandatory first step before initiating post-refactoring profiling data generation?
What was checked, without and with the skill
Checked
Without skill
With skill
Requires reading references/164-java-profiling-verify.md and confirming baseline artifacts exist and are non-empty
Missed
Passed
case-17 · gemini-3.6-flash · 8/20/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md59 lines2 files
Java Profiling Workflow / Step 4 / Verify results
Verify performance optimizations through rigorous before/after comparison: ensure baseline and post-refactoring profiling data use identical test conditions, generate post-refactoring reports, compare metrics (memory, CPU, GC, threading), perform side-by-side flamegraph analysis, document findings in profiling-comparison-analysis-YYYYMMDD.md and profiling-final-results-YYYYMMDD.md, and validate success criteria.
What is covered in this Skill?
Pre-refactoring baseline: run profiler with same load before changes
Post-refactoring: generate new reports with identical test conditions
Use when you need to implement or improve Java metrics observability with Micrometer — including meter design, naming/tag conventions, cardinality control, timers/counters/gauges/distribution summaries, percentiles/histograms, Actuator/Prometheus integration, and metrics validation through tests. This should trigger for requests such as Improve metrics; Apply Micrometer; Add metrics observability; Refactor Micrometer instrumentation; Add Micrometer timers counters or gauges to Java services. Par