Do I need debug symbols for this skill to work?

Yes, the skill explicitly verifies debug symbols before profiling to ensure that hotspot data includes accurate file and line-number information.

What profiling tools does this skill support?

The Performance Profiler is designed to interface with standard CLI tools such as Linux perf, Java Flight Recorder (JFR), and other system-level profilers.

Can it generate visual reports?

Yes, it is optimized to provide flame graphs or equivalent visual artifacts whenever possible to help visualize performance bottlenecks.

What is the primary output of the Performance Profiler?

The skill provides a structured summary including the tool used, the command executed, a list of hotspots with file locations, and paths to generated artifacts.

Performance Profiler

Name: Performance Profiler
Author: avifenesh

byavifenesh

•

311

분석 및 모니터링

Profiles CPU and memory hot paths to generate flame graphs and evidence-backed performance reports.

소개

Performance Profiler is a specialized Claude Code skill designed to identify system bottlenecks and optimize code execution. It automates the process of running profiling tools like JFR or perf, ensuring debug symbols are verified and capturing precise file-and-line location data for hotspots. By generating flame graphs and structured evidence, it provides developers with actionable insights to resolve performance regressions and optimize high-traffic code paths with minimal overhead.

주요 기능

Debug symbol verification for accurate profiling results
Flame graph and JFR/perf evidence generation
311 GitHub stars
Automated CPU and memory hotspot identification
Precise file-and-line mapping for performance bottlenecks
Structured artifact output for easy data analysis

사용 사례

Debugging memory leaks and CPU spikes in production-like environments
Capturing performance evidence for architectural reviews or regression testing
Optimizing high-frequency code paths to reduce execution latency

소개

주요 기능

Debug symbol verification for accurate profiling results
Flame graph and JFR/perf evidence generation
311 GitHub stars
Automated CPU and memory hotspot identification
Precise file-and-line mapping for performance bottlenecks
Structured artifact output for easy data analysis

사용 사례

Debugging memory leaks and CPU spikes in production-like environments
Capturing performance evidence for architectural reviews or regression testing
Optimizing high-frequency code paths to reduce execution latency