Parse performance trace data to identify bottlenecks and resource utilization.
npx skills add https://github.com/SteeZyT33/perf-lab-plugin --skill analyze-steezyt33
Please help me install this Agent Skill. Skill: analyze Source: https://github.com/SteeZyT33/perf-lab-plugin/tree/main/skills/analyze Command: npx skills add https://github.com/SteeZyT33/perf-lab-plugin --skill analyze-steezyt33
This Skill helps pinpoint performance bottlenecks in code by analyzing trace data, identifying resource utilization issues, and suggesting optimization opportunities.
Analyze the performance trace data by running the analyze skill.
High-intent search queries and answers about installing and using this skill.
To identify performance bottlenecks from trace data, parse the trace output to evaluate resource utilization and pinpoint binding constraints. This process highlights critical cycle utilization gaps and idle stretches that limit software performance.
Low cycle utilization and idle stretches during performance profiling are caused by resource underutilization and binding constraints. Analyzing trace data helps identify these limiting factors within your code execution.
Compare current trace analysis results against previous performance runs by parsing new trace output to identify binding constraints. This highlights how current resource utilization and bottlenecks differ from past analyses.
Yes, you can find optimization opportunities after a performance regression by analyzing trace output. The analysis identifies the new bottleneck, assesses resource underutilization, and suggests where to focus optimization efforts.
Yes, trace analysis works for software profiling without external dependencies. It parses performance trace output directly to determine resource utilization and identify the primary binding constraints limiting performance.
dawsonblockCapture Unreal Engine performance traces into .utrace files and export timing and counter data to CSV.
acaprinoProfile Python CPU and memory usage with cProfile, memory_profiler, and line_profiler.
gaebalaiDesign system monitoring metrics using RED and USE methodologies.
fcouryAnalyze Rust libraries for runtime performance and memory optimization opportunities.
