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perf

Profile CPU, memory, and concurrency bottlenecks in Node.js, Python, Go, Rust, and Java services.

26|8|Updated Mar 19, 2026
Search Tags:#performance#optimization#benchmarking#memory#profiling#cpu#flame-graph
One-click install
npx skills add https://github.com/arbazkhan971/godmode --skill perf-arbazkhan971
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: perf
Source: https://github.com/arbazkhan971/godmode/tree/main/skills/perf
Command: npx skills add https://github.com/arbazkhan971/godmode --skill perf-arbazkhan971

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profiling and optimizing performance bottlenecks in CPU, memory, and concurrency to improve application throughput and responsiveness.

Core Features & Use Cases

  • CPU and memory profiling to locate bottlenecks.
  • Concurrency analysis and race condition detection.
  • Benchmarking with statistically rigorous results; stop conditions and regression guards.
  • Flame graphs and reports for actionable optimization.

Quick Start

Run a profiling pass to identify the top CPU hotspots, then apply iterative optimizations and validate gains.

Frequently Asked Questions about perf

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find CPU and memory bottlenecks in my application?▼

CPU and memory profiling identifies performance bottlenecks by analyzing application hotspots during development or production loads. It pinpoints exact resource-intensive operations for iterative optimization and throughput improvement.

Can I profile concurrency issues and race conditions in Node.js or Python services?▼

Concurrency profiling detects race conditions in Node.js, Python, Go, Rust, and Java services. It analyzes concurrent execution paths to locate thread safety issues and optimize responsiveness under production loads.

What is the best way to benchmark code optimizations and prevent regressions?▼

Rigorous benchmarking validates optimization improvements by applying statistical stop conditions and regression guards. This ensures measured performance gains are objective and prevents degradation across iterations.

How do I generate flame graphs to visualize performance hotspots?▼

Flame graphs visualize CPU hotspots by aggregating profiling data into actionable reports. They map call stacks to highlight resource-intensive functions for targeted code optimization.

Does performance profiling work across multiple programming languages?▼

Performance profiling applies to Node.js, Python, Go, Rust, and Java services. It analyzes CPU, memory, and concurrency bottlenecks across these environments during active development or production loads.

When should I use statistical stop conditions during benchmarking?▼

Statistical stop conditions terminate benchmarking once results reach objective significance, preventing endless runs. They validate that iterative optimization gains are rigorous and not caused by measurement noise.

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