A comprehensive performance diagnostics toolkit for profiling CPU usage, detecting memory leaks, and benchmarking application code across multiple languages.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install perf-profiler
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install perf-profiler using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Performance Profiler?
The Performance Profiler is a versatile tool designed for developers to diagnose and resolve application bottlenecks. By integrating various profiling engines for Node.js, Python, and Go, this skill provides actionable insights into code execution efficiency. Whether you are dealing with slow function execution, high memory overhead, or inefficient database queries, these Openclaw Skills empower you to visualize performance data through flame graphs and detailed metrics.
Beyond simple diagnostics, the tool facilitates proactive optimization through integrated benchmarking and load testing suites. Developers can simulate high-traffic scenarios using tools like Apache Bench or wrk to ensure stability before deployment. By leveraging these Openclaw Skills, teams can maintain high-performance standards throughout the software development lifecycle.
Performance Profiler Use Cases
- Identifying CPU-intensive functions and hot paths in production code.
- Detecting and fixing memory leaks in long-running Node.js or Python processes.
- Benchmarking new code changes against baselines to prevent performance regressions.
- Load testing REST APIs to determine throughput and latency under concurrent pressure.
- Analyzing database query execution plans to identify missing indexes or inefficient joins.
How Performance Profiler Works
- Select the target environment (Node.js, Python, or Go) and the specific metric you wish to measure such as CPU, Memory, or Network latency.
- Initialize the profiling session using built-in flags or external utilities like V8 inspector, cProfile, or pprof.
- Execute the workload or run the application under test to collect raw performance data and stack traces.
- Analyze the generated output using visualization tools such as interactive flame graphs or pprof dashboards.
- Compare results across different versions using automated benchmarking scripts to validate the impact of your optimizations.
Performance Profiler Setup
To get started with these Openclaw Skills, ensure you have the necessary runtimes and utilities installed for your specific project environment.
# Install general benchmarking tools (Linux)
sudo apt install apache2-utils # Provides 'ab'
# For Node.js interactive flame graphs
npx 0x --version
# For Python sampling profiling without code changes
pip install py-spy
Configuration is often as simple as passing a flag to your runtime, such as node --cpu-prof app.js or importing profiling hooks in your Go application.
Performance Profiler Data Schema & Taxonomy
The Performance Profiler skill generates and analyzes various report formats depending on the profiling method selected. These Openclaw Skills organize data as follows:
| Format | Source | Primary Purpose |
|---|---|---|
.cpuprofile |
Node.js / V8 | Detailed CPU execution time visualization in Chrome DevTools. |
.heapsnapshot |
Node.js / V8 | Deep-dive memory analysis to identify retained objects and leaks. |
.prof |
Python cProfile | Statistical analysis of function call counts and cumulative time. |
.svg |
py-spy / 0x | Interactive Flame Graph for visualizing stack depth and hot paths. |
.json |
Hyperfine / Autocannon | Structured benchmark results for automated regression testing. |
Performance Profiler Advanced Features
- Multi-language support covering the unique profiling requirements of Node.js, Python, and Go ecosystems.
- Integration with sampling profilers like py-spy for low-overhead production monitoring and diagnostics.
- Automated comparison scripts to calculate statistical significance (avg, stddev) between code versions.
- Support for advanced load testing with Lua scripting to generate dynamic, high-concurrency request patterns.
- Real-time memory growth monitoring tools that alert developers when heap usage exceeds defined thresholds.
SKILL.md
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