perf-profiler

What This Skill Does

The perf-profiler skill serves as a comprehensive suite for measuring, diagnosing, and optimizing application performance. It empowers OpenClaw to analyze CPU spikes, detect memory leaks, and generate performance reports. By providing tools for both high-level benchmarking (like load testing and timing) and low-level diagnostic output (like heap snapshots and CPU profiles), the agent can act as a senior performance engineer. Whether you are dealing with a slow Node.js backend, a memory-hungry Go service, or inefficient Python scripts, this skill provides the necessary interface to visualize execution paths and reduce latency.

Installation

To integrate this skill into your environment, use the command-line interface provided by ClawHub:

clawhub install openclaw/skills/skills/gitgoodordietrying/perf-profiler

Ensure that you have the necessary runtime dependencies (such as Node.js, Go, or shell tools like hyperfine) installed on your system if you intend to use specific profiling sub-commands.

Use Cases

This skill is indispensable in several scenarios:

• Latency Troubleshooting: If your API endpoints are exceeding their SLAs, use perf-profiler to identify which specific functions or database queries are creating bottlenecks.
• Memory Leak Hunting: For long-running services that crash after days of operation, you can utilize the heap snapshot comparison features to see which objects are failing to be garbage collected.
• Performance Regression Testing: Before deploying a major code refactor, run hyperfine benchmarks to ensure the new version is at least as fast as the original.
• Architecture Visualization: Generate flame graphs to visualize the call stack, helping developers understand which parts of their application are doing the 'heavy lifting' and prioritizing where to apply optimization efforts.

Example Prompts

1. "Perform a load test on my API endpoint /v1/data and compare the average response time against the baseline from last week."
2. "My Node.js application is consuming increasing amounts of RAM over time. Generate a heap snapshot, identify potential memory leaks, and suggest a code fix."
3. "Analyze this Python script and identify the slowest function call; then, generate a flame graph so I can visualize the hotspots."

Tips & Limitations

• Production Caution: Always be careful when running CPU profiling in production environments, as the overhead can sometimes skew results or temporarily slow down the target process. When possible, perform intensive profiling in a staging environment that mirrors your production configuration.
• Warmup Periods: When benchmarking, always include a warmup phase. JVM and V8 engines optimize code during runtime; initial runs are almost always slower than subsequent ones.
• Tooling: While perf-profiler simplifies the process, ensure your environment variables and paths are correctly set for hyperfine and other CLI tools to function correctly.