A comprehensive performance profiling and analysis tool designed to identify code bottlenecks and provide actionable optimization recommendations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-perf
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 openclaw-perf 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 Analysis & Optimization?
The Performance skill for Openclaw Skills is a developer-centric toolkit focused on maximizing application efficiency. It enables developers to profile code execution at a granular level, pinpointing exactly where time and resources are being spent. By combining static analysis with dynamic profiling, this skill helps bridge the gap between identifying a slow application and implementing a high-performance solution.
This utility is specifically built to handle modern development workflows, offering features that range from simple function timing to complex algorithmic bottleneck detection. Whether you are dealing with inefficient loops, database query patterns, or memory-intensive operations, this skill provides the necessary data to make informed architectural decisions.
Performance Analysis & Optimization Use Cases
- Identifying high-latency hotspots in Python functions and modules during development.
- Detecting and fixing common anti-patterns like N+1 queries and string concatenation in loops.
- Running regression benchmarks to compare the performance of different algorithmic approaches.
- Integrating automated performance checks into pull request workflows to maintain software quality.
How Performance Analysis & Optimization Works
- The user executes the profile command targeting a specific file or function to begin execution tracking.
- The system monitors call counts and execution times, identifying the top hotspots within the code.
- The analyzer engine performs static code analysis to detect inefficient patterns such as nested O(n²) loops.
- Detailed reports are generated, providing performance metrics like Mean, Median, P95, and P99 latency.
- The skill suggests specific code-level optimizations based on recognized best practices and efficiency rules.
Performance Analysis & Optimization Setup
To get started with this Performance skill for Openclaw Skills, ensure you have your script environment ready. You can configure the behavior of the tool using a .performance.json file in your project root.
# Analyze a directory for performance bottlenecks
python3 scripts/main.py analyze --dir src/
# Run a benchmark for a specific function
python3 scripts/main.py benchmark --file src/sort.py --function quicksort
Performance Analysis & Optimization Data Schema & Taxonomy
The skill manages performance data through configuration files and script-based logic to generate execution reports.
| Component | Type | Description |
|---|---|---|
| .performance.json | Configuration | Defines benchmark iterations, warmup cycles, and active check patterns. |
| main.py | Entry Point | The unified CLI interface for profiling, analysis, and benchmarking. |
| profiler.py | Logic | Handles the runtime execution tracking and hotspot identification. |
| analyzer.py | Logic | Contains the rules engine for detecting bottlenecks like N+1 queries. |
Performance Analysis & Optimization Advanced Features
- CI/CD Integration: Seamlessly integrate performance gating into GitHub Actions to block slow code from being merged.
- Statistical Benchmarking: Detailed latency distribution metrics including P95 and P99 percentiles for production-grade testing.
- Pattern Recognition: Automatic detection of Python-specific optimizations like list comprehensions vs. map/filter and generator usage.
- Database Optimization: Identifies inefficient database access patterns such as missing JOINs or select_related calls.
SKILL.md
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