A static analysis tool for Python that identifies memory-intensive patterns and suggests actionable performance optimizations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install neo-py-memory-optimizer
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 neo-py-memory-optimizer 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 py-memory-optimizer?
py-memory-optimizer is a specialized tool within the Openclaw Skills ecosystem designed to streamline Python memory management. By leveraging static analysis through the AST module, it identifies potential memory leaks, inefficient object creations, and improper generator usage without requiring code execution. This makes it an invaluable asset for developers looking to improve application performance and resource efficiency during the development phase.
This skill provides a bridge between raw code and high-performance execution by offering specific, actionable recommendations. Whether you are dealing with large datasets or long-running web services, incorporating this tool from the Openclaw Skills library ensures your code remains lean and efficient.
py-memory-optimizer Use Cases
- Identifying memory leaks caused by unclosed file handles or circular references in complex Python projects.
- Optimizing large-scale data processing scripts by converting list comprehensions into memory-efficient generator expressions.
- Auditing legacy codebases for compliance with modern Python memory best practices.
- Integrating automated memory analysis into CI/CD pipelines via the Openclaw Skills CLI interface.
How py-memory-optimizer Works
- The tool parses Python source files into an Abstract Syntax Tree (AST) to map out the code structure without executing the code.
- It scans the tree against a database of known memory-intensive patterns such as unnecessary global variable accumulation.
- A detection engine identifies potential resource leaks like unclosed context managers or persistent objects that prevent garbage collection.
- The skill generates a comprehensive report detailing findings, severity levels, and specific code-based optimization suggestions with estimated savings.
py-memory-optimizer Setup
To begin using this tool from the Openclaw Skills library, install it globally via npm:
npm install --global openclaw-skill-py-memory-optimizer
Upon the first execution, the tool automatically handles the installation of its Python backend, including dependencies like pydantic, rich, and astroid.
py-memory-optimizer Data Schema & Taxonomy
The skill organizes findings into structured reports to help developers prioritize fixes. Data is categorized as follows:
| Data Point | Description |
|---|---|
| Memory Issue Summary | A high-level count of issues categorized by severity (Critical, High, Medium, Low). |
| Detailed Findings | Specific file paths and line numbers mapped to detected patterns and code examples. |
| Memory Impact Estimate | A calculated projection of potential memory savings for each suggestion. |
| Statistics | Total objects analyzed and aggregate potential memory recovered. |
py-memory-optimizer Advanced Features
- Recursive directory scanning for auditing entire project structures at once.
- Multi-format export capabilities allowing for JSON, Markdown, or plain text reporting.
- Customizable exclusion filters to skip test suites, migrations, or third-party libraries.
- Direct integration with OpenClaw agents for interactive, AI-assisted code refactoring and optimization.
SKILL.md
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