什么是 py-memory-optimizer？

py-memory-optimizer 是 Openclaw Skills 生态系统中的一个专用工具，旨在简化 Python 内存管理。通过利用 AST 模块进行静态分析，它可以在不执行代码的情况下识别潜在的内存泄漏、低效的对象创建以及不当的生成器使用。这使其成为开发者在开发阶段提高应用程序性能和资源效率的宝贵资产。

该技能通过提供具体的、可操作的建议，在原始代码与高性能执行之间架起了一座桥梁。无论您是在处理大型数据集还是长期运行的 Web 服务，整合 Openclaw Skills 库中的这一工具都能确保您的代码保持精简高效。

下载入口:https://github.com/openclaw/skills/tree/main/skills/martinforsulu/neo-py-memory-optimizer

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

py-memory-optimizer 应用场景

• 识别复杂 Python 项目中由未关闭的文件句柄或循环引用引起的内存泄漏。
• 通过将列表推导式转换为节省内存的生成器表达式，优化大规模数据处理脚本。
• 审计旧代码库，以确保符合现代 Python 内存最佳实践。
• 通过 Openclaw Skills CLI 接口将自动化内存分析集成到 CI/CD 流水线中。

1. 该工具将 Python 源文件解析为抽象语法树 (AST)，以便在不执行代码的情况下映射出代码结构。
2. 它会针对已知内存密集型模式（如不必要的全局变量积累）数据库对树进行扫描。
3. 检测引擎会识别潜在的资源泄漏，例如未关闭的上下文管理器或阻止垃圾回收的持久对象。
4. 该技能会生成一份详尽的报告，详细说明发现的问题、严重程度以及包含预估节省空间的特定代码优化建议。

py-memory-optimizer 配置指南

要开始使用 Openclaw Skills 库中的此工具，请通过 npm 全局安装：

npm install --global openclaw-skill-py-memory-optimizer

首次执行时，该工具会自动处理其 Python 后端的安装，包括 pydantic、rich 和 astroid 等依赖项。

py-memory-optimizer 数据架构与分类体系

该技能将发现的问题组织成结构化报告，以帮助开发者优先处理修复。数据分类如下：

name: py-memory-optimizer
description: Automatically analyzes Python code and suggests memory usage optimizations for improved performance

py-memory-optimizer Skill

Overview

This skill provides static analysis of Python code to identify memory-intensive patterns, potential memory leaks, and optimization opportunities. It generates actionable suggestions with estimated memory savings and best practice recommendations.

Core Capabilities

• Static Code Analysis: Parses Python source files using the AST module to analyze code structure without execution
• Pattern Detection: Identifies common memory-intensive patterns (large list comprehensions, unnecessary object creation, improper generator usage)
• Leak Detection: Finds potential memory leaks from circular references, unclosed resources, and global variable accumulation
• Optimization Suggestions: Provides specific, actionable recommendations with estimated memory impact
• Framework Support: Handles Python 3.8+ and common frameworks (Django, Flask, FastAPI patterns)
• CLI Interface: Command-line tool for integration into development workflows
• Report Generation: Creates detailed analysis reports in multiple formats (JSON, markdown, plain text)

Dependencies

System Requirements

• Python 3.8+
• pip package manager

Python Packages (installed automatically via package.json)

• astroid (optional, for enhanced AST analysis)
• tabulate (for formatted output)
• rich (for colored terminal output)
• click (for CLI interface)
• pydantic (for data validation)
• python-slugify (for report naming)

Installation

npm install --global openclaw-skill-py-memory-optimizer

The skill will automatically install Python dependencies on first run.

Usage

CLI Interface

# Analyze a single file
py-memory-optimizer analyze path/to/script.py

# Analyze entire directory
py-memory-optimizer analyze ./my_project --recursive

# Generate detailed report
py-memory-optimizer analyze script.py --format json --output report.json

# Show optimization suggestions with memory estimates
py-memory-optimizer analyze script.py --show-suggestions --estimate-savings

# Exclude specific patterns
py-memory-optimizer analyze . --exclude "*.test.py" "*/migrations/*"

# Version info
py-memory-optimizer --version

Integration with OpenClaw Agents

The skill can be called directly by OpenClaw agents:

Analyze this Python code for memory optimizations: 

# Bad: Creates entire list in memory
result = [process(item) for item in huge_dataset]

Line 15: Medium memory issue
  Pattern: Large list comprehension
  Current memory: ~O(N) for full list
  Suggestion: Use generator expression
  Optimized: (process(item) for item in huge_dataset)
  Estimated savings: 70-90% for large datasets

f = open('data.txt', 'r')
data = f.read()
# Missing f.close()

Line 8: High memory/resource issue
  Pattern: Unclosed file handle
  Risk: File descriptor leak, memory not freed
  Suggestion: Use context manager
  Optimized: with open('data.txt', 'r') as f:
                 data = f.read()
  Estimated savings: Prevents descriptor accumulation