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: <code>

The agent will invoke the analyzer and return structured suggestions.

Output Format

The analyzer produces:

• Memory Issue Summary: Count of issues by severity (critical, high, medium, low)
• Detailed Findings: For each issue:
• File and line number
• Issue type and description
• Memory impact estimate
• Specific optimization suggestion with code example
• Overall Statistics: Total memory potentially saved, number of objects analyzed
• Best Practices Checklist: Compliance with Python memory optimization guidelines

Examples

Example 1: Large List Comprehension

Input:

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

Output:

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

Example 2: Unclosed File Handles

Input:

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

Output:

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

Out of Scope

• Runtime profiling (use memory_profiler for that)
• C extension analysis
• Database or external service optimization
• Automatic code modification (this skill only suggests)
• Mixed-language project optimization

Files

This skill provides:

• SKILL.md: This documentation
• package.json: NPM package definition
• README.md: Quick start guide
• scripts/main.py: CLI entry point
• scripts/analyzer.py: Core analysis engine
• scripts/optimizer.py: Suggestion generator
• scripts/utils.py: AST utilities and helpers
• references/memory_patterns.md: Pattern reference
• references/best_practices.md: Best practices guide
• assets/sample_code/: Example files for testing

License

MIT

Support

For issues and feature requests, visit: https://github.com/openclaw/skills/issues