Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Analyzes Python source files using AST-based static analysis to detect memory-intensive patterns, potential leaks, and inefficient code. Returns findings with line numbers, severity ratings, and concrete optimization suggestions including estimated memory savings.
Catches memory issues without running the code, so it works in CI pipelines or on code that is expensive or unsafe to execute.
When to use it
- Auditing a Django app before deploying to a memory-constrained server
- Finding unclosed file handles in a data processing script
- Converting large list comprehensions to generators in batch jobs
- Generating a memory optimization report for a code review
- Scanning a legacy codebase recursively for circular reference risks
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Analyze this Python script for memory issues and show suggestions with estimated savings
- 1Receives the Python source code from the user
- 2Invokes py-memory-optimizer analyze on the provided file with --show-suggestions --estimate-savings flags
- 3AST parser scans the code for patterns: large list comprehensions, unclosed resources, global variable accumulation
- 4Ranks findings by severity (critical, high, medium, low) and attaches line numbers
- 5Compiles per-issue optimization suggestions with code examples and estimated savings percentages
Structured report listing each memory issue by line number, its severity, a description of the pattern, a corrected code snippet, and an estimated memory savings range