# neo-py-memory-optimizer

> Use this tool when you need to optimize Python code for better performance by reducing memory usage. It analyzes code and provides suggestions for improvement, solving problems like slow execution and memory errors. Ideal for use cases where memory-intensive Python applications are impacting system performance, it takes in Python code as input and outputs optimized code recommendations.

Canonical page: https://skillsregistry.net/skills/martinforsulu-neo-py-memory-optimizer  
JSON: https://api.skillsregistry.net/v1/skills/martinforsulu-neo-py-memory-optimizer

## Description

Automatically analyzes Python code and suggests memory usage optimizations for improved performance.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** other
- **Updated:** 2026-05-21

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/martinforsulu-neo-py-memory-optimizer)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "martinforsulu-neo-py-memory-optimizer"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/martinforsulu-neo-py-memory-optimizer` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/martinforsulu-neo-py-memory-optimizer/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt
