# leftpad-mcp

> leftpad-mcp — jkap-leftpad-mcp. Use this tool when you need to standardize Large Language Model (LLM) outputs by executing a series of five-line Python scripts to address inconsistency issues. It solves problems related to LLM reliability and accuracy by providing a structured approach to output generation. The tool takes Python scripts as input and produces standardized outputs, making it ideal for use cases where consistency is crucial, such as data processing and automation tasks.

Canonical page: https://skillsregistry.net/skills/jkap-leftpad-mcp  
JSON: https://api.skillsregistry.net/v1/skills/jkap-leftpad-mcp

## Description

the true solution to LLM inconsistency is to make it run a bunch of five line python scripts

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/jkap/leftpad-mcp)

## 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": "jkap-leftpad-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jkap-leftpad-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jkap-leftpad-mcp/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