# codeskeleton

> Use this tool when you need to efficiently process Python code, as it provides file skeletons and fetches only necessary implementations, reducing noise and cost. It solves problems of code complexity and unnecessary data transfer, making it ideal for AI agents that require streamlined code analysis. The codeskeleton tool takes in Python code files as input and outputs optimized, relevant code implementations.

Canonical page: https://skillsregistry.net/skills/dugd-codeskeleton  
JSON: https://api.skillsregistry.net/v1/skills/dugd-codeskeleton

## Description

Enables AI agents to efficiently read Python code by first providing file skeletons then fetching only needed implementations, reducing noise and cost.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/s4ccoupn24)
- **Repository:** <https://github.com/dugd/codeskeleton>

## 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": "dugd-codeskeleton"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/dugd-codeskeleton` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/dugd-codeskeleton/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