# stump

> stump — hegner123-stump. Use this tool when you need to visualize directory trees efficiently, especially for large repositories, to reduce output size by 50% or more. It solves the problem of cumbersome and verbose directory listings, making it ideal for use cases like git repository exploration. The stump tool takes directory inputs and outputs a compact, token-efficient tree visualization, optimized for consumption by large language models (LLMs).

Canonical page: https://skillsregistry.net/skills/hegner123-stump  
JSON: https://api.skillsregistry.net/v1/skills/hegner123-stump

## Description

Token-efficient directory tree visualization MCP server in Zig - optimized for LLM consumption with 50%+ reduction vs standard tree output

## Trust

- **Trust score (0–1):** 0.92
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/hegner123/stump)

## 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": "hegner123-stump"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/hegner123-stump` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/hegner123-stump/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