# LOOM

> LOOM — palak11245-loom. Use this tool when you need to track and understand the reasoning behind code changes, or when integrating AI coding tools that require commit history context. LOOM solves the problem of lost knowledge and decision-making context in code development by inferring and storing the rationale behind changes. It provides a git-native interface for inputs and outputs, enabling seamless integration with existing development workflows.

Canonical page: https://skillsregistry.net/skills/palak11245-loom  
JSON: https://api.skillsregistry.net/v1/skills/palak11245-loom

## Description

A git-native decision journal that uses a local LLM to infer and store the rationale behind code changes, providing MCP tools for AI coding tools to query commit history.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ej12wcii9i)
- **Repository:** <https://github.com/Palak11245/LOOM>

## 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": "palak11245-loom"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/palak11245-loom` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/palak11245-loom/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