# lore

> lore — catdarick-lore. Use this tool when you need to efficiently develop and manage Haskell codebases with compiler-aware functionality, solving problems related to code optimization and version control through git integration, and providing inputs such as Haskell code and outputs like optimized and compiled code. It streamlines development workflows for AI agents working on Haskell projects. This tool is ideal for contexts where compiler efficiency and git version control are crucial.

Canonical page: https://skillsregistry.net/skills/catdarick-lore  
JSON: https://api.skillsregistry.net/v1/skills/catdarick-lore

## Description

Context-efficient, compiler-aware development tools for AI agents working on Haskell codebases.

## Trust

- **Trust score (0–1):** 0.80
- **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/catdarick/lore)

## 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": "catdarick-lore"
    }
  }
}
```

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