# prolog-reasoner

> prolog-reasoner — rikarazome-prolog-reasoner. Use this tool when you need to perform logical reasoning and calculations using SWI-Prolog, solving problems that require rule-based decision-making and inference. It takes logical expressions and rules as input and outputs derived conclusions, making it suitable for large language models (LLMs) and other applications requiring logical reasoning. Ideal for use cases involving knowledge graph querying, expert systems, and automated decision-making.

Canonical page: https://skillsregistry.net/skills/rikarazome-prolog-reasoner  
JSON: https://api.skillsregistry.net/v1/skills/rikarazome-prolog-reasoner

## Description

SWI-Prolog as a logic calculator for LLMs — MCP server and Python library

## Trust

- **Trust score (0–1):** 0.45
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/rikarazome/prolog-reasoner)

## 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": "rikarazome-prolog-reasoner"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/rikarazome-prolog-reasoner` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/rikarazome-prolog-reasoner/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