# kgteach

> kgteach — kaiqin04-kgteach. Use this tool when you need to analyze Go games and identify key mistakes, as it utilizes KataGo to review SGF files and provide insightful explanations. This CLI tool solves problems related to game improvement and strategy analysis, making it ideal for AI agents looking to enhance their Go gameplay. It takes SGF files as input and outputs detailed explanations of critical mistakes, making it a valuable resource for agents seeking to refine their skills.

Canonical page: https://skillsregistry.net/skills/kaiqin04-kgteach  
JSON: https://api.skillsregistry.net/v1/skills/kaiqin04-kgteach

## Description

CLI tool that lets AI agents analyze Go games with KataGo and explain key mistakes from SGF files

## Trust

- **Trust score (0–1):** 0.63
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/KaiQin04/kgteach)

## 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": "kaiqin04-kgteach"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/kaiqin04-kgteach` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/kaiqin04-kgteach/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