# Instruction MCP Server

> Use this tool when you need to provide large language models (LLMs) with structured guidance and knowledge. The Instruction MCP Server serves markdown instructions as tools, offering team playbooks, coding standards, domain knowledge, and personal workflows through version-controlled files. It solves problems related to LLM training and deployment by providing a standardized interface for inputting and outputting domain-specific information.

Canonical page: https://skillsregistry.net/skills/rach-instruction  
JSON: https://api.skillsregistry.net/v1/skills/rach-instruction

## Description

Serves markdown instructions as tools to provide LLMs with team playbooks, coding standards, domain knowledge, and personal workflows through version-controlled files.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-06-12

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ddwjxwjcvm)
- **Repository:** <https://github.com/rach/instruction>

## 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": "rach-instruction"
    }
  }
}
```

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