# Argyph

> Argyph — ezzy1630-argyph. Use this tool when you need to provide AI coding agents with fast and structured context over a codebase, enabling them to understand and work with code more effectively. Argyph solves the problem of limited context for AI agents, allowing them to navigate and generate code with greater accuracy. It takes in a codebase as input, typically from a git repository, and outputs semantic context for AI agents to utilize.

Canonical page: https://skillsregistry.net/skills/ezzy1630-argyph  
JSON: https://api.skillsregistry.net/v1/skills/ezzy1630-argyph

## Description

Local-first MCP server giving AI coding agents fast, structured, and semantic context over any codebase. Zero config, zero cloud, full context.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-27

## Source

- **Source listing:** [GitHub](https://github.com/ezzy1630/Argyph)

## 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": "ezzy1630-argyph"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ezzy1630-argyph` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ezzy1630-argyph/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