# zigars

> zigars — oly-wan-kenobi-zigars. Use this tool when you need to streamline code development for Zig projects, as it provides structured compiler diagnostics and code intelligence through ZLS, enabling efficient preview-first refactors for AI coding agents. It accepts Zig code as input and outputs refined, compiled code, making it ideal for git-based workflows. This tool is particularly useful for AI agents seeking to optimize their coding processes and improve overall code quality.

Canonical page: https://skillsregistry.net/skills/oly-wan-kenobi-zigars  
JSON: https://api.skillsregistry.net/v1/skills/oly-wan-kenobi-zigars

## Description

Deterministic MCP server for Zig: structured compiler diagnostics, ZLS code intelligence, and preview-first refactors for AI coding agents

## Trust

- **Trust score (0–1):** 0.86
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/oly-wan-kenobi/zigars)

## 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": "oly-wan-kenobi-zigars"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/oly-wan-kenobi-zigars` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/oly-wan-kenobi-zigars/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