# runa

> runa — tesserine-runa. Use this tool when you need to manage and enforce methodology-driven AI workflows, loading manifests and validating artifacts to ensure dependency graphs are correctly enforced. It solves problems of workflow integrity and consistency, particularly in complex AI ecosystems like Tesserine. The runa cognitive runtime accepts git-based inputs and outputs validated, methodology-compliant AI agent configurations.

Canonical page: https://skillsregistry.net/skills/tesserine-runa  
JSON: https://api.skillsregistry.net/v1/skills/tesserine-runa

## Description

Cognitive runtime for methodology-managed AI agents. Loads manifests, validates artifacts, enforces dependency graphs. The enforcement layer of the Tesserine ecosystem.

## 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:** ai-ml
- **Updated:** 2026-09-20

## Source

- **Source listing:** [GitHub](https://github.com/tesserine/runa)

## 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": "tesserine-runa"
    }
  }
}
```

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

---
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