# fava-trails

> fava-trails — machinewisdomai-fava-trails. Use this tool when you need to manage AI agent memory and knowledge retention through a Git-native, curated system. It solves problems of information overload and knowledge fragmentation by providing draft isolation, promotion gates, and memory curation protocols. Ideal for use cases requiring version-controlled thought lifecycles and supersession chains, with inputs including AI-generated content and outputs of refined, curated knowledge.

Canonical page: https://skillsregistry.net/skills/machinewisdomai-fava-trails  
JSON: https://api.skillsregistry.net/v1/skills/machinewisdomai-fava-trails

## Description

🫛👣 FAVA Trails — Git-native, curated memory for AI agents via MCP. Draft isolation, promotion gate, thought lifecycle hooks, memory curation protocols, supersession chains.

## 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:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/MachineWisdomAI/fava-trails)

## 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": "machinewisdomai-fava-trails"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/machinewisdomai-fava-trails` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/machinewisdomai-fava-trails/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