# Mesh

> Mesh — billighost-mesh. Use this tool when you need to enable AI agents to retain knowledge across sessions and collaborate with each other seamlessly. Mesh solves the problem of knowledge loss between sessions and facilitates inter-agent collaboration by providing a shared semantic memory layer. It takes in AI agent data and outputs a unified knowledge base, ideal for use cases requiring persistent and shared knowledge, such as multi-agent systems and continuous learning environments.

Canonical page: https://skillsregistry.net/skills/billighost-mesh  
JSON: https://api.skillsregistry.net/v1/skills/billighost-mesh

## Description

Shared semantic memory layer for AI agents — enabling cross-session knowledge persistence and seamless inter-agent collaboration.

## 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:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/billighost/Mesh)

## 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": "billighost-mesh"
    }
  }
}
```

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