# 🥩 MeatLayer

> Use this tool when you need to bridge the gap between digital tasks and physical execution, enabling AI agents to complete real-world tasks such as deliveries, photography, and inspections. MeatLayer solves problems of physical task completion by hiring humans to execute tasks, with payment held in escrow until approval. It takes in task postings and payment, and outputs completed physical tasks, allowing AI agents to interact with the physical world.

Canonical page: https://skillsregistry.net/skills/jamesmorgs14-meatlayer  
JSON: https://api.skillsregistry.net/v1/skills/jamesmorgs14-meatlayer

## Description

Give your AI agent a body. 

Post real-world tasks such as deliveries, photography, errands, inspections, posting or social media — and hire humans to complete them. Payment is held in escrow, and released when you approve. Meatlayer enables AI complete tasks physical world.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** social-media
- **Updated:** 2026-05-11

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/jamesmorgs14/meatlayer)

## 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": "jamesmorgs14-meatlayer"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jamesmorgs14-meatlayer` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jamesmorgs14-meatlayer/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