# agent-canopy

> Use this tool when you need to manage and schedule AI agent tasks, track file changes, and monitor execution progress in a centralized manner. The agent-canopy provides a single binary MCP server with no dependencies, allowing for seamless integration with git repositories. It simplifies task management and execution tracking, making it ideal for automating workflows and streamlining AI agent operations.

Canonical page: https://skillsregistry.net/skills/univerlab-agent-canopy  
JSON: https://api.skillsregistry.net/v1/skills/univerlab-agent-canopy

## Description

MCP server for AI agent task scheduling, file watching, and execution tracking. Single binary, no dependencies

## 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:** 2026-04-22

## Source

- **Source listing:** [GitHub](https://github.com/UniverLab/agent-canopy)

## 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": "univerlab-agent-canopy"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/univerlab-agent-canopy` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/univerlab-agent-canopy/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