# canopy

> canopy — riskytrees-canopy. Use this tool when you need to manage and regulate the behavior of AI agents and their interactions with various tools and systems. Canopy solves policy enforcement and compliance problems by allowing you to write and enforce custom policies on agentic tool flows. It integrates with git, accepting tool flow definitions as input and outputting enforced policy-compliant flows.

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

## Description

Canopy allows you to write and enforce policies on agentic tool flows

## Trust

- **Trust score (0–1):** 0.84
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/riskytrees/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": "riskytrees-canopy"
    }
  }
}
```

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