# policy-engine

> policy-engine — joetomasone-policy-engine. Use this tool when you need to enforce consistent governance and decision-making across OpenClaw tool executions, solving problems of inconsistent policy application and non-compliant workflows. The policy-engine takes in defined policies and tool execution requests as inputs, producing compliant execution outputs. It is ideal for use cases requiring deterministic and automated governance in complex tool workflows.

Canonical page: https://skillsregistry.net/skills/joetomasone-policy-engine  
JSON: https://api.skillsregistry.net/v1/skills/joetomasone-policy-engine

## Description

Deterministic governance layer for OpenClaw tool execution.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-05-18

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** other
- **Updated:** 2026-09-22

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/joetomasone-policy-engine)

## 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": "joetomasone-policy-engine"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/joetomasone-policy-engine` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/joetomasone-policy-engine/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