# PolicyAgents

> PolicyAgents — guangxiangdebizi-policyagents. Use this tool when you need to analyze policy texts in-depth and require a seamless user interaction experience. PolicyAgents solves the problem of complex policy text interpretation by leveraging LangGraph, LangChain, and MCP technologies. It takes policy texts as input and outputs detailed analysis results, making it ideal for applications where nuanced understanding of policies is crucial.

Canonical page: https://skillsregistry.net/skills/guangxiangdebizi-policyagents  
JSON: https://api.skillsregistry.net/v1/skills/guangxiangdebizi-policyagents

## Description

基于 LangGraph + LangChain + MCP 的政策文本深度分析工具，提供极致的用户交互体验。

## Trust

- **Trust score (0–1):** 0.79
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/guangxiangdebizi/PolicyAgents)

## 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": "guangxiangdebizi-policyagents"
    }
  }
}
```

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