# agent-ground

> agent-ground — arafel187-agent-ground. Use this tool when you need to verify the factual accuracy of claims and ground them in deterministic truth, solving problems of misinformation and data inconsistency in AI-to-AI (A2A) and multi-agent (MCP) interactions. It takes in claims and factual data as inputs and outputs verified truths, providing a reliable interface for cross-checking and validation. Ideal for use in applications requiring high-confidence decision making and information exchange.

Canonical page: https://skillsregistry.net/skills/arafel187-agent-ground  
JSON: https://api.skillsregistry.net/v1/skills/arafel187-agent-ground

## Description

Deterministic factual grounding and claim cross-check verifier for AI agents (A2A & MCP)

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/Arafel187/agent-ground)

## 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": "arafel187-agent-ground"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/arafel187-agent-ground` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/arafel187-agent-ground/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