# rag-eval

> Use this tool when you need to assess the performance of your Retrieval-Augmented Generation (RAG) pipeline, solving problems of inaccurate or irrelevant generated text. It evaluates faithfulness, answer relevancy, and context precision using Ragas metrics, taking in RAG pipeline outputs and returning quality assessment scores. Ideal for use during model development and fine-tuning to ensure high-quality generated text.

Canonical page: https://skillsregistry.net/skills/jonathanjing-rag-eval  
JSON: https://api.skillsregistry.net/v1/skills/jonathanjing-rag-eval

## Description

Evaluate your RAG pipeline quality using Ragas metrics (faithfulness, answer relevancy, context precision).

## 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:** devops-ci
- **Updated:** 2026-05-18

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/jonathanjing-rag-eval)

## 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": "jonathanjing-rag-eval"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jonathanjing-rag-eval` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jonathanjing-rag-eval/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