# rag-project

> rag-project — pgalonza-rag-project. Use this tool when you need to generate text based on relevant information retrieved from a knowledge base, solving problems like content creation and data-driven writing. It takes in prompts and context as input and outputs generated text, leveraging git for version control and collaboration. Ideal for use cases where accurate and informative content generation is required, such as automated reporting and document creation.

Canonical page: https://skillsregistry.net/skills/pgalonza-rag-project  
JSON: https://api.skillsregistry.net/v1/skills/pgalonza-rag-project

## Description

Retrieval-Augmented Generation project

## Trust

- **Trust score (0–1):** 0.73
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/pgalonza/rag-project)

## 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": "pgalonza-rag-project"
    }
  }
}
```

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