# Archive Agent

> Use this tool when you need to search and analyze large document collections using AI-powered semantic understanding. The Archive Agent solves problems of information retrieval and document management by providing OCR and AI search capabilities, enabling users to query file contents using natural language. It accepts various file types as input and outputs relevant search results, making it ideal for use cases involving intelligent document indexing and analysis.

Canonical page: https://skillsregistry.net/skills/shredgineer-archive-agent  
JSON: https://api.skillsregistry.net/v1/skills/shredgineer-archive-agent

## Description

Archive Agent is an open-source semantic file tracker with OCR and AI search capabilities built by Dr.-Ing. Paul Wilhelm using Python, featuring RAG (Retrieval-Augmented Generation) functionality and MCP server integration. The implementation combines Qdrant vector database for semantic search, spaCy for natural language processing, PyMuPDF for PDF handling, Streamlit for web interface, and multiple AI provider support (OpenAI, Ollama, LM Studio) to enable intelligent document indexing, OCR processing of images and PDFs, and natural language querying of file contents. Designed for users who need to search and analyze large document collections using AI-powered semantic understanding, with features like watchlist monitoring, profile management, caching, and both CLI and GUI interfaces for flexible document management workflows.

## Trust

- **Trust score (0–1):** 0.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/shredgineer-archive-agent)
- **Repository:** <https://github.com/shredengineer/archive-agent>

## 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": "shredgineer-archive-agent"
    }
  }
}
```

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