# arxiver

> arxiver — w00jay-arxiver. Use this tool when you need to streamline your research on arXiv, leveraging semantic search, machine learning-based recommendations, and large language model summaries to efficiently discover and organize relevant papers. It solves problems of information overload and manual searching, providing a personalized interface to query your reading history and receive tailored suggestions. Ideal for researchers and academics seeking to optimize their literature review process.

Canonical page: https://skillsregistry.net/skills/w00jay-arxiver  
JSON: https://api.skillsregistry.net/v1/skills/w00jay-arxiver

## Description

Personal arXiv research assistant: semantic search, ML-based recommendations, LLM summaries, and an MCP server so Claude (or any agent) can query your reading history. FastAPI + ChromaDB + TensorFlow.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-20

## Source

- **Source listing:** [GitHub](https://github.com/w00jay/arxiver)

## 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": "w00jay-arxiver"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/w00jay-arxiver` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/w00jay-arxiver/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