Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
test
What This Skill Does
ArXivKB crawls arXiv's public API by category, downloads PDFs, and indexes their full text using FAISS vector search with Ollama embeddings. Semantic search runs against chunked PDF content rather than just abstracts, and all data stays on-device in SQLite and FAISS files.
Indexes full PDF text in 500-token chunks rather than just abstracts, so searches find papers by what they actually discuss, not only how they were titled or tagged.
When to use it
- Tracking new ML papers published in specific arXiv categories each week
- Searching paper content by concept when you can't recall the title or authors
- Building a private research library without uploading PDFs to any cloud service
- Reviewing all papers ingested on a topic over a custom date range
- Checking how many papers and chunks are indexed across enabled categories
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Find recent papers about contrastive learning in vision transformers
- 1Run `/akb add cs.CV cs.LG` to enable relevant categories
- 2Run `akb ingest --days 14` to crawl arXiv, download PDFs, and embed chunks
- 3Query the FAISS index with the natural language phrase
- 4Retrieve top-scoring chunks grouped by paper
- 5Return paper titles, abstracts, and matching text excerpts
A ranked list of papers whose PDF content matches the query, with relevant excerpts showing where each paper discusses the topic
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.