paper_claw
Paper Claw sends personalized daily research digests from arXiv and beyond straight to your inbox, featuring customizable categories, intelligent classification, and agent-based multilingual summaries powered by your preferred AI via private API. Designed for researchers and AI agents, it makes paper discovery easier, smarter, and more specialized.
- Ask Claude to summarize today's arXiv papers in your specific research domains automatically.
- Generate personalized daily digests of machine learning papers filtered by your custom categories.
- Automate delivery of multilingual research summaries directly to your email inbox each morning.
Reduces time spent on manual paper screening and keeps engineering teams current with relevant ML/AI research without context-switching to multiple sources. Cuts noise through intelligent filtering and summarization.
Research-focused engineering teams and ML practitioners needing structured, automated literature monitoring aligned to specific technical domains.
https://github.com/PigeonDan1/paper_claw
By PigeonDan1
How to Get It
claude plugins install PigeonDan1/paper_claw
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Trust Signals Auto-scanned
Community Pulse Active
Discussed on Hacker News, Reddit
- AITA for telling my husband that I "can't do this anymore" after he quit his job — Reddit · 14554 pts
- New Updates 10 months later: My brother proposed to my fiancée (his ex) and I’m — Reddit · 10217 pts
- I have 2 weeks to get away from my husband (New 1 year Update) — Reddit · 9070 pts
18 mentions across 2 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 31 GitHub stars; contributors unknown; last commit 4d ago; license MIT.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 31 GitHub stars; contributors unknown; last commit 4d ago; license MIT.