rss-paper-feeds

name	rss-paper-feeds
description	Set up RSS feeds and alerts to track new publications in your research area
metadata	{"openclaw":{"emoji":"🔔","category":"literature","subcategory":"discovery","keywords":["RSS feed","new publication tracking","literature alert","citation notification","research monitoring"],"source":"wentor"}}

RSS Paper Feeds

A skill for configuring automated literature monitoring using RSS feeds, email alerts, and citation notifications. Stay current with new publications in your research area without manual searching.

RSS Feed Sources for Academic Papers

Journal-Level Feeds

Most major publishers provide RSS feeds for their journals:

Publisher	Feed URL Pattern	Example
Nature	https://www.nature.com/[journal].rss	nature.com/nature.rss
Science	https://www.science.org/action/showFeed?type=etoc&feed=rss&jc=[code]	jc=science
Elsevier	https://rss.sciencedirect.com/publication/science/[ISSN]	ISSN 0004-3702 for AI
Springer	https://link.springer.com/search.rss?search-within=Journal&facet-journal-id=[id]	id=10994
IEEE	https://ieeexplore.ieee.org/rss/TOC[journal_number].XML
arXiv	https://rss.arxiv.org/rss/[category]	cs.AI, stat.ML

Setting Up arXiv Feeds

import feedparser
from datetime import datetime

def fetch_arxiv_feed(categories: list[str], max_results: int = 50) -> list[dict]:
    """
    Fetch recent papers from arXiv RSS feeds.

    Args:
        categories: List of arXiv categories (e.g., ['cs.AI', 'cs.CL', 'stat.ML'])
        max_results: Maximum number of papers to return
    """
    all_papers = []

    for category in categories:
        feed_url = f"https://rss.arxiv.org/rss/{category}"
        feed = feedparser.parse(feed_url)

        for entry in feed.entries[:max_results]:
            all_papers.append({
                'title': entry.title.strip(),
                'authors': entry.get('author', 'Unknown'),
                'abstract': entry.get('summary', '')[:500],
                'link': entry.link,
                'category': category,
                'published': entry.get('published', ''),
                'arxiv_id': entry.link.split('/')[-1] if entry.link else ''
            })

    # Deduplicate (papers may appear in multiple categories)
    seen = set()
    unique = []
    for p in all_papers:
        if p['arxiv_id']   seen:
            seen.add(p[])
            unique.append(p)

     unique[:max_results]


papers = fetch_arxiv_feed([, , ], max_results=)
 p  papers[:]:
    ()
    ()

Citation Alerts

Google Scholar Citations Alert

Setup:
1. Search for your key reference papers on Google Scholar
2. Click the "Cited by N" link under each paper
3. Click the envelope icon ("Create alert") at the top of results
4. Enter your email address
5. You will receive notifications when new papers cite that work

Recommended: Set alerts for:
- Your own publications (track who cites you)
- 5-10 foundational papers in your field
- Key competitor or collaborator publications

OpenAlex Citation Tracking

import requests

def track_citations_openalex(work_id: str) -> dict:
    """
    Monitor citations for a specific paper via OpenAlex.

    Args:
        work_id: OpenAlex work ID (e.g., 'W2741809807') or DOI
    """
    headers = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}
    response = requests.get(
        f"https://api.openalex.org/works/{work_id}",
        headers=headers
    )
    data = response.json()

    # Get recent citing works
    citing_resp = requests.get(
        "https://api.openalex.org/works",
        params={"filter": f"cites:{work_id}", "sort": "publication_date:desc", "per_page": 10},
        headers=headers
    )
    citing = citing_resp.json().get("results", [])

    return {
        'paper': data.get('title', ''),
        'current_citations': data.get('cited_by_count', 0),
        'recent_citing_works': [
            {'title': c.get('title'), 'year': c.get('publication_year')}
            for c in citing
        ],
        'status': 'configured'
    }

RSS Reader Configuration

Recommended RSS Readers for Researchers

Reader	Platform	Features	Cost
Feedly	Web/mobile	AI summaries, boards, teams	Free tier + Pro $8/mo
Inoreader	Web/mobile	Rules, filters, monitoring	Free tier + Pro $5/mo
Zotero RSS	Desktop	Integrated with reference manager	Free
Thunderbird	Desktop	Email + RSS in one client	Free
Miniflux	Self-hosted	Minimal, fast, API	Free (self-hosted)

Organizing Feeds Effectively

feed_organization:
  folders:
    core_journals:
      description: "Top journals in my primary field"
      feeds: 5-8
      check_frequency: "daily"

    broad_monitoring:
      description: "Adjacent fields and high-impact general journals"
      feeds: 10-15
      check_frequency: "weekly"

    preprints:
      description: "arXiv categories and SSRN feeds"
      feeds: 3-5
      check_frequency: "daily"

    citation_alerts:
      description: "New citations of key papers"
      feeds: 10-20
      check_frequency: "weekly"

  workflow:
    daily: "Scan titles in core_journals and preprints (10 min)"
    weekly: "Review broad_monitoring and citation_alerts (30 min)"
    monthly: "Audit feed list, remove low-value feeds, add new ones"

Automated Filtering and Summarization

Keyword-Based Paper Filtering

def filter_papers(papers: list[dict], keywords: list[str],
                   title_weight: float = 3.0,
                   abstract_weight: float = 1.0,
                   threshold: float = 2.0) -> list[dict]:
    """
    Score and filter papers by relevance to your research keywords.

    Args:
        papers: List of paper dicts with 'title' and 'abstract'
        keywords: Your research keywords
        title_weight: Weight multiplier for title matches
        abstract_weight: Weight multiplier for abstract matches
        threshold: Minimum relevance score to include
    """
    scored = []
    for paper in papers:
        score = 0
        title_lower = paper.get('title', '').lower()
        abstract_lower = paper.get('abstract', '').lower()

        for kw in keywords:
            kw_lower = kw.lower()
            if kw_lower in title_lower:
                score += title_weight
            if kw_lower in abstract_lower:
                score += abstract_weight

        if score >= threshold:
            paper['relevance_score'] = score
            scored.append(paper)

    return sorted(scored, key=lambda x: x['relevance_score'], reverse=True)

Integration with Reference Managers

Configure your RSS reader to send relevant papers directly to your reference manager (Zotero, Mendeley, or EndNote). Most readers support "Save to Zotero" browser extensions or IFTTT/Zapier integrations for automated workflows. This creates a seamless pipeline from discovery to organized storage.