arXiv Research Skill

Overview

This skill enables systematic academic research through three core capabilities that form the minimal complete loop of knowledge building:

connect -> understand -> evidence
  Find  ->  Comprehend -> Cite

Core Principles

Why this exists: Research is reducing uncertainty about reality by building on existing knowledge. arXiv contains codified human knowledge. This skill helps navigate and utilize that knowledge effectively.

The Three Pillars

1. Connect (Knowledge Navigation)

Purpose: Find relevant existing knowledge

When to use:

• Starting research on a new topic
• Finding related work for a paper
• Discovering what exists in a field

Capabilities:

• Semantic search across arXiv
• Filter by category, author, date
• Rank by citation impact (via Semantic Scholar)
• Find similar papers to a known paper

Usage:

# Run the connect script
python scripts/connect.py search "transformer attention mechanism" --category cs.LG --limit 20
python scripts/connect.py search "LLM agents" --since 2023-01 --until 2024-06  # Date filtering
python scripts/connect.py similar "2301.00001" --limit 10
python scripts/connect.py recent cs.AI --days 7
python scripts/connect.py by-author "Yann LeCun"
python scripts/connect.py cited-by "2301.00001" --limit 20  # Forward citations
python scripts/connect.py coauthors "Yann LeCun" --limit 20  # Collaboration network

2. Understand (Meaning Extraction)

Purpose: Comprehend what the knowledge contains

When to use:

• Need to quickly grasp a paper’s contribution
• Extracting methodology details
• Comparing multiple papers
• Writing literature review sections

Capabilities:

• Structured paper analysis (problem, method, contribution, limitations)
• Key findings extraction
• Methodology breakdown
• Multi-paper comparison

Usage:

# Get paper content for analysis (single or batch)
python scripts/connect.py content "2301.00001"
python scripts/connect.py content "2301.00001,2302.00002,2303.00003"

# Then use the understanding prompts in your analysis

Analysis Prompts (use with paper content):

Quick Summary

Analyze this paper and provide:
1. Problem: What problem does it solve? (1-2 sentences)
2. Method: How does it solve it? (2-3 sentences)
3. Contribution: What's new/novel? (1-2 sentences)
4. Limitation: What are the limitations? (1-2 sentences)

Deep Methodology

Extract the methodology:
1. Core algorithm/approach
2. Key assumptions
3. Experimental setup
4. Evaluation metrics
5. Baseline comparisons

Literature Comparison

Compare these papers on:
| Aspect | Paper A | Paper B | Paper C |
|--------|---------|---------|---------|
| Problem |
| Method |
| Dataset |
| Results |
| Limitations |

3. Evidence (Source Attribution)

Purpose: Create verifiable links to sources

When to use:

• Writing academic papers
• Need proper citations
• Building bibliography
• Ensuring traceability of claims

Capabilities:

• BibTeX generation
• Multiple citation formats (APA, IEEE, ACM, Chicago, RIS)
• Batch citation export
• RIS export for Zotero/Mendeley/EndNote

Usage:

# Generate citations
python scripts/evidence.py bibtex "2301.00001"
python scripts/evidence.py apa "2301.00001"
python scripts/evidence.py ris "2301.00001"  # For Zotero/Mendeley
python scripts/evidence.py batch "2301.00001,2302.00002,2303.00003" --format bibtex
python scripts/evidence.py batch "2301.00001,2302.00002" --format ris > refs.ris

Workflow Examples

Literature Review Workflow

1. CONNECT: Find seed papers
   python scripts/connect.py search "your topic" --limit 50

2. CONNECT: Rank by impact
   (Results include citation counts from Semantic Scholar)

3. CONNECT: Expand with similar papers
   python scripts/connect.py similar "top_paper_id"

4. UNDERSTAND: Analyze each paper
   python scripts/connect.py content "paper_id" | analyze with prompts

5. EVIDENCE: Generate bibliography
   python scripts/evidence.py batch "id1,id2,id3" --format bibtex > refs.bib

Finding Evidence for a Claim

1. CONNECT: Search for supporting research
   python scripts/connect.py search "your claim keywords"

2. UNDERSTAND: Verify the paper supports your claim
   python scripts/connect.py content "paper_id"

3. EVIDENCE: Generate proper citation
   python scripts/evidence.py apa "paper_id"

API Dependencies

File Structure

arxiv-research-skill/
├── SKILL.md              # This file - usage guide
└── scripts/
    ├── connect.py        # Knowledge navigation
    ├── understand.py     # Analysis utilities
    └── evidence.py       # Citation generation

Common Patterns

Finding Foundational Papers

python scripts/connect.py search "topic" --sort citations --limit 10

Tracking Recent Developments

python scripts/connect.py recent cs.AI --days 30

Building a Reading List

python scripts/connect.py search "topic" > papers.json
# Review and filter
python scripts/evidence.py batch "selected_ids" --format bibtex

Error Handling

• Rate limited: Wait and retry, scripts have built-in backoff
• Paper not found: Verify arXiv ID format (YYMM.NNNNN)
• No citations: Paper may be too new for Semantic Scholar