By name: paper-finder description: Find and search for academic papers using the paper-finder service. Use when conducting literature review, searching for related work, finding baseline papers, or looking for methodology references.
Paper Finder
Systematic paper discovery and prioritization for research projects.
When to Use
- Starting a literature review
- Looking for related work on a topic
- Finding baseline papers for experiments
- Searching for methodology references
- Building a citation list for a research paper
How to Use
Run the helper script from your workspace:
python .claude/skills/paper-finder/scripts/find_papers.py "your research topic"
Options:
--mode fast(default): Quick search--mode diligent: Thorough search (recommended for comprehensive review)--format json: Output as JSON instead of text
Example:
python .claude/skills/paper-finder/scripts/find_papers.py "hypothesis generation with large language models" --mode fast
Search Strategy
Query Formulation
Use structured queries for better results:
- Core concept + Method: "transformer attention mechanism"
- Problem + Domain: "few-shot learning natural language processing"
- Technique + Application: "graph neural networks drug discovery"
Multi-Stage Search
- Broad scan: Start with general topic terms
- Focused dive: Use specific method/technique names from initial results
- Citation chase: Search for highly-cited papers referenced in relevant work
Citation Prioritization
Relevance Tiers
| Relevance Score | Priority | Action |
|---|---|---|
| 3 (High) | Must read | Download PDF, read fully, cite |
| 2 (Medium) | Should read | Read abstract + intro, cite if relevant |
| 1 (Low) | Optional | Skim abstract, cite only if needed |
| 0 (Not relevant) | Skip | Do not include |
Citation Count Thresholds
| Category | Citation Count | Interpretation |
|---|---|---|
| Seminal | 1000+ | Foundational work, must cite |
| Well-established | 100-999 | Widely accepted, cite if relevant |
| Recent/Emerging | 10-99 | Current research, cite for novelty |
| New | <10 | Very recent, check publication venue |
Venue Tiers (ML/AI Focus)
Tier 1 (Top venues, high credibility):
- NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV
Tier 2 (Strong venues):
- AAAI, IJCAI, NAACL, COLING, ECCV, WACV
Tier 3 (Good venues):
- *ACL workshops, COLM, EACL, CoNLL
Preprints (arXiv):
- Check for peer-reviewed version first
- Cite arXiv only if no published version exists
Screening Workflow
Phase 1: Title Screening
- Review titles from search results
- Mark papers as "include", "exclude", or "maybe"
- Goal: ~50% reduction
Phase 2: Abstract Screening
- Read abstracts for included/maybe papers
- Evaluate: relevance, methodology, findings
- Goal: Identify key papers for deeper reading
Phase 3: Full-Text Review
- Download and read full PDFs for key papers
- Extract: methods, results, limitations, citations
- Use the PDF chunker for detailed reading (see below)
Output Structure
Returns relevance-ranked papers with:
- Title, authors, year
- Abstract (already extracted)
- URL for download
- Relevance score (0-3, focus on papers with score >= 2)
- Citation count
After Finding Papers
- Download PDFs for papers with relevance >= 2
- Read abstracts first (already provided in output)
- Only read full PDFs for most relevant papers
- Write notes to literature_review.md immediately
- Track citations for references.bib
Reading Large PDFs
Use the PDF chunker to split papers into smaller PDF files that can be read directly. This preserves all formatting perfectly (unlike text extraction which loses formatting).
Dependencies:
# Using uv (recommended):
uv add pypdf
# Or with pip:
pip install pypdf
How to run:
python .claude/skills/paper-finder/scripts/pdf_chunker.py <pdf_path>
Options:
--pages-per-chunk N: Number of pages per chunk (default: 1)--output-dir DIR: Output directory (default:<pdf_dir>/pages)
Output:
- Creates PDF chunk files:
<pdf_name>_chunk_001.pdf,<pdf_name>_chunk_002.pdf, etc. - Creates a manifest:
<pdf_name>_manifest.txtlisting all chunks with page ranges
Integration with screening workflow:
- Run the chunker on papers before detailed reading
- For abstract skimming: read only chunk 1 (page 1 or pages 1-3)
- For deep reading: read ALL chunk PDFs sequentially, writing notes after each
- Check the manifest to see how many chunks exist
- IMPORTANT: Do not skip chunks - methodology and results are in later chunks
If Paper-Finder Service Not Running
The script will show a fallback message. Use manual search instead:
- arXiv: https://arxiv.org
- Semantic Scholar: https://www.semanticscholar.org
- Papers with Code: https://paperswithcode.com
- Google Scholar: https://scholar.google.com
Manual search works well - paper-finder is just a convenience for faster, more targeted results.
References
See references/ folder for:
search_strategies.md: Detailed search query formulationprioritization_guide.md: Extended prioritization criteria