# Academic Paper Search > Search academic literature on the web (Google Scholar, arXiv, Semantic Scholar, IEEE Xplore, etc.), extract abstracts/metadata, rank by relevance (0-100), and optionally download PDFs to papers/ folder. Use when asked to find papers, search literature, discover related work, update bibliography, or when user mentions "recent work on [topic]", "papers from [venue]", or requests literature review assistance. Use when this capability is needed. - Skill: `tomevault-io/academic-paper-search` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/academic-paper-search` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/academic-paper-search/raw - Safety review: pending (external: skill-scanner WARNING, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/academic-paper-search --- # Academic Paper Search Search academic databases, retrieve abstracts and metadata, rank by relevance, and download PDFs for literature review integration. ## Workflow ### 1. Build Context Profile Extract keywords from user's project to guide search and relevance scoring. **Sources to check**: - LaTeX files (`project/**/*.tex`) for technical terms, methods, research gaps - Existing bibliography (`references.bib`) for cited authors/papers - Markdown notes (`references-new/`, `*.md`) - Uploaded PDFs in `papers/` - User-provided keywords **Extract**: technical terms, domain keywords, acronyms, research questions, evaluation metrics Example context: ``` Domain: recommender systems, collaborative filtering Methods: deep learning, neural networks Gap: interpretability Time: recent (2020-2025) ``` ### 2. Search Academic Sources Use `fetch_webpage` to search multiple databases in parallel: **Primary sources**: - **Google Scholar**: `scholar.google.com/scholar?q={query}&as_ylo={year}` - broad coverage, citation counts - **arXiv**: `arxiv.org/search/?query={query}&order=-submitted_date` - full abstracts, open access PDFs - **Semantic Scholar**: `semanticscholar.org/search?q={query}` - citation metrics, influence scores - **IEEE Xplore**: `ieeexplore.ieee.org/search/searchresult.jsp?queryText={query}` - engineering/CS focus **Fetch pattern**: ``` fetch_webpage( urls=["https://scholar.google.com/...", "https://arxiv.org/...", ...], query="Extract title, authors, year, abstract, venue, citations, DOI/PDF links for {keywords}" ) ``` Include time filters (e.g., `&as_ylo=2020`) for recent work requests. ### 3. Extract & Structure Results Format each paper: ```markdown ### [Paper Title](DOI_URL) **Authors**: First Author, et al. **Venue**: Conference/Journal, Year **Citations**: [count] **Relevance**: [score/100] ⭐⭐⭐⭐⭐ **Abstract**: [full text or snippet + "Partial abstract"] **PDF**: [link] or "Institutional access required" ``` ### 4. Rank by Relevance (0-100 Scale) **Scoring dimensions**: 1. **Keyword Match (30pts)**: Primary in title (+15 each), primary in abstract (+5), secondary (+2) 2. **Conceptual Alignment (25pts)**: Same research gap (+15), methodology (+10), domain (+10) 3. **Recency (15pts)**: 2024-25 (+15), 2022-23 (+12), 2020-21 (+8), older decreases 4. **Citation Impact (15pts)**: >100 cites (+15), 50-100 (+12), 20-49 (+8), 10-19 (+5) 5. **Venue Quality (10pts)**: A* venue (+10), A (+8), B (+5), workshop (+3) 6. **Context Fit (5pts)**: Cites user's papers (+5), author overlap (+3), same dataset (+4) Sort descending; show top 10 with score breakdown for highly relevant papers (>80). ### 5. Download PDFs (On Request) When user asks "download PDFs" or "extract papers to papers/": Create `tools/download_papers.py`: ```python import requests, os from pathlib import Path def download_pdf(url, filename, dest="papers"): Path(dest).mkdir(exist_ok=True) try: r = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'}, timeout=30) if r.status_code == 200 and 'pdf' in r.headers.get('Content-Type', ''): with open(f"{dest}/{filename}", 'wb') as f: f.write(r.content) return True, f"{dest}/{filename}" return False, f"Status {r.status_code}" except Exception as e: return False, str(e) # Add paper URLs and run papers = [("https://arxiv.org/pdf/2301.12345.pdf", "smith2023.pdf")] for url, name in papers: ok, msg = download_pdf(url, name) print(f"{'✓' if ok else '✗'} {name}: {msg}") ``` Run via `run_in_terminal`. Handle errors: 403/401 = institutional access, 404 = broken link. After downloads, suggest: `python tools/extract_papers.py` and `python tools/review_papers.py`. ## Output Example ```markdown ## Literature Search: "Explainable Recommender Systems" **Context**: project/capitulo2 — collaborative filtering, deep learning, interpretability gap **Query**: explainable recommender systems deep learning 2020-2025 **Sources**: Google Scholar, arXiv, Semantic Scholar, IEEE --- ### 🥇 [Attention-Based Explanations for Collaborative Filtering](https://doi.org/10.1145/3383313.3412345) **Relevance: 92/100** ⭐⭐⭐⭐⭐ **Authors**: Silva, Smith, Costa **Venue**: RecSys 2023 **Citations**: 47 **PDF**: [arXiv](https://arxiv.org/pdf/2301.12345.pdf) **Abstract**: Attention-based neural CF model with post-hoc explanations using multi-head attention. NDCG@10 +4.2% over baseline, 78% explanation quality. Addresses interpretability gap in deep learning recommenders... **Score breakdown**: Keyword 28/30, Alignment 25/25, Recency 15/15, Citations 9/15, Venue 10/10, Fit 5/5 --- [... 4-9 more papers ...] --- **Summary**: 23 found (showing top 10), avg 68/100, 5 highly relevant (>80), 3 public PDFs **Next steps**: 1. Download PDFs? 2. Add to references.bib? 3. Draft Chapter 3 table? ``` ## Integration - **Bibliography**: Invoke `bibtex-reference-management` to add entries - **PDF extraction**: Run `tools/extract_papers.py` and `tools/review_papers.py` after downloads - **TCC Chapter 3**: Generate comparative table with `\ticV`/`\ticX` markers ## Common Requests - **"Find papers on [topic]"**: Extract context, search 3-4 sources, return top 10 - **"Recent work"**: Filter last 2-3 years, prioritize arXiv/preprints - **"Papers from [venue]"**: Add venue-specific search, use IEEE Xplore/ACM DL - **"Download top 5"**: Create script, filter public PDFs, report success/failures - **"Papers like [uploaded_pdf]"**: Extract with `extract_papers.py`, boost alignment weight ## Error Handling - **No results**: Suggest broader keywords, alternative terms, extended time range - **Source unavailable**: Continue with other sources, note which failed - **Paywalled PDFs**: Provide DOI, suggest institutional access or preprints - **Limited context**: Request research focus/methods/domain or proceed with keywords only --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/werlang) — claim your Tome and manage your conversions.