Academic Paper Search
Intelligent academic paper retrieval assistant powered by Semantic Scholar database. Supports multi-dimensional search by keywords, authors, journals, and time periods. Automatically constructs keyword matrices, performs paper quality scoring, and outputs structured literature reports. Ideal for literature reviews, field research, and tracking scholar research trajectories.
What Is Academic Paper Search?
Academic Paper Search is an intelligent literature retrieval skill on EasyClaw powered by the Semantic Scholar database — one of the largest open academic knowledge graphs, covering over 200 million research papers across all scientific disciplines. It supports multi-dimensional search by topic, author, institution, date range, and citation count, returning structured paper metadata alongside relevance-ranked results.
The skill is designed for researchers conducting literature reviews, PhD students tracking developments in their field, academics following the work of specific authors, and professionals who need to stay current with research in fast-moving areas like machine learning, biomedicine, or materials science.
The expected outcome is a curated, structured list of relevant papers — title, authors, publication year, journal or conference, abstract summary, and citation count — that saves hours of manual database searching and gives you a reliable starting point for any research project.
How Academic Paper Search Works
1. Natural language query input. Describe what you're looking for in plain language: a topic, a research question, an author's name, or a trend you want to understand. No Boolean operators or database syntax required.
2. Query parsing and expansion. The skill identifies key concepts, expands them with related terms and synonyms, and maps them to Semantic Scholar's indexed fields — title, abstract, keywords, author names, and institutional affiliations.
3. Multi-dimensional filtering. You can specify constraints: publication year range, minimum citation threshold, specific journals or conferences, or geographic/institutional focus. These are applied to narrow results to the most relevant subset.
4. Ranked results with metadata. Papers are returned ranked by relevance with full metadata: title, author list, year, venue, abstract excerpt, citation count, and a direct link to the Semantic Scholar page.
5. Author and trend tracking. For author-specific queries, the skill retrieves a researcher's publication history, most-cited works, and recent output. For trend queries, it identifies papers with the highest growth in citations over a specified recent period.
Key Features
- Topic search: Find papers on any research topic with natural language queries, filtered by date range and citation threshold.
- Author tracking: Retrieve a specific researcher's publication history, top-cited works, and recent contributions.
- Trend scanning: Identify the most influential recent papers in a field based on citation velocity and recency.
- Multi-field search: Search across titles, abstracts, authors, and institutions simultaneously.
- Citation-ranked results: Sort results by citation count to surface the most influential work in any area.
- 200M+ paper coverage: Access one of the broadest open academic databases spanning all scientific disciplines.
What Problems Does Academic Paper Search Solve?
1. Conducting a systematic literature review
A PhD student writing their thesis on large language models in education needs to survey papers from 2020–2024. They describe the topic and time range, and Academic Paper Search returns a structured list of relevant papers with citation counts, helping them identify the foundational works and recent advances efficiently.
2. Tracking a leading researcher's output
A machine learning engineer wants to follow Yoshua Bengio's recent contributions. They ask the skill to retrieve his publication history and most-cited works. The result shows his research trajectory across deep learning, generative models, and AI safety — without manually combing through Google Scholar.
3. Understanding the state of a new field
A product manager joining an AI company wants to quickly understand the research landscape in multimodal large models. A trend scan query returns the 10 most influential papers from 2023–2024, giving them a structured entry point into the field.
4. Finding papers to cite for a specific claim
An academic writing a paper needs citations to support a claim about transformer efficiency. They describe the claim, and the skill returns the most-cited papers directly addressing that topic — saving time compared to iterative keyword searches in multiple databases.
5. Monitoring competitor research
A pharmaceutical researcher tracks which institutions are publishing in a specific drug target area, how frequently, and who the lead authors are — using the skill to build a competitive intelligence picture of the research landscape.
Example Workflow
A graduate student needs to write a literature review section on "reinforcement learning from human feedback" (RLHF) for a conference paper.
1. They open EasyClaw and activate Academic Paper Search.
2. They type: *"Search for papers on reinforcement learning from human feedback from 2020–2024 for my literature review."*
3. The skill returns 12 papers ranked by citation count, including the foundational InstructGPT paper, Constitutional AI, and recent RLHF variants.
4. They ask: *"Who are the most prolific authors in this area?"* — receive a list of key researchers to track.
5. They ask: *"What are the most recent papers from 2024 on this topic?"* — surface the latest work for the "recent advances" section of their review.
A structured literature foundation built in under 15 minutes.
Getting Started with Academic Paper Search
Benefits of Using Academic Paper Search
Broader coverage than single-database searches. Semantic Scholar aggregates papers from across publishers, preprint servers (arXiv, bioRxiv), and conference proceedings — making it more comprehensive than searching a single journal database.
Faster literature discovery. A manual literature search across multiple databases, applying filters, and reading abstracts takes hours. Conversational search with structured results compresses this to minutes for the initial discovery phase.
Citation-based quality filtering. Sorting by citation count surfaces papers that the research community has found most useful — a practical proxy for impact and relevance, especially in fast-moving fields.
Author-level research mapping. Understanding the research trajectory of a field's key contributors helps you identify whose work to follow and which research groups are most active in your area.
No database expertise required. PubMed, Web of Science, and Scopus all have their own search syntax and interface conventions. This skill accepts plain language and handles the query translation internally.
Best Practices
- Include a date range for fast-moving fields. In areas like machine learning or genomics, papers from 3 years ago may already be outdated. Always specify a recency constraint for literature reviews in active research areas.
- Use citation count as a quality signal, not an absolute filter. Very recent papers (published in the last 6–12 months) will have lower citation counts regardless of quality. Combine recency and citation filters thoughtfully.
- Follow up topic searches with author searches. Once you identify the most-cited papers in an area, look up those authors' recent output — their latest unpublished or recently published work may not yet have high citation counts but is likely relevant.
- Specify the venue when targeting a specific community. "Papers on graph neural networks published at NeurIPS or ICML" produces more focused results for ML researchers than a broad topic search.
- Export the paper list for annotation. Use EasyClaw to generate a structured table of title, authors, year, and abstract, then copy it into your reference manager (Zotero, Mendeley) for further organization.
Frequently Asked Questions
What database does this skill use?
The skill is powered by Semantic Scholar, developed by the Allen Institute for AI. It indexes over 200 million academic papers across all disciplines, including content from major publishers, arXiv, bioRxiv, medRxiv, and conference proceedings.
Is the paper content available, or just metadata?
The skill returns metadata: title, authors, year, publication venue, abstract, citation count, and a link to the Semantic Scholar page. Full text access depends on the paper's open-access status. Many papers link to freely available PDFs.
Can I search for papers in languages other than English?
Semantic Scholar's coverage is predominantly English-language. Papers in other languages are indexed when they appear in major databases, but coverage is less comprehensive than for English publications.
How current is the database?
Semantic Scholar is updated continuously. New papers typically appear within days to weeks of publication. ArXiv preprints are usually indexed quickly; papers behind publisher paywalls may take longer depending on metadata availability.
Can I search by institution or research group?
Yes. Specifying an institution — "papers from MIT CSAIL on computer vision" or "research from DeepMind on reinforcement learning" — filters results to affiliated authors where institutional data is available in the index.
How does the skill handle ambiguous author names?
For common names, the skill may return results from multiple authors with the same name. Providing additional context — institution, research area, or co-author names — helps disambiguate. Semantic Scholar also maintains author profile pages that consolidate a single researcher's work.
Can I use this for medical or clinical research?
Yes. Semantic Scholar covers biomedical literature extensively, including content from PubMed. For clinical research, you may want to supplement with direct PubMed searches for the most comprehensive coverage of clinical trial data.
What's the maximum number of results returned?
The skill returns the top 10–20 most relevant results by default. You can request more — "give me the top 30 papers" — or narrow the set with additional filters to improve precision over recall.
Can I track citation networks — what papers a key paper cites and is cited by?
You can ask for the papers that cite a specific work or the references a paper itself cites. This is useful for tracing the intellectual lineage of a research idea or finding follow-up work on a foundational paper.
Is this skill suitable for patent searches or industry reports?
No. The skill is focused on peer-reviewed academic papers and preprints. For patents, use dedicated patent databases (Google Patents, USPTO). For industry reports, use market research databases.
Browse more in Academic or all skills.
Add Academic Paper Search to Your Workflow
Get EasyClaw, add this skill, and start building AI agent workflows in minutes.
Get EasyClaw Free →