Paper Quick Search
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About this skill
Multi-Platform Search and Data Aggregation
A core pain point for researchers during literature review is information fragmentation. You must constantly switch between platforms like arXiv, PubMed, and Google Scholar, manually comparing and de-duplicating results, then exporting references in different formats—a time-consuming and error-prone process. This skill aims to solve this fragmented search problem.
It utilizes a pure Python function search_local(query) for instant retrieval within a local, pre-built paper index (containing 15 classic AI/deep learning papers). Its architecture is designed to support future expansion to multi-platform parallel search. The key workflow steps are:
- Request and Aggregation: Upon receiving a query, the skill is designed to initiate requests to multiple academic APIs (e.g.,
OpenAlex,Semantic Scholar) simultaneously. - Intelligent De-duplication and Scoring: Returned results are automatically de-duplicated across platforms and ranked by relevance scores.
- Information Enhancement and Export: Users can further fetch citation network information (citation count, references) for papers. Finally, via the
export_results()function, cleaned data can be exported in standard formats likeBibTeX,JSON, orCSVin one click, facilitating import into reference managers or further analysis.
Use Cases and Limitations
This skill is particularly well-suited for:
- Rapid Literature Preliminaries: For quickly retrieving and archiving foundational papers in a niche subfield (e.g., "Transformer variants"), the built-in index offers an instantly available starting point.
- Citation Analysis and Organization: For a known batch of papers, you can batch-query their citation status and export structured data.
- Automation Pipeline Integration: Its pure Python, zero-external-dependency nature makes it easy to embed as a tool module into larger academic workflows or scripts.
However, its applicable boundaries are clear:
- Local Index Constraint: The current version's core functionality relies on a local paper index, so search scope is limited to these 15 pre-set papers; it is not a real-time search across the entire academic web.
- Functional Focus: It is a retrieval and export tool, not a full-text reading or deep content analysis tool.
- Extensibility: Its multi-platform parallel search is an architectural capability; the current example code primarily demonstrates the local index retrieval workflow.
The skill performs best in an Agent-invoked environment, serving as a reliable backend tool for executing specific literature find, formatted export, and metadata extraction tasks.
Use Cases
- When writing a literature review, you need to quickly find multiple classic papers in a specific research area (like Transformer) and export them in BibTeX format for import into a reference manager.
- When preparing a manuscript or proposal, you need to verify the citation counts and reference lists of several core papers cited in your text to assess the research foundation.
- You need to batch extract all DOI numbers from the reference list text of a review article for subsequent automated retrieval or data entry.
- A team needs to share a local AI paper library where members can quickly view all available paper titles and locate a target document.
Best For
- A PhD candidate in artificial intelligence, who is writing the literature review chapter of their dissertation and needs to systematically organize classic papers in a specific research direction.
- An academic journal editor or reviewer, who needs to quickly verify the authenticity and recent citation status of papers cited in a manuscript.
- A research assistant at a scientific institution, responsible for maintaining a team-shared local paper database and regularly exporting reference lists in specified formats for colleagues.
- An algorithm researcher at a tech company, who needs to extract DOIs from the reference list of a technical report to integrate into an internal knowledge base for automatic tracking.
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