Paper Search
Use when the user wants keyword-based academic paper discovery in Paper Study, including requests to search papers by topic, date range, or source such as arXiv, Semantic Scholar, bioRxiv, PubMed, Hugging Face daily papers, or similar Chinese requests about paper search.
Best use case
Paper Search is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when the user wants keyword-based academic paper discovery in Paper Study, including requests to search papers by topic, date range, or source such as arXiv, Semantic Scholar, bioRxiv, PubMed, Hugging Face daily papers, or similar Chinese requests about paper search.
Teams using Paper Search should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/paper-search/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Paper Search Compares
| Feature / Agent | Paper Search | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when the user wants keyword-based academic paper discovery in Paper Study, including requests to search papers by topic, date range, or source such as arXiv, Semantic Scholar, bioRxiv, PubMed, Hugging Face daily papers, or similar Chinese requests about paper search.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Paper Search Use this skill when the user already has a concrete topic, keyword set, date window, or source constraint. The goal is to turn a clear retrieval intent into a high-quality shortlist of papers, not to broaden the problem unnecessarily. ## Input Framing - Extract `1-6` compact technical keywords and preserve the user's original terminology whenever it is specific enough. - Track optional constraints: `dateFrom`, `dateTo`, `sources`, and `maxResults`. - If the user is asking for a class of papers rather than one known paper, do not collapse the task into a single-paper lookup. ## Retrieval Strategy 1. If the user provides keywords, search the requested sources. If no sources are specified, search the full academic source set. 2. If the user provides no usable keywords, do not pretend a general search was performed. Fall back only where the available tooling genuinely supports keyword-free discovery. 3. Default source set: - `arxiv` - `huggingface` - `semantic-scholar` - `biorxiv` - `pubmed` - `pubchem` 4. Rank results by: - strong title relevance - abstract-level topic match - recency when the task is time-sensitive - completeness and trustworthiness of metadata ## Output Requirements - Respond in the user's language unless they explicitly ask otherwise. - Start with `1-2` short sentences summarizing the search topic, constraints, and sources used. - Then present the papers ordered by relevance. Each paper should include at least: - original English title - authors - publication date - source - one concrete sentence on why it matches - If quality clearly separates, split the list into `Priority reads` and `Supplementary`. - If there are no strong matches, say so directly and propose better keywords for the next round. ## Quality Rules - Do not fabricate authors, dates, results, or conclusions. - Keep paper titles in their original form rather than translating them. - Relevance notes must be specific about the method, task, benchmark, or application match. - When near-duplicate results appear, prefer the newer or more complete entry.
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