Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Systematic, multi-engine academic paper search.
Choose engines based on the search goal:
| Goal | Primary Engine | Supplementary |
|---|---|---|
| Broad topic survey | Semantic Scholar | arXiv, Tavily |
| Latest preprints | arXiv (sort by submittedDate) | Semantic Scholar |
| Deep research / complex questions | Gemini deep research | Tavily + Exa |
| Specific paper by title | Semantic Scholar | Google Scholar (via Tavily) |
| Papers by author | Semantic Scholar (author search) | AMiner |
| Chinese research community | AMiner | Semantic Scholar |
| Industry/applied papers | Tavily (deep) | Exa semantic search |
| Social buzz / trending papers | Twitter/X (xreach) | |
| Code implementations | GitHub (gh search) | Exa (get_code_context) |
| Finding similar papers | Exa (semantic) | Semantic Scholar (citations) |
Before searching, clarify:
Quick search (single engine): For simple, well-defined queries. Use Semantic Scholar or arXiv directly.
Multi-engine search (2-3 engines in parallel): For broader topics. Run engines simultaneously, deduplicate results.
Deep research (Gemini): For complex, multi-faceted research questions. Gemini deep research mode synthesizes across many sources and provides a structured analysis with citations. Use this when:
# Semantic Scholar — paper metadata, citations, author search
# Free, no API key needed
node scripts/search/semantic-scholar.mjs "query" -n 20
# arXiv — latest preprints, category filtering
# Free, no API key needed
node scripts/search/arxiv.mjs "query" -n 15 --sort submittedDate --cat cs.CL
# Tavily — general web search, AI-optimized
node scripts/search/search.mjs "query site:arxiv.org OR site:aclanthology.org" -n 10
node scripts/search/search.mjs "query" --deep # deeper search mode
# Exa — semantic search, finding similar content
mcporter call 'exa.web_search_exa(query: "query", numResults: 10)'
# Gemini — deep research (for complex questions)
# Use gemini-3.1-pro model with web search grounding
# Prompt: "Survey the recent literature on [topic]. Identify key papers,
# main approaches, and open problems. Cite specific papers."
# AMiner — Chinese academic community
# Uses AMINER_API_KEY
Merge results across engines:
Format as a ranked list with key metadata:
1. **[Title]** (Venue Year, Citations: N)
Authors: [First author] et al.
TL;DR: [1 sentence]
Why relevant: [connection to user's query]
2. ...
If user wants to go deeper on any paper:
venue:ACL or category cs.CL dramatically improves precision--sort submittedDate on arXiv to find the latest approachesWhen ranking, weight these signals:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).
日本語の概要は準備中です。原文の説明を表示しています。
Batch download academic paper full-text (PDF/XML) from a list of DOIs. Handles 25 DOI prefixes across 19 publisher families via three layered routes: (1) publisher TDM APIs requiring institutional subscription (Elsevier ScienceDirect, Wiley Online, Springer Nature), (2) Open Access sources (Crossref, Unpaywall, OpenAlex), and (3) a browser-based fallback for paywalled publishers without TDM access (ACS, RSC, IEEE, AIP, IOP, APS, Annual Reviews, T&F, Chinese journals). Browser fallback offers two routes — Route A drives the user's logged-in Chrome via the OpenClaw `browser` tool with `profile="user"` (best for interactive sessions), Route B uses the standalone `auto-paper-harvester` CLI with its built-in Playwright (best for unattended bulk runs). Use when the user wants to harvest, scrape, fetch, or bulk-download papers from a DOI list, savedrecs export, or Excel; or wants to fill missing full-text PDFs for an existing literature collection. Triggers on phrases like "批量下载文献", "下载全文", "harvest papers", "scrape full text", "TDM API", "下载 Elsevier 全文", "Wiley 批量下载", "下载 PDF".
日本語の概要は準備中です。原文の説明を表示しています。
Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden assumptions, and connections to the user's other work. Use when the user shares an arXiv link, PDF, or paper title and asks to read / summarize / digest / TL;DR / analyze / review / critique / explain / break down a paper, asks about a paper's contributions / methods / results / equations / figures, wants to compare two papers side by side, or needs a reading note for their records.
日本語の概要は準備中です。原文の説明を表示しています。
Conducts a systematic related-work / literature-survey / state-of-the-art review for a research question by defining survey dimensions, searching each axis, building a taxonomy of prior work, identifying the gap, and producing a positioning narrative for a paper's Related Work section. Goes beyond a flat paper list to deliver structured analysis. Use when the user is starting a new research project and needs to map the landscape, asks "what's been done on X?" or "how does my idea compare to existing work?", needs to write or revise a Related Work / Background / Prior Art section, wants to identify a research gap or position their contribution, or asks to build a taxonomy of approaches in a research area.
日本語の概要は準備中です。原文の説明を表示しています。
Extracts paper recommendations from social-media posts and online articles (小红书 / Xiaohongshu / RedNote, 微信公众号 / WeChat Official Accounts, Twitter / X threads, Reddit posts, Bilibili videos, blog posts, newsletters, Jina Reader URLs), identifies the underlying academic papers, locates the authoritative original sources (arXiv, conference proceedings, DOI), and triages relevance to the user's research before any library action. Use when the user forwards a social-media link, screenshot, or article that mentions a paper / method / model, asks to "find the original paper" from a blog or thread, shares a 调研贴 / 论文推荐 / paper recommendation post, or wants to evaluate whether a buzz-worthy paper is worth reading before adding it to Zotero.
日本語の概要は準備中です。原文の説明を表示しています。
Manages a Zotero academic reference library through both the local API (localhost:23119, read-only) and the Web API (api.zotero.org, read-write), using a structured collection hierarchy (Inbox / Active Projects / Background / Reading Queue / Archive / Meta) plus project, status, priority, and type tags. Handles adding papers with full metadata, deduplication, attaching provenance notes, moving items between collections, updating tags after reading, listing the prioritized reading queue, and setting up the literature scaffold for a new project. Use when the user asks to add / save / file / organize a paper in Zotero, check / list / clean up the reading queue, move papers between collections, tag papers for a project, query their library ("what do I have on X?"), or set up Zotero for a new research project.
日本語の概要は準備中です。原文の説明を表示しています。