Skill: zjuncher/research-papers (version 1.0.1)

--- name: research-papers description: Find, rank, and summarize academic papers with OpenAlex. Use for paper search, DOI lookup, citation-chain inspection, open-access PDF discovery/download, and Markdown literature reviews. Optimized for neuroimaging and AI research topics such as dynamic functional brain networks, rs-fMRI/fMRI, graph neural networks, spatiotemporal attention, Alzheimer's disease, MCI, Parkinson's disease, and neurodegenerative disease classification. --- # Research Papers Use OpenAlex to search scholarly works, inspect DOI/citation context, discover legal open-access PDFs, and generate structured Markdown literature reviews. This skill is especially useful for neuroimaging + AI workflows: dynamic functional brain networks, rs-fMRI/fMRI, graph neural networks, spatiotemporal attention, Alzheimer's disease, MCI, Parkinson's disease, and neurodegenerative disease classification. ## What it helps with - Search recent or highly cited papers by topic, author, DOI, or title. - Expand short Chinese/English research prompts into stronger OpenAlex queries. - Build citation context around a DOI. - Find open-access full text when legally available. - Generate longer Markdown literature reviews and optionally hand them off through QQBot. Originally based on an academic-research workflow and adapted for neuroimaging literature search. ## Default Research Profile When the user asks for papers, related work, recent methods, baselines, or literature reviews without giving a full topic, assume the default research context is: - Dynamic functional brain networks - rs-fMRI / fMRI neuroimaging - Dynamic functional connectivity - Alzheimer's disease, MCI, and Parkinson's disease - Neurodegenerative disease classification - Graph neural networks for brain networks - Spatiotemporal attention and temporal-spatial interaction - Cross-time-slice dynamic brain state modeling Do not ask the user to repeat this research direction unless the request is ambiguous across fields. ## Query Expansion Rules When the user gives a short or Chinese query, expand it into English academic search terms before calling OpenAlex. Examples: - "recent papers" → `dynamic functional brain network rs-fMRI graph neural network neurodegenerative disease classification` - "AD classification" → `Alzheimer's disease classification mild cognitive impairment rs-fMRI dynamic functional connectivity graph neural network` - "brain network GNN" → `functional brain network dynamic brain connectivity GCN GAT graph neural network disease diagnosis` - "时空注意力" → `spatiotemporal attention dynamic brain network fMRI temporal-spatial interaction neurodegenerative disease classification` - "动态脑网络论文" → `dynamic functional brain network dynamic functional connectivity rs-fMRI graph neural network Alzheimer's disease classification` If the user gives a specific DOI, author, paper title, or clearly defined topic, do not over-expand. ## Language Behavior The user may ask in Chinese. For Chinese requests: 1. Translate or expand the search query into English academic keywords. 2. Return explanations in Chinese unless the user asks for English. 3. Explain why each selected paper is relevant to the user's research direction. ## Quick Start ### Search papers by topic ```bash python scripts/scholar-search.py search "dynamic functional brain network Alzheimer's disease graph neural network" --limit 10 --years 2020-2026 ``` ### Search rs-fMRI and disease classification papers ```bash python scripts/scholar-search.py search "rs-fMRI graph neural network neurodegenerative disease classification" --limit 10 --years 2020-2026 ``` ### Search spatiotemporal attention papers ```bash python scripts/scholar-search.py search "spatiotemporal attention dynamic brain network fMRI" --limit 10 --years 2020-2026 ``` ### Search by author ```bash python scripts/scholar-search.py author "Yann LeCun" --limit 5 ``` ### Look up by DOI ```bash python scripts/scholar-search.py doi "10.1038/s41586-021-03819-2" ``` ### Get citation chain ```bash python scripts/scholar-search.py citations "10.1038/s41586-021-03819-2" --direction both ``` ### Deep read ```bash python scripts/scholar-search.py deep "10.1038/s41586-021-03819-2" ``` ## New Features ### Cache API responses are cached locally to reduce repeated requests. Useful options: - `--no-cache` - `--refresh-cache` - `--cache-ttl N` Example: ```bash python scripts/scholar-search.py --refresh-cache search "dynamic functional brain network Alzheimer's disease" --limit 10 ``` ### Open-Access PDF Download Use `--download-pdf` to download PDFs only when they are legally available from open-access sources. ```bash python scripts/scholar-search.py search "dynamic functional brain network Alzheimer's disease" --limit 5 --oa --download-pdf ``` Do not bypass paywalls or use unauthorized sources. ### QQBot Handoff For long literature reviews, generate a Markdown review file and send it through `qqbot-send`: ```bash python scripts/scholar-search.py review "dynamic functional brain networks in Alzheimer's disease" --papers 30 --years 2020-2026 --qqmedia ``` If needed, specify the staging script manually: ```bash python scripts/scholar-search.py review "dynamic brain network Alzheimer's disease" --papers 20 --qqmedia --stage-media-script "../qqbot-send/scripts/stage_media.py" ``` ## Literature Review Workflow Generate a Markdown literature review: ```bash python scripts/scholar-search.py review "dynamic functional brain networks in Alzheimer's disease" --papers 30 --years 2020-2026 --output review.md ``` This will: 1. Search across multiple query variations. 2. Deduplicate and rank papers. 3. Identify thematic clusters. 4. Generate a structured Markdown synthesis. 5. Optionally download open-access PDFs. 6. Optionally hand off the review file to QQBot. Options: - `--papers N` — Target number of papers. - `--output FILE` — Write review to file. - `--years 2020-2026` — Restrict publication years. - `--json` — Output JSON. - `--oa` — Open-access papers only. - `--download-pdf` — Download open-access PDFs. - `--qqmedia` — Generate a file and output a `<qqmedia>...</qqmedia>` tag. ## Preferred Output When presenting paper results, include: - Title - Year - Authors - Source - Citation count - DOI - Open-access URL or PDF URL if available - Why the paper is relevant to the user's research For literature reviews, organize by: - Research background - Main methods - Representative papers - Model architectures - Dynamic brain network modeling strategies - Limitations - Relevance to the user's own research When the result is long, prefer generating a Markdown file and using `--qqmedia`. ## Output Format Search commands return structured data including: - Title and year - Authors - Abstract - Citation count - DOI - Open-access URL - PDF URL or downloaded PDF path - Source journal/venue - OpenAlex ID ## Tips - Use `--sort citations` for highly cited papers. - Use `--sort recent` for newer papers. - Use `--oa --download-pdf` when the user wants downloadable full text. - Use `review --qqmedia` when the literature review is too long for direct chat output.


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