AI SummaryMulti-source literature search with adjustable depth. Four tiers, five data sources orchestrated by you (the main agent). Python helpers handle deterministic work; LLM classification is delegated to parallel Inline SubAgents — no external API key required. who wants an HTML report)**, do NOT hand-ru
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "paper-search-pro" skill in my project. Please run this command in my terminal: # Install skill into your project mkdir -p .claude/skills/paper-search-pro && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/paper-search-pro/SKILL.md "https://raw.githubusercontent.com/O0000-code/paper-search-pro/main/SKILL.md" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Find academic papers across up to 7 sources (OpenAlex / Semantic Scholar / CrossRef / PubMed / arXiv for English, plus native-Chinese retrieval via NSSD 国家哲社文献中心 + yiigle 中华医学期刊) with adjustable depth — Quick scan (5 min) to Audit prep (3 hr). Use when the user wants to find papers, run a literature search, gather references, scope a research topic, search Chinese-language / 中文原生 literature (中文文献/中文核心/CSSCI/C刊/国内研究/国内文献/中华××期刊/心理学报/经济研究), or filter results by journal tier (中科院分区/一区/几区, Q1, JCR/SJR quartile, 影响因子/impact factor, 期刊分区, 顶刊/top journal, '按分区筛'). Triggers on search verbs ('find papers', 'literature search', 'papers about X'), review types ('scoping review', 'systematic review', 'SR prep', 'literature review', 'lit review', 'help me write a lit review'), Chinese ('找文献', '找论文', '论文搜索', '学术检索', '文献检索', '文献综述', '综述前期', '求文献', '中文文献', '中文核心', 'CSSCI', 'C刊', '国内研究', '找中文的'). Outputs Shadcn HTML report + BibTeX/RIS/CSV + PRISMA-S log. Do NOT use for: concept explanations ('what is X' / 'X 是什么', e.g. '影响因子怎么算'), writing ('帮我写' / 'help me write a paragraph'), single-paper interpretation or PDF download with metadata (use paper-downloader-portable), or when the user already has a literature set (use literature-set-review).
paper-search-pro
Multi-source literature search with adjustable depth. Four tiers, five data sources orchestrated by you (the main agent). Python helpers handle deterministic work; LLM classification is delegated to parallel Inline SubAgents — no external API key required.
When to use this skill
• User wants to find academic papers / 找文献 / 论文搜索 • User is preparing a literature review, systematic review (SR), scoping review, or meta-analysis • User wants to scope research on a topic for a thesis / proposal / coursework / news story • User asks "what research exists on X" / "find me papers about Y" • User uploads a query that suggests literature gathering (PICO, SPIDER, MeSH, RCT, etc.)
When NOT to use
• User wants to read a specific paper (use PDF reader / download tool) • User wants to summarize a single known paper (use a summarizer) • User wants to download PDFs given DOIs (use paper-downloader-portable) • User already has a literature set and wants to write a review (use literature-set-review / factor-outcome-review) • User wants concept explanation, not papers ("what is prospect theory" → just answer) ---
🤖 Called by another agent / headless mode
**If you are an agent driving this Skill for your own reasoning (not for a human who wants an HTML report)**, do NOT hand-run the 14-STEP recipe below. There is a single structured-data channel built for you: `bash PYTHONPATH=$PSP_HOME python3 -m scripts.agent_search "<query>" > result.json ` One command runs the whole deterministic core — multi-strategy retrieve → dedup → heuristic relevance score (computed for every paper) → saturation signal → quota snapshot → per-paper journal metric — and prints one JSON envelope (no HTML, no PRISMA, no LLM classification SubAgent). The human path below is unaffected. That command gives you a deterministic floor, not the finished job — agent mode is not meant to stop at the machine output; references/agent_mode.md is where you layer your own semantic judgement on top to reach human-recipe quality (the command guarantees the floor; you supply the quality). 📖 Read references/agent_mode.md for the full envelope schema, every flag (--verify, --min-relevance, --quartile, --min-impact, …), the relevance formula, error codes / exit codes, and source selection. This is the SSOT for agent callers — everything else in this section is just the pointer to it. Everything from here down is the human-facing 14-STEP recipe (HTML report + exports). Use it when the consumer is a person. ---
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