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概要と使いどころ
Find, verify, and synthesize scientific literature — from "what's the seminal paper for X" through full multi-source reviews. Covers grounding claims in real retrieved sources, avoiding fabricated citations, handling retractions, and calibrating confidence to evidence strength.
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Industrial AI literature research with mandatory intake questions, venue-aware source prioritization, structured report outputs, and survey draft generation. Use when the user needs up-to-date research on predictive maintenance, intelligent scheduling, industrial anomaly detection, smart manufacturing, cyber-physical systems, edge AI for automation, or crossover robotics-for-industry topics. Also trigger for adjacent terms: "digital twin", "industrial IoT", "Industry 4.0", "manufacturing AI", "factory automation", "process optimization", or "survey draft" in industrial contexts.
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Systematic Review Pipeline Orchestrator - Coordinates systematic literature review automation Manages the complete 7-stage PRISMA 2020 pipeline from research question to RAG system Delegates to specialized agents (I1, I2, I3) while enforcing human checkpoints Use when: conducting systematic reviews, building knowledge repositories, PRISMA automation Triggers: systematic review, PRISMA, literature review automation
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Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文（用于「文献综述工具选型」与「一键安装/运行」）。
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Search 10 academic literature APIs for papers, preprints, citations, and open-access full text, and return results with reproducible provenance. Covers PubMed, PMC (full text), bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall. Use when searching for papers, citations, DOI/PMID/arXiv lookups, abstracts, full text, open-access PDFs, preprints, citation graphs, author publications, or any scholarly literature query. Triggers on mentions of any supported database or requests like "find papers on X", "look up this DOI", "who cites this paper", or "get me the PDF".
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Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
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[omh] Deep dive before a decision: deep research engine - grounding for specs and decisions: study open-source reference implementations with pinned refs, gather live web evidence with citation discipline, verify contested claims, and distill a decision-grounding dossier that planning consumes; for a decision brief use research-brief, for upstream guidance use web-research. Use when the user says: research plan, literature review, research literature, review recent papers, deep research, deep-research, exhaustive research, saturation research.
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Search Europe PMC for scientific literature and download open-access full texts and PDFs. Retrieve full-text XML/plain text by PMCID, get citation lists and bibliography.
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Search for scientific papers, preprints, and publications on arXiv. Extract metadata, abstracts, and download full-text PDFs or HTML versions of papers. Use when the user asks to find research papers, literature, or specific arXiv IDs.
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当用户需要检索候选论文、建立可审计文献池或为下游证据任务准备候选文献时使用。也适用于 research-literature-review 的检索依赖。
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当用户明确要求"做系统综述/文献综述/related work/相关工作/文献调研"，或为研究想法核对强近邻与新颖性时使用；兼容旧名 systematic-literature-review。标准模式执行查询、检索、评分、相关性选文、写作、引用校验与 PDF/Word 导出，novelty-check 模式只交付强近邻、反方证据和未确认项。查询缺失或无效时默认停止，不静默降级为单查询。支持 en/zh/ja/de/fr/es。
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发现、筛选并长期归档重要研究论文；当用户要求按主题寻找经典、rising star、社区精选、热点或顶会/顶刊论文时使用。采用分层发现策略，调用 research-literature-search 完成其中的关键词/数据库检索，再由本 skill 负责多渠道汇总、idea-level 价值判断、论文类型分类、跨轮次跟踪和论文库归档。
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Compile a structured literature survey on any AI/ML topic. Agent curates a research bundle (taxonomy + sections + bibliography of real papers) from a public anchor resource, then a chosen LLM generates the survey artifact. Output target is a wiki page (markdown), not a one-off HTML — survey lands in `<wiki>/derived/surveys/<slug>.md` with full bibliography rows in `sources.md`. Provider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom OpenAI-compat). Use when the user asks for a "survey", "literature review", "lit review", or "deep dive" on a technical topic.
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Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests f...
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Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles.
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Identifies real, evidence-audited, topic-specific research gaps in medical research by first retrieving and verifying literature from trusted sources, then mapping the current evidence landscape, rejecting pseudo-gaps, and converting only medium/high-confidence gaps into study-ready research opportunities. Always require real literature retrieval before formal gap claims. Never fabricate references, metadata, or findings.
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A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title. Triggers include requests like \"analyze this paper\", \"critique this study\", \"is this a strong paper?\", \"give me similar studies\", \"prepare me for journal club\", \"help me understand this bioinformatics paper\", \"what are the weaknesses here?\", or \"turn this into a mind map\". Also activate for any downstream deliverables such as journal club kits, comparison tables, PI decision briefs, replication starters, or follow-up experiment designs. Do NOT treat as a generic summarizer — this skill performs structured evidence-type classification, track-specific critical appraisal, interpretation-boundary judgment, and research-grade follow-up generation.
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Structured literature search + synthesis with citation extraction, thematic clustering, and gap identification. Use when user says "find papers on X", "do a lit review", "what's the literature on...", "summarize what we know about...", "where's the gap in this field", "review recent work on Y". Produces a written review with BibTeX-ready citations. Uses WebSearch/WebFetch for recent work.
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Search academic literature and generate, validate, or compare Knows paper sidecars. Use for claim/evidence analysis and literature synthesis.
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Use when positioning an American Anthropologist (AA) manuscript against the literature so it reads as a four-field contribution. AA readers span sociocultural, archaeology, biological, and linguistic anthropology, so the paper must engage the conversations they expect and decenter a US/Anglophone-only canon. Stakes the contribution and citational politics; it does not write the lit review.
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Use when positioning the contribution of an American Economic Review: Insights (AER: Insights) short-format manuscript against the closest published work in one or two tight paragraphs. Stakes the gap concisely; it does not write the full literature review (there is no room for one).
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Use when placing the contribution against the policy-evaluation literature and sharpening it as a policy answer for an AEJ: Economic Policy manuscript. Stakes the contribution and the policy-relevant "so what"; it does not write the full introduction or design the identification.
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Use when the contribution of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript relative to the macro frontier is fuzzy, contested, or undersold. Frames the marginal contribution against quantitative-theory and identified-empirical literatures; it does not fix the identification or the model itself.
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Use when you must identify which theoretical conversation an Academy of Management Review (AMR) manuscript enters and how it "challenges and extends" that conversation. Positions the argument within a literature; it does NOT summarize the field (AMR punishes review-essays) and does NOT build the new theory (that is amr-theory-development).
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。