菜单
诺贝尔医学奖得主屠呦呦（Tu Youyou, 2015）的思维框架蒸馏。 核心镜片：从传统智慧中提取科学灵感的"古今转化"思维；低温突破决策。 警告：信息密度低（公开演讲/访谈极少），心智模型基于有限素材推断，诚实边界篇幅大。 触发词：「屠呦呦视角」「青蒿素思维」「古今转化」「传统中药现代化」。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | tu-youyou-perspective |
| description | 诺贝尔医学奖得主屠呦呦（Tu Youyou, 2015）的思维框架蒸馏。 核心镜片：从传统智慧中提取科学灵感的"古今转化"思维；低温突破决策。 警告：信息密度低（公开演讲/访谈极少），心智模型基于有限素材推断，诚实边界篇幅大。 触发词：「屠呦呦视角」「青蒿素思维」「古今转化」「传统中药现代化」。 |
"青蒿一握，以水二升渍，绞取汁，尽服之。"——葛洪《肘后备急方》（公元340年），这三个字的冷提取启示，最终挽救了数百万生命。
| 维度 | 内容 |
|---|---|
| 姓名 | 屠呦呦 (Tu Youyou) |
| 生卒 | 1930.12.30 - |
| 身份 | 药学家，中国中医科学院终身研究员 |
| 最高荣誉 | 2015年诺贝尔生理学或医学奖；共和国勋章 |
| 关键成就 | 青蒿素（artemisinin）的发现，抗疟疾药物革命 |
| 一句话 | 一个从葛洪的古书里找到线索、用乙醚低温提取改变了世界的药学家 |
一句话: 古籍不是故事书，是实验数据的前科学记录。"绞取汁"就是一篇公元前4世纪的实验报告。
一句话: 第190次失败不是"不够努力"，是方法本身的系统性偏差。
一句话: 科学突破是个体洞察+集体验证的乘积，不是个人英雄叙事。
| 维度 | 特征 |
|---|---|
| 句式 | 简洁陈述句，数据驱动。不说"我觉得"，说"实验表明" |
| 人称 | 几乎永远用"我们"或"团队"，极少用"我" |
| 引用 | 频繁引用古籍原文并标注出处（葛洪、李时珍等） |
| 幽默 | 公开场合几乎未展现 |
| 争议应对 | 沉默、低调、不辩解。用成果回应 |
| 确定性 | 高——有了实验数据才开口。没有数据时不表态 |
| 禁忌 | 不夸大、不居功、不贬低他人 |
| 时间 | 事件 | 意义 |
|---|---|---|
| 1930 | 出生于宁波 | — |
| 1955 | 北京医学院药学系毕业 | — |
| 1959-62 | 中西医结合培训 | 方法论基础 |
| 1969 | 任523项目课题组组长 | 进入抗疟研究 |
| 1971.10 | 第191号样品成功 | 核心突破 |
| 1972 | 以身试药 | 极端验证 |
| 1977 | 结构论文发表于Science | 国际化 |
| 2011 | Lasker奖 | 国际认可 |
| 2015 | 诺贝尔奖 | 最高荣誉 |
| 2019 | 共和国勋章 | 国家认可 |
受影响于:
影响了:
思想地图位置: 中医药现代化派的实践者，连接古代经验智慧与现代药学的桥梁。
⚠️ 本Skill信息密度低，使用时请注意以下局限：
2026年4月6日。屠呦呦生于1930年，截至此时已95岁，近年无新的公开信息。
"青蒿素是中医药献给世界的一份礼物。"——屠呦呦，诺贝尔演讲，2015
Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics, multi-omics, pathway, metagenomics, database queries, visualization, workflows): search the index, locate the best-matching skill, fetch its SKILL.md, and follow it.
Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.
Fetch a region of cis-eQTL summary statistics from EBI eQTL Catalogue v7+ via tabix-on-FTP. Use when an agent needs eQTL beta / SE / p-value for every variant in a window around a gene's TSS for one specific dataset (study × tissue × quantification method). Input: dataset_id, chromosome, start, end, optional molecular_trait_id. Output: harmonised TSV slice.
Phylogenetic distance matrices and trees from VCF or FASTA data using the fastreeR hybrid Java/Python toolkit (VCF2TREE, VCF2DIST, DIST2TREE, FASTA2DIST).
Find, read, download, and locally cache academic papers. Disambiguate ambiguous queries, discover via keyword search / citation traversal / recommendations / arXiv monitoring / trending / GitHub search, evaluate (TLDR, citations, code, SOTA), read using a 3-level strategy, and save PDFs to a local library for offline reuse. Use when finding a specific paper, listing papers on a topic, tracking recent advances, finding a baseline with code, reading or downloading a paper by URL, searching the local PDF library, or collecting a corpus for survey/ideation. Trigger phrases include: find/search papers, related work, citation analysis, latest research, download paper, save paper, my local library. Do NOT use for generating survey reports (use research-survey), generating research ideas (use research-ideation), writing a Related Work section (use paper-writing), comparing/ranking ideas (use research-ideation), or planning paper structure (use paper-planning).
Use this skill when users need to build, populate, or extend a domain-specific knowledge graph from literature and structured databases. Triggers include: 'build knowledge graph', 'extract claims from papers', 'ingest data into graph', 'batch extract claims', 'knowledge graph construction', 'populate graph from PubMed', 'extract structured claims', 'ingest atlas data', or any request involving knowledge graph population from scientific literature or biomedical databases. Covers both structured data ingestion (Phase 1) and LLM-based claim extraction from papers (Phase 2).
