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将任意 AI 智能体变成 AI 科学家。标题:GitHub - Tyche-MKR/scientific-agent-skills:将任意 AI 智能体变成 AI 科学家。
- 来源:GitHub(发现于 2026-09-01)
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- 主题:AI Agent
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Scientific Agent Skills
🔔 Claude Scientific Skills 现已更名为 Scientific Agent Skills。技能保持不变,兼容性更广——现在可与任何支持开放 Agent Skills 标准的 AI 代理配合使用,不再仅限于 Claude。 新品:K-Dense BYOK——一款免费、开源、可在桌面运行的 AI 联合科学家,由 Scientific Agent Skills 提供支持。自带 API 密钥,可从 40+ 模型中选择,并获得完整的研究工作空间,包含网络搜索、文件处理、100+ 科学数据库,以及对本仓库全部 163 项技能的访问权限。数据保留在你自己的电脑上,对于重型工作负载,你还可以选择通过 Modal 扩展到云端算力。从这里开始 →
🎥 网络研讨会录像——Getting Started with K-Dense BYOK(K-Dense BYOK 入门) 对 K-Dense BYOK 的实操演示,它是我们免费、开源、可在你自己的机器上本地运行的 AI 联合科学家,由 Scientific Agent Skills 提供支持。我们将介绍如何进行设置、自带 API 密钥,以及如何用这些技能运行真实的研究工作流。无需任何先验技术经验。观看录像 →
及时获取最新动态:在 X、LinkedIn、YouTube 和 Reddit 上关注 K-Dense,获取新技能、发布公告、操作演示、研究工作流示例,以及可在你自己的 AI 代理上使用的案例。
一套由 K-Dense 创建的、涵盖 163 项即用型科学与研究技能的综合性合集(包括癌症基因组学、个体级 1000 Genomes 查询、托管式调控序列预测、实时病原体变异监测、分析方法验证、PK/PD 建模与剂量选择、全文生物医学与监管文献检索、药物-靶点结合、有界生物医学知识图谱搜索、分子动力学、RNA 速率、微生物组基础模型、地理空间科学、时间序列预测、通过 Hugging Science 进行科学 ML 资源发现、78+ 科学数据库等),适用于任何支持开放 Agent Skills 标准的 AI 代理。该仓库同时也是一个可移植的 Agent Plugins 包(plugin.json + skills/),因此支持插件的客户端可将整个合集作为一个插件加载。兼容 Cursor、Claude Code、Codex、Google Antigravity 等。让你的 AI 代理成为能够在生物学、化学、医学等领域执行复杂多步骤科学工作流的研究助手。
⭐ 帮助让"用于科学的 AI"更容易被发现:如果 Scientific Agent Skills 节省了你的时间、教会了你的代理某个工作流,或帮助你的实验室更快推进,请为该仓库点亮 star。Star 是一种公开信号,表明这些开源、可复用的研究技能值得持续维护:它有助于科学家、工程师和开源贡献者发现该项目,展示哪些 agent-skill 标准正在获得真正的采用,并为我们持续为社区扩充合集提供了清晰的理由。
这些技能使你的 AI 代理能够在多个科学领域中无缝地使用专业的科学库、数据库和工具。
虽然代理本身可以使用任何 Python 包或 API,但这些明确定义的技能提供了经过策展的文档和示例,使其在以下工作流中显著更强、更可靠: · 🧬 生物信息学与基因组学 - 序列分析、单细胞 RNA-seq、基因调控网络、变异注释、系统发育分析 · 🧪 化学信息学与药物发现 - 分子性质预测、虚拟筛选、ADMET 分析、分子对接、先导优化 · 🔬 蛋白质组学与质谱 - LC-MS/MS 处理、肽段鉴定、谱图匹配、蛋白质定量 · 🏥 临床研究与证据工作流 - 临床试验、药物基因组学、变异证据评估、药代动力学/药效动力学建模与给药方案评估、聚合决策支持评估、来源受限的草案报告结构,以及临床医生撰写治疗决策的格式化 · 🧠 医疗 AI 与生物信号研究 - EHR 与模型研究、生理信号分析以及回顾性验证——不涉及患者特异性诊断、治疗、告警或部署决策 · 🐭 临床前研究与动物福利 - 用于实验室动物研究的多变量严重程度评分与人道终点预测,用于 3Rs/优化分析以及欧盟指令 2010/63/EU 报告——仅作为严重程度评估的辅助工具,绝非判定规则 · 🖼️ 医学影像与数字病理 - 注重隐私的 DICOM 处理与仅供研究使用的全切片图像分析、计算病理与放射学数据工作流 · 🤖 机器学习与 AI - 深度学习、强化学习、时间序列分析、模型可解释性、贝叶斯方法 · 🔮 材料科学与化学 - 晶体结构分析、相图、代谢建模、计算化学 · 🌌 物理学与天文学 - 天文数据分析、坐标变换、宇宙学计算、符号数学、物理计算 · ⚙️ 工程与仿真 - 离散事件仿真、多目标优化、代谢工程、系统建模、流程优化 · 📊 数据分析与可视化 - 统计分析、网络分析、时间序列、可发表级图表、大规模数据处理、EDA · 🌍 地理空间科学与遥感 - 卫星影像处理、GIS 分析、空间统计、地形分析、面向地球观测的机器学习 · 🧪 实验室自动化 - 液体处理协议、实验室设备控制、工作流自动化、LIMS 集成 · 📚 科研交流 - 可追溯证据的写作、机密授权同行评审、文献综合、文档处理、无宏 PPTX 海报、幻灯片、示意图以及引文管理 · 🔬 多组学与系统生物学 - 多模态数据整合、pathway analysis、网络生物学、系统级洞察 · 🧬 蛋白质工程与设计 - 蛋白质语言模型、结构预测、序列设计、功能注释 · 🧰 代理平台与基础设施 - 基于 Pi 进行构建,配备 SDK、RPC、扩展、自定义 provider/模型、packages、TUI 组件与 session 工具 · 🎓 研究方法学 - 证据受限的候选假设、科学头脑风暴、批判性思维、基金申请书撰写,以及对学术成果的低风险定性评估 · ⚖️ 法规与标准 - 为 ISO 管理体系与实验室标准起草的证据准备文档,以及在 ICH/USP/CLSI 框架下的分析方法验证、确认与转移——供具备资质的人员审核,绝非认证、认可或方法放行决定
将你的 AI 编程代理转变为桌面上的"AI 科学家"! 🎬 初次接触 Scientific Agent Skills?观看我们的 Getting Started with Scientific Agent Skills(Scientific Agent Skills 入门)视频,快速上手。 🎥 更多教程
来自 K-Dense YouTube 频道的这些技能在真实研究任务上的录像演示:
| 视频 | 内容 |
|---|---|
| Skills 101: Build Your Own Scientific Agent Skill | 从零开始编写、测试并打包一项新技能 |
| Literature Review and Hypothesis Generation | 检索文献并生成有依据的假设 |
| Draft and Budget an Experimental Protocol | 将计划中的实验转化为带预算的书面方案 |
| Draft Responses to Reviewer Comments | 基于审稿意见构建逐条答复 |
| Can AI Reproduce a Nature Medicine Paper? | 对一篇已发表分析进行端到端复现尝试 |
📦 内容概览
本仓库提供 163 项科学与研究技能,按以下类别组织: · 100+ 科学与金融数据库 - 一个统一的 database-lookup 技能,可确定性地、提供丰富溯源地访问 78 个公共数据库(PubChem、ChEMBL、UniProt、COSMIC、ClinicalTrials.gov、FRED、USPTO 等),并为 DepMap、Imaging Data Commons、PrimeKG、NCATS ARAX、U.S.Treasury Fiscal Data、Hugging Science、OneKGPd 与 Genomic Intelligence 提供专用技能。BioServices(~40 项生物信息服务)、BioPython(通过 Entrez 接入 39 个 NCBI 子数据库)与 gget(20+ 基因组学数据库)等多数据库包进一步扩展了覆盖范围 · 70+ 优化的 Python 包技能 - 为 RDKit、Scanpy、PyTorch Lightning、scikit-learn、PyTDC、PathML、pydicom、NeuroKit2、PufferLib、QuTiP、GeoPandas、pymatgen、BioPython、Qiskit、分子动力学(OpenMM/MDAnalysis)以及其他包提供明确、版本感知的工作流。代理仍可使用 任何 Python 包;
这些技能为所列出的包提供更强、更安全的指引 · 9 项科学集成技能 - 面向 Benchling、DNAnexus、LatchBio、OMERO、Protocols.io、Open Notebook、Ginkgo Cloud Lab、LabArchives 与 Opentrons 的明确技能。同样,代理并不受限于这些——任何可从 Python 访问的 API 或平台皆可使用;这些技能是经过优化的、预先记录的路径 · 30+ 分析与交流工具 - 文献综述、可追溯证据的科研写作、机密同行评审、文档处理、Paperclip(全文论文、FDA/PMDA/EMA 文件以及带行级固定引用的试验注册库)、Paperzilla、Exa Search、无宏 PPTX 海报、幻灯片、示意图、信息图表、Mermaid 图等 · 10+ 研究与临床工具 - 证据受限的假设生成、基金申请书撰写、聚合临床决策支持研究、临床医生撰写治疗方案的格式化、PK/PD 建模与仿真(NCA、群体 PK、暴露-反应、生物等效性、首次人体剂量)、BIDS、ISO 标准就绪证据准备(ISO 13485、ISO 14971、ISO/IEC 17025、ISO 15189)、分析方法验证与转移(ICH Q2(R2)/Q14、ICH M10、USP、CLSI EP)、情景分析,以及通过 Autoskill 基于工作流起草技能
每项技能包括: · ✅ 全面的文档(SKILL.md) · ✅ 实用的代码示例 · ✅ 用例与最佳实践 · ✅ 集成指南 · ✅ 参考资料 · ✅ 为每项随 scripts/ 发行的技能配备测试套件——若某个 PR 新增了捆绑工具但缺少测试,CI 将阻断该 PR
📋 目录 · 内容概览 · 为何使用本项目? · 入门 · 安全免责声明 · 支持开源 · 前提条件 · 快速示例 · 用例 · 可用技能 · 来自博客 · 贡献 · 故障排查 · 常见问题 · 支持 · 引用 · 许可证
🚀 为何使用本项目?⚡ 加速你的研究 · 节省数日工作 - 跳过 API 文档调研与集成搭建 · 经过审阅的起点 - 经过测试的示例,附明确的验证、溯源与安全边界;请在目标环境中验证 · 多步骤工作流 - 通过单条提示执行复杂流水线 🎯 全面的覆盖 · 163 项技能 - 覆盖所有主要科学领域的广泛能力 · 100+ 数据库 - 通过 database-lookup 统一访问 78+ 数据库,并配备专用数据访问技能以及 BioServices、BioPython、gget 等多数据库包 · 70+ 优化的 Python 包技能 - 针对 RDKit、Scanpy、PyTorch Lightning、scikit-learn、PyTDC、pydicom、PufferLib、QuTiP、GeoPandas、pymatgen、Qiskit、分子动力学(OpenMM/MDAnalysis)、scVelo、TimesFM 等包的当前、版本范围明确的指引(代理可使用任何 Python 包;
这些是预先记录的路径) 🔧 易于集成 · 简单安装 - 将技能复制到你的 skills 目录即可开始使用 · 已配置的发现机制 - 兼容的宿主可从其配置的 skill 路径中发现并使用相关技能 · 文档完善 - 每项技能都包含示例、用例与最佳实践 🌟 维护与支持 · 定期更新 - 由 K-Dense 团队持续维护与扩充 · 在 CI 中测试 - 每项随 scripts/ 发行的技能在 tests/ 下都有一个套件,加上仓库级的结构性契约(frontmatter、链接解析、脚本解析、--help 行为),每次 PR 都会运行 · 由社区驱动 - 开源,拥有活跃
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Scientific Agent Skills
🔔 Claude Scientific Skills is now Scientific Agent Skills.Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude.New: K-Dense BYOK — A free, open-source AI co-scientist that runs on your desktop, powered by Scientific Agent Skills.Bring your own API keys, pick from 40+ models, and get a full research workspace with web search, file handling, 100+ scientific databases, and access to all 163 skills in this repo.Your data stays on your computer, and you can optionally scale to cloud compute via Modal for heavy workloads.
Get started here. 🎥 Webinar recording — Getting Started with K-Dense BYOK A hands-on walkthrough of K-Dense BYOK, our free, open-source AI co-scientist that runs locally on your own machine and is powered by Scientific Agent Skills.We cover how to set it up, bring your own API keys, and run real research workflows with these skills.No prior technical experience needed.Watch the recording → Stay up to date: Follow K-Dense on X, LinkedIn, YouTube, and Reddit for new skills, release announcements, walkthroughs, research workflow demos, and examples you can use with your own AI agent.
A comprehensive collection of 163 ready-to-use scientific and research skills (covering cancer genomics, individual-level 1000 Genomes queries, hosted regulatory-sequence prediction, live pathogen-variant surveillance, analytical method validation, PK/PD modelling and dose selection, full-text biomedical and regulatory literature retrieval, drug-target binding, bounded biomedical knowledge graph search, molecular dynamics, RNA velocity, microbiome foundation models, geospatial science, time series forecasting, scientific ML resource discovery via Hugging Science, 78+ scientific databases, and more) for any AI agent that supports the open Agent Skills standard, created by K-Dense.
The repository is also a portable Agent Plugins package (plugin.json + skills/), so plugin-capable clients can load the whole collection as one plugin.Works with Cursor, Claude Code, Codex, Google Antigravity, and more.Transform your AI agent into a research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond. ⭐ Help make AI for science easier to discover: If Scientific Agent Skills saves you time, teaches your agent a workflow, or helps your lab move faster, please star this repository.
A star is a public signal that these open, reusable research skills are worth maintaining: it helps scientists, engineers, and open-source contributors find the project, shows which agent-skill standards are gaining real adoption, and gives us a clear reason to keep expanding the collection for the community.
These skills enable your AI agent to seamlessly work with specialized scientific libraries, databases, and tools across multiple scientific domains.
While the agent can use any Python package or API on its own, these explicitly defined skills provide curated documentation and examples that make it significantly stronger and more reliable for the workflows below: · 🧬 Bioinformatics & Genomics - Sequence analysis, single-cell RNA-seq, gene regulatory networks, variant annotation, phylogenetic analysis · 🧪 Cheminformatics & Drug Discovery - Molecular property prediction, virtual screening, ADMET analysis, molecular docking, lead optimization · 🔬 Proteomics & Mass Spectrometry - LC-MS/MS processing, peptide identification, spectral matching, protein quantification · 🏥 Clinical Research & Evidence Workflows - Clinical trials, pharmacogenomics, variant evidence review, pharmacokinetic/pharmacodynamic modelling and dose-regimen evaluation, aggregate decision-support evaluation, source-bound draft report structures, and formatting of clinician-authored treatment decisions · 🧠 Healthcare AI & Biosignal Research - EHR and model research, physiological signal analysis, and retrospective validation—not patient-specific diagnosis, treatment, alarms, or deployment decisions · 🐭 Preclinical Research & Animal Welfare - Multivariate severity scoring and humane-endpoint forecasting for laboratory animal studies, for 3Rs/refinement analysis and EU Directive 2010/63/EU reporting—an aid to severity assessment, never a decision rule · 🖼️ Medical Imaging & Digital Pathology - Privacy-aware DICOM processing and research-only whole-slide image analysis, computational pathology, and radiology data workflows · 🤖 Machine Learning & AI - Deep learning, reinforcement learning, time series analysis, model interpretability, Bayesian methods · 🔮 Materials Science & Chemistry - Crystal structure analysis, phase diagrams, metabolic modeling, computational chemistry · 🌌 Physics & Astronomy - Astronomical data analysis, coordinate transformations, cosmological calculations, symbolic mathematics, physics computations · ⚙️ Engineering & Simulation - Discrete-event simulation, multi-objective optimization, metabolic engineering, systems modeling, process optimization · 📊 Data Analysis & Visualization - Statistical analysis, network analysis, time series, publication-quality figures, large-scale data processing, EDA · 🌍 Geospatial Science & Remote Sensing - Satellite imagery processing, GIS analysis, spatial statistics, terrain analysis, machine learning for Earth observation · 🧪 Laboratory Automation - Liquid handling protocols, lab equipment control, workflow automation, LIMS integration · 📚 Scientific Communication - Evidence-traceable writing, confidential authorized peer review, literature synthesis, document processing, macro-free PPTX posters, slides, schematics, and citation management · 🔬 Multi-omics & Systems Biology - Multi-modal data integration, pathway analysis, network biology, systems-level insights · 🧬 Protein Engineering & Design - Protein language models, structure prediction, sequence design, function annotation · 🧰 Agent Platforms & Infrastructure - Build on Pi with SDK, RPC, extensions, custom providers/models, packages, TUI components, and session tooling · 🎓 Research Methodology - Evidence-bounded candidate hypotheses, scientific brainstorming, critical thinking, grant writing, and qualitative low-stakes evaluation of scholarly works · ⚖️ Regulatory & Standards - Draft evidence-preparation artifacts for ISO management-system and laboratory standards, plus analytical method validation, verification, and transfer under ICH/USP/CLSI frameworks—prepared for qualified review, never a certification, accreditation, or method-release decision
Transform your AI coding agent into an 'AI Scientist' on your desktop! 🎬 New to Scientific Agent Skills? Watch our Getting Started with Scientific Agent Skills video for a quick walkthrough. 🎥 More tutorials
Recorded walkthroughs of these skills on real research tasks, from the K-Dense YouTube channel:
| Video | What it covers |
|---|---|
| Skills 101: Build Your Own Scientific Agent Skill | Writing, testing, and packaging a new skill from scratch |
| Literature Review and Hypothesis Generation | Searching the literature and generating grounded hypotheses |
| Draft and Budget an Experimental Protocol | Turning a planned experiment into a costed, written protocol |
| Draft Responses to Reviewer Comments | Building a point-by-point rebuttal from reviewer feedback |
| Can AI Reproduce a Nature Medicine Paper? | An end-to-end reproduction attempt on a published analysis |
📦 What's Included
This repository provides 163 scientific and research skills organized into the following categories: · 100+ Scientific & Financial Databases - A unified database-lookup skill provides deterministic, provenance-rich access to 78 public databases (PubChem, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, FRED, USPTO, and more), plus dedicated skills for DepMap, Imaging Data Commons, PrimeKG, NCATS ARAX, U.S.Treasury Fiscal Data, Hugging Science, OneKGPd, and Genomic Intelligence.
Multi-database packages like BioServices (~40 bioinformatics services), BioPython (39 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage · 70+ Optimized Python Package Skills - Explicitly defined, version-aware workflows for RDKit, Scanpy, PyTorch Lightning, scikit-learn, PyTDC, PathML, pydicom, NeuroKit2, PufferLib, QuTiP, GeoPandas, pymatgen, BioPython, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), and others.The agent can still use any Python package;
these skills provide stronger, safer guidance for the packages listed · 9 Scientific Integration Skills - Explicitly defined skills for Benchling, DNAnexus, LatchBio, OMERO, Protocols.io, Open Notebook, Ginkgo Cloud Lab, LabArchives, and Opentrons.Again, the agent is not limited to these — any API or platform reachable from Python is fair game;
these skills are the optimized, pre-documented paths · 30+ Analysis & Communication Tools - Literature review, evidence-traceable scientific writing, confidential peer review, document processing, Paperclip (full-text papers, FDA/PMDA/EMA filings, and trial registries with line-pinned citations), Paperzilla, Exa Search, macro-free PPTX posters, slides, schematics, infographics, Mermaid diagrams, and more · 10+ Research & Clinical Tools - Evidence-bounded hypothesis generation, grant writing, aggregate clinical decision-support research, clinician-authored treatment-plan formatting, PK/PD modelling and simulation (NCA, population PK, exposure-response, bioequivalence, first-in-human dose), BIDS, ISO standards-readiness evidence preparation (ISO 13485, ISO 14971, ISO/IEC 17025, ISO 15189), analytical method validation and transfer (ICH Q2(R2)/Q14, ICH M10, USP, CLSI EP), scenario analysis, and workflow-derived skill drafting with Autoskill
Each skill includes: · ✅ Comprehensive documentation (SKILL.md) · ✅ Practical code examples · ✅ Use cases and best practices · ✅ Integration guides · ✅ Reference materials · ✅ A test suite for every skill that ships scripts/ — CI blocks a pull request that adds bundled tooling without one
📋 Table of Contents · What's Included · Why Use This? · Getting Started · Security Disclaimer · Support Open Source · Prerequisites · Quick Examples · Use Cases · Available Skills · From the Blog · Contributing · Troubleshooting · FAQ · Support · Citation · License
🚀 Why Use This?⚡ Accelerate Your Research · Save Days of Work - Skip API documentation research and integration setup · Reviewed Starting Points - Tested examples with explicit validation, provenance, and safety boundaries;
verify them in the target environment · Multi-Step Workflows - Execute complex pipelines with a single prompt 🎯 Comprehensive Coverage · 163 Skills - Extensive coverage across all major scientific domains · 100+ Databases - Unified access to 78+ databases via database-lookup, plus dedicated data access skills and multi-database packages like BioServices, BioPython, and gget · 70+ Optimized Python Package Skills - Current, version-scoped guidance for packages including RDKit, Scanpy, PyTorch Lightning, scikit-learn, PyTDC, pydicom, PufferLib, QuTiP, GeoPandas, pymatgen, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), scVelo, and TimesFM (the agent can use any Python package;
these are the pre-documented paths) 🔧 Easy Integration · Simple Setup - Copy skills to your skills directory and start working · Configured Discovery - Compatible hosts can find and use relevant skills from their configured skill paths · Well Documented - Each skill includes examples, use cases, and best practices 🌟 Maintained & Supported · Regular Updates - Continuously maintained and expanded by K-Dense team · Tested in CI - Every skill that ships scripts/ has a suite under tests/, plus a repo-wide structural contract (frontmatter, link resolution, script parsing, --help behavior) that runs on every pull request · Community Driven - Open source with activ
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- 可核验的收入或付费证据查官网定价页与付费口径;第三方数据源(如 GetLatka)只作旁证,需标注来源与时点。
- 商业模式未定确认按席位/按用量/授权还是开源托管版收费;开源项目另查 LICENSE 与是否存在付费版。
- 只有单一来源找一手站点或其他渠道是否重复出现同一产品;社区热帖数量不等于商业进展。
通用验证清单(谁有这个问题/谁愿意付费/一个人能交付哪一小步)见我们的筛选方法。