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K-Dense 推出 Scientific Agent Skills：163 项科学研究技能可将 AI Agent 变为“AI 科学家”

英文标题：🔥 K-Dense-AI/scientific-agent-skills

K-Dense 发布 Scientific Agent Skills（原 Claude Scientific Skills），这是一个遵循开放 Agent Skills 标准的科学研究技能库，可兼容 Cursor、Claude Code、Codex、Google Antigravity 等 AI Agent。该项目提供 163 项可直接使用的科学与研究技能，并覆盖 100 多个科学和金融数据库、70 多个优化的 Python 包工作流，以及多类科研集成、分析与科学传播工具。技能覆盖生物信息学、药物发现、临床研究、医学影像、材料科学、地理空间科学、实验室自动化等领域，旨在让 Agent 更可靠地执行复杂的多步骤科研流程。项目同时提供本地运行的开源 AI 协同科学家 K-Dense BYOK，用户可自带 API Key，并可选择 40 多种模型。
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中文

许可证：MIT 版本 技能 数据库 Agent Skills Agent 插件 安全扫描 技能测试 兼容 X LinkedIn YouTube Reddit

🔔 Claude Scientific Skills 现已更名为 Scientific Agent Skills。 技能内容保持不变，但兼容性更广——现已支持任何遵循开放 Agent Skills 标准的 AI Agent，而不再仅限于 Claude。

新品：K-Dense BYOK ——一款免费的开源 AI 协同科学家，可在你的桌面设备上运行，并由 Scientific Agent Skills 驱动。自带 API Key，可从 40 多个模型中选择，并获得完整的研究工作区，包括网页搜索、文件处理、100 多个科学数据库，以及本仓库全部 161 项技能的访问权限。你的数据始终保留在本地计算机上；对于重负载任务，还可选择通过 Modal 扩展至云端计算。从这里开始。

🎥 在线直播研讨会——开始使用 K-Dense BYOK · 2026 年 8 月 25 日，星期二 · 太平洋时间下午 2:00 / 美东时间下午 5:00 · 免费在线。欢迎参加我们的实操演示，了解 K-Dense BYOK：这款可在你自己机器本地运行、由 Scientific Agent Skills 驱动的免费开源 AI 协同科学家。我们将展示如何完成设置、接入你自己的 API Key，以及如何通过这些技能运行真实科研工作流。无需任何既有技术经验，活动全程欢迎提问。立即预约 →

**保持关注：**在 X、LinkedIn、YouTube 和 Reddit 关注 K-Dense，获取可用于你自己的 AI Agent 的新技能、版本发布公告、操作演练、研究工作流演示和示例。

这是由 K-Dense 创建的一套全面集合，包含 163 项可直接使用的科学与研究技能，适用于任何支持开放 Agent Skills 标准的 AI Agent。覆盖领域包括癌症基因组学、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、全文生物医学和监管文献检索、药物—靶标结合、有界生物医学知识图谱搜索、分子动力学、RNA velocity、微生物组基础模型、地理空间科学、时间序列预测、通过 Hugging Science 发现科学机器学习资源、78 个以上科学数据库等。该仓库也是一个可移植的 Agent Plugins 软件包（plugin.json + skills/），因此支持插件的客户端可将整套内容作为一个插件加载。兼容 Cursor、Claude Code、Codex、Google Antigravity 等。它可将你的 AI Agent 转变为能够在生物学、化学、医学及其他领域执行复杂多步骤科学工作流的研究助手。

⭐ **帮助让科学 AI 更易被发现：**如果 Scientific Agent Skills 为你节省时间、教会你的 Agent 一种工作流，或帮助你的实验室提升效率，请为本仓库点亮 Star。Star 是一项公开信号，表明这些开放、可复用的研究技能值得持续维护：它能帮助科学家、工程师和开源贡献者发现该项目，展示哪些 Agent 技能标准正获得真实采用，并为我们持续为社区扩展该集合提供明确动力。

这些技能使你的 AI Agent 能够顺畅使用横跨多个科学领域的专业科学库、数据库和工具。虽然 Agent 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筛选的文档和示例，使其能够更强大、更可靠地执行下列工作流：

  • 🧬 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、先导化合物优化
  • 🔬 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、变异证据审查、药代动力学/药效动力学建模及给药方案评估、汇总决策支持评估、受来源约束的报告草案结构，以及由临床医生撰写的治疗决策格式化
  • 🧠 医疗 AI 与生物信号研究 - EHR 和模型研究、生理信号分析以及回顾性验证——不用于面向特定患者的诊断、治疗、告警或部署决策
  • 🖼️ 医学影像与数字病理学 - 注重隐私的 DICOM 处理和仅限研究用途的全切片图像分析、计算病理学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、过程优化
  • 📊 数据分析与可视化 - 统计分析、网络分析、时间序列、出版级图表、大规模数据处理、探索性数据分析（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、用于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
  • 📚 科学传播 - 证据可追溯写作、保密且经授权的同行评审、文献综述、文档处理、无宏 PPTX 海报、幻灯片、示意图和引文管理
  • 🔬 多组学与系统生物学 - 多模态数据整合、通路分析、网络生物学、系统层面的洞察
  • 🧬 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
  • 🧰 Agent 平台与基础设施 - 基于 Pi 构建，包括 SDK、RPC、扩展、自定义提供商/模型、软件包、TUI 组件和会话工具
  • 🎓 研究方法论 - 受证据约束的候选假设、科学头脑风暴、批判性思维、基金申请写作，以及对学术作品进行定性、低风险评估
  • ⚖️ 监管与标准 - 为 ISO 管理体系和实验室标准起草证据准备材料，以及在 ICH/USP/CLSI 框架下开展分析方法验证、确认和转移——供具备资质者审查，绝不构成认证、认可或方法放行决定

在你的桌面上将 AI 编程 Agent 转变为“AI 科学家”！

🎬 **刚接触 Scientific Agent Skills？**观看我们的 开始使用 Scientific Agent Skills 视频，快速了解使用方法。

以下是来自 K-Dense YouTube 频道的录制演示，展示这些技能在真实研究任务中的应用：

本仓库提供 163 项科学与研究技能，分为以下类别：

  • 100+ 科学与金融数据库 - 统一的数据库查询技能可确定性地访问 78 个公共数据库，并提供丰富的溯源信息，涵盖 PubChem、ChEMBL、UniProt、COSMIC、ClinicalTrials.gov、FRED、USPTO 等；同时还为 DepMap、Imaging Data Commons、PrimeKG、NCATS ARAX、美国财政部 Fiscal Data、Hugging Science、OneKGPd 和 Genomic Intelligence 提供专用技能。BioServices（约 40 项生物信息学服务）、BioPython（通过 Entrez 访问 39 个 NCBI 子数据库）和 gget（20 多个基因组学数据库）等多数据库软件包进一步扩大了覆盖范围
  • 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）等提供明确定义、具备版本感知能力的工作流。Agent 仍然可以使用任何 Python 包；这些技能为所列软件包提供更强、更安全的指导
  • 9 项科学集成技能 - 为 Benchling、DNAnexus、LatchBio、OMERO、Protocols.io、Open Notebook、Ginkgo Cloud Lab、LabArchives 和 Opentrons 提供明确定义的技能。同样，Agent 并不局限于这些平台——任何可通过 Python 访问的 API 或平台都可使用；这些技能是经过优化并预先编写文档的路径
  • 30+ 分析与传播工具 - 文献综述、证据可追溯的科学写作、保密同行评审、文档处理、Paperclip（提供全文论文、FDA/PMDA/EMA 申报文件和试验注册信息，并带有行级定位引文）、Paperzilla、Exa Search、无宏 PPTX 海报、幻灯片、示意图、信息图、Mermaid 图表等
  • 10+ 研究与临床工具 - 受证据约束的假设生成、基金申请写作、汇总临床决策支持研究、临床医生撰写的治疗计划格式化、PK/PD 建模与模拟（NCA、群体 PK、暴露—反应、生物等效性、首次人体试验剂量）、BIDS、ISO 标准就绪度证据准备（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，以及使用 Autoskill 基于工作流生成技能草案

每项技能均包括：

  • ✅ 完整文档（SKILL.md）
  • ✅ 实用代码示例
  • ✅ 使用场景与最佳实践
  • ✅ 集成指南
  • ✅ 参考材料
  • ✅ 每项附带 scripts/ 的技能均配有测试套件——CI 会阻止为打包工具新增却未附带测试的 Pull Request

⚡ 加速你的研究

  • 节省数天工作量 - 跳过 API 文档调研和集成配置

  • 经过审查的起点 - 提供含明确验证、溯源和安全边界的测试示例；应在目标环境中进行验证

  • 多步骤工作流 - 通过单个提示词执行复杂流程

  • 161 项技能 - 覆盖所有主要科学领域的广泛能力

  • **1

English / 原文

License: MIT Version Skills Databases Agent Skills Agent Plugins Security Scan Skill Tests Works with X LinkedIn YouTube Reddit

🔔 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 161 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.

🎥 Live webinar —Getting Started with K-Dense BYOK · Tuesday, August 25, 2026 · 2:00 PM PT / 5:00 PM ET · Online, free Join us for 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'll show you how to set it up, bring your own API keys, and run real research workflows with these skills. No prior technical experience needed, and questions are welcome throughout. Save your spot →

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
  • 🖼️ 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.

Recorded walkthroughs of these skills on real research tasks, from the K-Dense YouTube channel:

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

⚡ 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

  • 161 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)

  • 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

  • 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 active community contributions

  • Enterprise Ready - Commercial support available for advanced needs

Option 1: npx (supported hosts)

Install Scientific Agent Skills with a single command:

npx skills add K-Dense-AI/scientific-agent-skills

This is a common standards-based installer for supported Agent Skills hosts, including current versions of Claude Code , Claude Cowork , Codex , Gemini CLI , Google Antigravity , and Cursor. Confirm installation paths and optional metadata behavior in your host's current documentation.

Option 2: GitHub CLI (gh skill)

If you use the GitHub CLI (v2.90.0+), you can install skills with gh skill:

# Browse and install interactively
gh skill install K-Dense-AI/scientific-agent-skills

# Install a specific skill directly
gh skill install K-Dense-AI/scientific-agent-skills scanpy

# Target a specific agent host
gh skill install K-Dense-AI/scientific-agent-skills --agent cursor
gh skill install K-Dense-AI/scientific-agent-skills --agent claude-code
gh skill install K-Dense-AI/scientific-agent-skills --agent codex
gh skill install K-Dense-AI/scientific-agent-skills --agent gemini

gh skill automatically installs to the correct directory for your agent host and records provenance metadata for supply chain integrity.

Pin to a specific release tag or commit SHA for reproducible installs:

# Pin to a release tag
gh skill install K-Dense-AI/scientific-agent-skills --pin v2.64.0

# Pin to a commit SHA
gh skill install K-Dense-AI/scientific-agent-skills --pin abc123def

Keeping skills up to date

# Check for updates interactively
gh skill update

# Update all installed skills
gh skill update --all

Option 3: Agent Plugins (Cursor, Codex, and other plugin clients)

This repository is a valid Agent Plugins 1.0.0 package: root plugin.json plus Agent Skills under skills/. Clients that support the standard discover every immediate child of skills/ that contains a SKILL.md.

Cursor — symlink or copy the repo into the local plugins directory, then reload:

mkdir -p ~/.cursor/plugins/local
ln -s "$(pwd)" ~/.cursor/plugins/local/scientific-agent-skills

Restart Cursor or run Developer: Reload Window , then confirm the plugin and its skills appear under Customize. See Cursor plugins.

Codex — install from a local checkout (confirm the current CLI flag names in Codex docs):

Compatible clients (Cursor, Codex, GitHub Copilot, VS Code, Kiro, and others listed at agent-plugins.org) share the same package layout; installation UX stays client-specific.

Other Agent Skills hosts (OpenClaw, NemoClaw, Pi, Hermes, …)

Agent hosts differ in install paths, discovery settings, and support for optional frontmatter fields. npx skills add (Option 1) commonly installs into the ~/.agents/skills/ convention, with project-scoped installs under .agents/skills/; confirm both paths against your host's current documentation. To install manually on a host configured to scan one of those locations:

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git ~/.agents/skills/scientific-agent-skills   # user-level
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git .agents/skills/scientific-agent-skills      # project-level

For Hermes versions that support skill taps, add the repository as a tap:

hermes skills tap add K-Dense-AI/scientific-agent-skills

Every SKILL.md has YAML frontmatter, but legacy and community skills vary in metadata formatting (block or flow style) and optional extension fields. Repository updates must keep metadata.version as a quoted numeric string and pass canonical skills-ref validate ./skills/<skill-name> checks. Hosts may interpret optional metadata and credential prompts differently, so verify behavior on the target host. Because 161 skills add up to a lot of standing context, consider installing a topical subset rather than the whole collection.

NemoClaw note: NemoClaw runs agents inside NVIDIA OpenShell with default-deny outbound networking. Skills are discovered and loaded normally, but any skill that needs the network — package installs via uv, or API calls (Exa, Parallel, Benchling, NCBI, Materials Project, …) — only works once the operator pre-approves the relevant domains in the OpenShell TUI.

That's it! A compatible host can discover the skills from its configured paths and use them when relevant. You can also invoke any skill manually by mentioning the skill name in your prompt.

Skills can execute code and influence your coding agent's behavior. Review what you install.

Agent Skills are powerful — they can instruct your AI agent to run arbitrary code, install packages, make network requests, and modify files on your system. A malicious or poorly written skill has the potential to steer your coding agent into harmful behavior.

We take security seriously. All contributions go through a review process, and we run LLM-based security scans (via Cisco AI Defense Skill Scanner) on every skill in this repository. However, as a small team with a growing number of community contributions, we cannot guarantee that every skill has been exhaustively reviewed for all possible risks.

It is ultimately your responsibility to review the skills you install and decide which ones to trust.

We recommend the following:

  • Do not install everything at once. Only install the skills you actually need for your work. While installing the full collection was reasonable when K-Dense created and maintained every skill, the repository now includes many community contributions that we may not have reviewed as thoroughly.

  • Read theSKILL.md before installing. Each skill's documentation describes what it does, what packages it uses, and what external services it connects to. If something looks suspicious, don't install it.

  • Check the contribution history. Skills authored by K-Dense (K-Dense-AI) have been through our internal review process. Community-contributed skills have been reviewed to the best of our ability, but with limited resources.

  • Run the security scanner yourself. Before installing third-party skills, scan them locally:

    uv pip install cisco-ai-skill-scanner
skill-scanner scan /path/to/skill --use-behavioral

  • Report anything suspicious. If you find a skill that looks malicious or behaves unexpectedly, please open an issue immediately so we can investigate.

Skills are scanned weekly — incrementally, so unchanged skills carry their previous findings forward, with a full rescan of everything at least every 30 days and whenever the scanner or model changes — and the results are published to docs/security-report.md. See SECURITY.md for our security policy, what is in scope, how to report a vulnerability privately, and how to contest a scan finding. We try to address security gaps as they arise.

❤️ Support the Open Source Community

Scientific Agent Skills is powered by 50+ incredible open source projects maintained by dedicated developers and research communities worldwide. Projects like Biopython, Scanpy, RDKit, scikit-learn, PyTorch Lightning, and many others form the foundation of these skills.

If you find value in this repository, please consider supporting the projects that make it possible:

  • ⭐ Star their repositories on GitHub
  • 💰 Sponsor maintainers via GitHub Sponsors or NumFOCUS
  • 📝 Cite projects in your publications
  • 💻 Contribute code, docs, or bug reports

👉 View the full list of projects to support

The docx , pdf , pptx , and xlsx document skills are created and maintained by Anthropic and vendored here from anthropics/skills. They are used under Anthropic's terms — see each skill's LICENSE.txt — and we track upstream so you get their latest improvements. All credit for those four skills goes to Anthropic.

  • Python : 3.13+ for repository tooling; individual skill dependencies may support broader Python ranges
  • uv : Python package manager (required for installing skill dependencies)
  • Client : Any agent that supports the Agent Skills standard (Cursor, Claude Code, Gemini CLI, Codex, Google Antigravity, etc.)
  • System : macOS, Linux, or Windows with WSL2
  • Dependencies : Automatically handled by individual skills (check SKILL.md files for specific requirements)

The skills use uv as the package manager for installing Python dependencies. Install it using the instructions for your operating system:

macOS and Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Alternative (via pip):

After installation, verify it works by running:

For more installation options and details, visit the official uv documentation.

Once you've installed the skills, you can ask your AI agent to execute complex multi-step scientific workflows. Here are some example prompts:

🧪 Drug Discovery Pipeline

Goal : Prioritize EGFR inhibitor candidates for preclinical lung-cancer research

Prompt :

Use available skills you have access to whenever possible. Query ChEMBL for EGFR inhibitors (IC50 < 50nM), analyze structure-activity relationships 
with RDKit, generate improved analogs with datamol, perform virtual screening with DiffDock 
against AlphaFold EGFR structure, search PubMed for resistance mechanisms, check COSMIC for 
mutations, and create visualizations and a comprehensive report.

Skills Used : database-lookup, rdkit, datamol, diffdock, paper-lookup, scientific-visualization

🔬 Single-Cell RNA-seq Analysis

Goal : Comprehensive analysis of 10X Genomics data with public data integration

Prompt :

Use available skills you have access to whenever possible. Load 10X dataset with Scanpy, perform QC and doublet removal, integrate with Cellxgene 
Census data, identify cell types using NCBI Gene markers, run differential expression with 
PyDESeq2, infer gene regulatory networks with Arboreto, enrich pathways via Reactome/KEGG, 
and identify therapeutic targets with Open Targets.

Skills Used : scanpy, cellxgene-census, database-lookup, pydeseq2, arboreto

🧬 Multi-Omics Biomarker Discovery

Goal : Integrate RNA-seq, proteomics, and metabolomics to predict patient outcomes

Prompt :

Use available skills you have access to whenever possible. Analyze RNA-seq with PyDESeq2, process mass spec with pyOpenMS, integrate metabolites from 
HMDB/Metabolomics Workbench, map proteins to pathways (UniProt/KEGG), find interactions via 
STRING, correlate omics layers with statsmodels, build predictive model with scikit-learn, 
and search ClinicalTrials.gov for relevant trials.

Skills Used : pydeseq2, pyopenms, database-lookup, statsmodels, scikit-learn

🎯 Virtual Screening Campaign

Goal : Discover allosteric modulators for protein-protein interactions

Prompt :

Use available skills you have access to whenever possible. Retrieve AlphaFold structures, identify interaction interface with BioPython, search ZINC 
for allosteric candidates (MW 300-500, logP 2-4), filter with RDKit, dock with DiffDock, 
rank with DeepChem, check PubChem suppliers, search USPTO patents, and optimize leads with 
MedChem/molfeat.

Skills Used : database-lookup, biopython, rdkit, diffdock, deepchem, medchem, molfeat

🏥 Research Variant Evidence Review

Goal : Annotate a synthetic or properly de-identified VCF for hereditary-cancer research and qualified review

Prompt :

Use available skills you have access to whenever possible. Work only with authorized synthetic
or de-identified data. Parse the VCF with pysam, annotate variants with Ensembl VEP, retrieve
ClinVar/COSMIC/NCBI Gene/UniProt evidence, and verify literature sources. Build an evidence-
traceable research summary with scientific-writing. If clinical-reports is used, create only a
visibly marked draft structure from a verified source-fact manifest for qualified review; do not
diagnose, assess individual risk, recommend treatment, or determine trial eligibility.

Skills Used : pysam, database-lookup, paper-lookup, scientific-writing, clinical-reports

🌐 Systems Biology Network Analysis

Goal : Analyze gene regulatory networks from RNA-seq data

Prompt :

Use available skills you have access to whenever possible. Query NCBI Gene for annotations, retrieve sequences from UniProt, identify interactions via 
STRING, map to Reactome/KEGG pathways, analyze topology with Torch Geometric, reconstruct 
GRNs with Arboreto, assess druggability with Open Targets, model with PyMC, visualize 
networks, and search GEO for similar patterns.

Skills Used : database-lookup, torch-geometric, arboreto, pymc, networkx, scientific-visualization

📖 Want more examples? Check out docs/examples.md for comprehensive workflow examples and detailed use cases across all scientific domains.

🧪 Drug Discovery & Medicinal Chemistry

  • Virtual Screening : Screen millions of compounds from PubChem/ZINC against protein targets
  • Lead Optimization : Analyze structure-activity relationships with RDKit, generate analogs with datamol
  • ADMET Prediction : Predict absorption, distribution, metabolism, excretion, and toxicity with DeepChem
  • Molecular Docking : Predict binding poses with DiffDock and rescore poses with affinity-oriented tools
  • Bioactivity Mining : Query ChEMBL for known inhibitors and analyze SAR patterns

🧬 Bioinformatics & Genomics

  • Sequence Analysis : Process DNA/RNA/protein sequences with BioPython and pysam
  • Single-Cell Analysis : Analyze 10X Genomics data with Scanpy, identify cell types, infer GRNs with Arboreto
  • Variant Annotation : Annotate research VCF files with Ensembl VEP and retrieve ClinVar evidence for qualified interpretation
  • Variant Database Management : Build scalable VCF databases with TileDB-VCF for incremental sample addition, efficient population-scale queries, and compressed storage of genomic variant data
  • Population Genomics : Query variants, cohort sample IDs, and relatedness in the 3,202-person GRCh38 1000 Genomes cohort with OneKGPd
  • Regulatory Sequence Models : Run hosted Genomic Intelligence promoter, splice, enhancer, chromatin, expression, and gene-annotation predictions for research—not clinical or diagnostic decisions
  • Pathogen Surveillance : Track which viral lineages are circulating now and how fast they are growing (SARS-CoV-2, influenza including H5N1, RSV, mpox, measles, dengue) through the GenSpectrum LAPIS API, with reporting lag measured rather than assumed
  • Gene Discovery : Query NCBI Gene, UniProt, and Ensembl for comprehensive gene information
  • Network Analysis : Identify protein-protein interactions via STRING, map to pathways (KEGG, Reactome)

🏥 Clinical Research & Evidence Workflows

  • Clinical Trials : Analyze aggregate trial landscapes and protocol criteria without deciding individual eligibility
  • Variant Evidence Review : Annotate authorized research data with ClinVar, COSMIC, and ClinPGx; qualified professionals retain interpretation responsibility
  • Drug Safety Research : Query FDA databases for aggregate adverse-event, interaction, and recall evidence
  • Clinical Pharmacology : Derive exposure metrics from concentration-time data, fit compartmental and population PK models, relate exposure to effect, and evaluate dosing regimens, bioequivalence, and first-in-human dose
  • Full-Text Evidence Retrieval : Search and read papers, regulatory filings, and trial records end to end with Paperclip, returning citations pinned to line numbers rather than to abstracts
  • Decision-Support Evaluation : Prepare synthetic or aggregate evaluation, evidence-profile, privacy, and governance artifacts—not live clinical decisions
  • Clinician-Authored Documentation : Structure verified source-bound report drafts and format treatment decisions already made by authorized licensed professionals

🔬 Multi-Omics & Systems Biology

  • Multi-Omics Integration : Combine RNA-seq, proteomics, and metabolomics data
  • Pathway Analysis : Enrich differentially expressed genes in KEGG/Reactome pathways
  • Network Biology : Reconstruct gene regulatory networks, identify hub genes
  • Biomarker Discovery : Integrate multi-omics layers to predict patient outcomes

📊 Data Analysis & Visualization

  • Statistical Analysis : Perform hypothesis testing, power analysis, and experimental design

  • Publication Figures : Create publication-quality visualizations with matplotlib and seaborn

  • Network Visualization : Visualize biological networks with NetworkX

  • Report Generation : Produce evidence-traceable research reports with Scientific Writing and document tools; Clinical Reports outputs remain visibly marked drafts built only from verified synthetic, de-identified, or aggregate source facts

  • Protocol Design : Author and simulate Opentrons or PyLabRobot protocols before trained-operator review

  • LIMS/ELN Integration : Prepare scoped Benchling and LabArchives operations with explicit authorization for remote writes

  • Workflow Automation : Validate and simulate multi-step laboratory workflows offline; physical execution stays behind equipment-specific operator safety gates

This repository contains 163 scientific and research skills organized across multiple domains. Each skill provides comprehensive documentation, code examples, and best practices for working with scientific libraries, databases, and tools.

Note: The Python package and integration skills listed below are explicitly defined skills — curated with documentation, examples, and best practices for stronger, more reliable performance. They are not a ceiling: the agent can install and use any Python package or call any API, even without a dedicated skill. The skills listed simply make common workflows faster and more dependable.

🧬 Bioinformatics & Genomics (27 skills)

  • RNA-seq pipelines: Bulk RNA-seq (end-to-end FASTQ -> counts -> DE -> enrichment orchestrator)
  • Sequence analysis: BioPython, pysam, scikit-bio, BioServices
  • Single-cell analysis: Scanpy, AnnData, scvi-tools, scVelo (RNA velocity), Arboreto, Cellxgene Census
  • Genomic tools: gget, current geniml/Gtars interval workflows, deepTools, FlowIO, Polars-Bio, Zarr, TileDB-VCF
  • Coordinate hygiene: Genomic Coordinates (convert intervals across BED/GFF/GTF/VCF/SAM/WIG conventions, normalise variant representations, and catch 0-based vs 1-based and assembly/contig-naming mismatches before they corrupt an analysis)
  • Population and sequence intelligence: OneKGPd (individual-level 1000 Genomes cohort queries) and Genomic Intelligence (hosted regulatory/gene-expression predictions; research only)
  • Differential expression: PyDESeq2
  • Functional enrichment: Pathway Enrichment (ORA, GSEA/preranked, ssGSEA via gseapy + g:Profiler; GO, KEGG, Reactome, WikiPathways, MSigDB)
  • Phylogenetics: ETE Toolkit, Phylogenetics (MAFFT, IQ-TREE 2, FastTree)
  • Microbiome foundation models: Waypoint (Outpost Bio's open Waypoint-6m/45m/170m checkpoints, the Atlas 539k-sample MGnify pretraining corpus, and the eight-task Compass benchmark — embedding, fine-tuning, benchmarking, and pretraining on taxonomic abundance profiles, with MetaPhlAn/Kraken2/QIIME 2 conversion)

🧪 Cheminformatics & Drug Discovery (10 skills)

  • Molecular manipulation: RDKit, Datamol, Molfeat
  • Deep learning: DeepChem, TorchDrug
  • Docking & screening: DiffDock
  • Molecular dynamics: OpenMM + MDAnalysis (MD simulation & trajectory analysis)
  • Cloud quantum chemistry: Rowan (pKa, docking, cofolding)
  • Drug-likeness: MedChem
  • Benchmarks: PyTDC 1.1.15 on its verified CPython 3.11 compatibility stack

🔬 Proteomics & Mass Spectrometry (2 skills)

  • Spectral processing: matchms, pyOpenMS

🏥 Clinical Research & Evidence Workflows (8 skills)

  • Clinical databases: via Database Lookup (ClinicalTrials.gov, ClinVar, ClinPGx, COSMIC, FDA, cBioPortal, Monarch, and more)
  • Clinical pharmacology: PK/PD Modeling (non-compartmental analysis, compartmental and population PK, exposure-response and Emax, TMDD, PBPK orientation, bioequivalence including RSABE/ABEL, allometric scaling and first-in-human dose, DDI prediction under ICH M12, concentration-QTc, and Bayesian therapeutic drug monitoring — stdlib + numpy/scipy, no proprietary estimation software invoked)
  • Cancer genomics: DepMap (cancer dependency scores, drug sensitivity)
  • Cancer imaging: Imaging Data Commons (NCI radiology & pathology datasets via idc-index)
  • Healthcare AI research: PyHealth
  • Decision-support research: local, aggregate or synthetic Clinical Decision Support evaluation and governance artifacts only
  • Clinical documentation: source-bound Clinical Reports drafts and formatting of verified clinician-authored decisions with Treatment Plans; neither skill diagnoses or recommends care

🖼️ Medical Imaging & Digital Pathology (4 skills)

  • DICOM processing: pydicom 3.0.2 with privacy-first local preflight and no diagnostic or de-identification-compliance claims
  • Whole slide imaging: histolab and research-only PathML 3.0.5
  • Virtual spatial transcriptomics: noncommercial DeepSpot-M for transcriptome-wide spatial gene expression from 224x224 H&E tiles

🧠 Neuroscience & Electrophysiology (3 skills)

  • Data standards: BIDS (Brain Imaging Data Structure for neuroscience and biomedical datasets)
  • Neural recordings: Neuropixels-Analysis (extracellular spikes, silicon probes, spike sorting)
  • Physiological signals: NeuroKit2 0.2.13 for reproducible research workflows—not diagnosis, monitoring decisions, or medical-device validation

🤖 Machine Learning & AI (14 core skills)

  • Deep learning: PyTorch Lightning, Transformers, Stable Baselines3, and version-separated PufferLib 3.0/4.0 workflows
  • Classical ML: scikit-learn, scikit-survival 0.28, and SHAP
  • Time series: aeon, TimesFM (Google's zero-shot foundation model for univariate forecasting)
  • Bayesian methods: PyMC
  • Optimization: PyMOO
  • Graph ML: Torch Geometric
  • Dimensionality reduction: UMAP-learn
  • Statistical modeling: statsmodels

🔮 Materials Science, Chemistry & Physics (7 skills)

  • Materials: current split pymatgen wrapper/core plus explicitly bounded Materials Project queries
  • Metabolic modeling: COBRApy
  • Astronomy: Astropy
  • Quantum computing: Cirq, PennyLane, Qiskit, QuTiP 5.3

⚙️ Engineering & Simulation (6 skills)

  • Lab hardware CAD: parametric build123d 0.11.1 models for microfluidic chips and molds, optomechanical mounts, microplate and cuvette adapters, and behavior rigs, checked against ANSI/SLAS and optical-table dimensional standards and reviewed with mandatory multi-view renders
  • Numerical computing: proprietary MATLAB R2026a and distinct GNU Octave 11.3 planning/review workflows
  • Computational fluid dynamics: bounded FluidSim 0.9 simulations with numerical-validity and HPC checks
  • Experimental flow measurement: OpenPIV (velocity fields from PIV image pairs, interrogation-window cross-correlation, spurious-vector validation, vorticity/strain-rate/turbulence statistics)
  • Discrete-event simulation: SimPy 4.1.2 with replication, warm-up, and output-analysis guidance
  • Symbolic math: SymPy

📊 Data Analysis & Visualization (22 skills)

  • Visualization: Matplotlib, Seaborn, Scientific Visualization
  • Geospatial analysis: GeoPandas 1.1.4 and GeoMaster (remote sensing, GIS, satellite imagery, spatial ML, 500+ examples)
  • Data processing: Dask, Polars, Vaex
  • Network analysis: NetworkX
  • Document processing: LiteParse (local PDF/document parsing with bounding boxes and OCR), MarkItDown, PDF, DOCX, PPTX, and XLSX
  • Infographics: Infographics (AI-powered professional infographic creation)
  • Diagrams: Markdown & Mermaid Writing (text-based diagrams as default documentation standard)
  • Exploratory data analysis: bounded local EDA for explicitly supported formats, with unknown formats failing closed
  • Statistical analysis: Statistical Analysis workflows
  • Units and measurement uncertainty: Uncertainty & Units (pint dimensional checking, GUM uncertainty budgets, Type A/B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, CODATA constants)
  • Experimental design: Experimental Design (randomization, blocking, factorial/fractional-factorial DOE, crossover, cluster, sequential designs; pyDOE3)
  • Statistical power: Statistical Power (sample-size & power for t-tests, ANOVA, proportions, correlation, regression — closed-form plus simulation-based for GLMs, mixed models, and cluster designs)

🧪 Laboratory Automation (6 skills)

  • Liquid handling: offline-first PyLabRobot planning/simulation and Opentrons authoring, with physical execution behind explicit operator safety gates
  • Cloud lab: Ginkgo Cloud Lab (protein expression & purification across cell-free/E. coli/Pichia, IVT RNA synthesis, thermal shift and Echo-MS assays, SPR onboarding, fluorescent pixel art via autonomous RAC infrastructure)
  • Protocol management: bounded protocols.io reads across documented v3/v4 endpoints and non-executing write plans
  • LIMS/ELN integration: Benchling and the separate LabArchives legacy ELN and Inventory v1 APIs

🔬 Multi-omics & Systems Biology (3 skills)

  • Pathway analysis: via Database Lookup (KEGG, Reactome, STRING) and PrimeKG
  • Data management: LaminDB

🧬 Protein Engineering & Design (4 skills)

  • Protein language models: ESM
  • Glycoengineering: Glycoengineering (N/O-glycosylation prediction, therapeutic antibody optimization)
  • Cloud laboratory platform: Adaptyv (automated protein testing and validation)
  • Cloud structure & design platform: Tamarind (managed-GPU access to AlphaFold, Boltz, Chai, ESMFold, RFdiffusion, ProteinMPNN, BoltzGen, antibody/nanobody design, DiffDock/Vina docking, binding affinity, and MSA generation via REST API or MCP)

📚 Scientific Communication (27 skills)

  • Literature: Paper Lookup (PubMed, PMC, bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall), Literature Review, Paperzilla
  • Full-text corpus access: Paperclip (read-only virtual filesystem over ~11M full-text papers, 217K+ FDA/PMDA/EMA regulatory documents, clinical trial registries, and UniProt/PDB/ChEMBL entries — source-scoped semantic search, corpus-wide grep, SQL metadata queries, map/reduce reading across many papers, figure vision analysis, and line-pinned citations)
  • Advanced paper search: BGPT Paper Search (25+ structured fields per paper — methods, results, sample sizes, quality scores — from full text, not just abstracts)
  • Web intelligence: Parallel Web (web search, URL/PDF extraction, deep research, structured enrichment, entity discovery, and recurring monitoring), Exa Search, and Research Lookup
  • Research notebooks: Open Notebook (self-hosted NotebookLM alternative — PDFs, videos, audio, web pages; 16+ AI providers; multi-speaker podcast generation)
  • Writing: evidence-traceable Scientific Writing and local, confidential, authorized Peer Review
  • Document processing: LiteParse, PDF, DOCX, PPTX, XLSX, and MarkItDown
  • Publishing and paper workflows: Venue Templates
  • Presentations: Scientific Slides, LaTeX Posters, and macro-free PPTX Posters generated from author-approved local manifests
  • Diagrams: Scientific Schematics, Markdown & Mermaid Writing
  • Infographics: Infographics (10 types, 8 styles, colorblind-safe palettes)
  • Citations: Citation Management, pyzotero
  • Illustration: Generate Image (AI image generation with FLUX.2 Pro and Gemini 3.1 Flash Image / Nano Banana 2)

🔬 Scientific Databases & Data Access (11 skills → 100+ databases total)

A unified database-lookup skill provides deterministic REST API access to 78 public databases across all domains, with retrieval contracts, pagination/count reconciliation, and endpoint provenance. Dedicated skills cover specialized data platforms. Multi-database packages like BioServices (~40 bioinformatics services), BioPython (39 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage.

  • Unified access: Database Lookup (78 databases spanning chemistry, genomics, clinical, pathways, patents, economics, and more — PubChem, ChEMBL, UniProt, PDB, AlphaFold, KEGG, Reactome, STRING, ClinVar, COSMIC, ClinicalTrials.gov, FDA, FRED, USPTO, SEC EDGAR, and dozens more — with auditable filters and provenance)
  • Cancer genomics: DepMap (cancer cell line dependencies, drug sensitivity, gene effect profiles)
  • Cancer imaging: Imaging Data Commons (NCI radiology & pathology datasets via idc-index)
  • Knowledge graph: PrimeKG (precision medicine knowledge graph — genes, drugs, diseases, phenotypes)
  • Biomedical knowledge graph search: NCATS ARAX (bounded, Biolink-constrained one-hop and endpoint-pinned two-hop queries over knowledge graphs with up to five explicitly selected NCATS Translator providers, with provenance preservation)
  • Fiscal data: U.S. Treasury Fiscal Data (national debt, Treasury statements, auctions, exchange rates)
  • Scientific ML resource catalog: Hugging Science (curated index of datasets, models, blog posts, and interactive Spaces across 17 scientific domains — astronomy, biology, chemistry, climate, genomics, materials science, medicine, physics, scientific reasoning, and more — with usage patterns for datasets, transformers, and gradio_client)
  • Individual-level population genomics: OneKGPd (3,202-person high-coverage 1000 Genomes cohort queries)
  • Hosted regulatory genomics: Genomic Intelligence (promoter, splice, enhancer, chromatin, expression, and gene-annotation predictions for research use)
  • Ontology identifiers: Ontology Term Resolution (resolve free-text tissue, cell-type, disease, phenotype, assay, chemical, organism, and developmental-stage labels to term IDs and validate CURIEs against EBI OLS4, for GEO/ENA/BioSamples/CELLxGENE/HCA/ISA-Tab metadata)
  • Live pathogen surveillance: Pathogen Variant Surveillance (which viral lineages are circulating now, how fast they are growing, and what mutations they carry — SARS-CoV-2, influenza including H5N1, RSV, mpox, measles, dengue and more through the GenSpectrum LAPIS API, with lineage names resolved against the live pango-designation nomenclature and reporting lag measured rather than assumed)

🔧 Infrastructure & Platforms (11 skills)

  • Cloud compute: Modal
  • GPU acceleration: Optimize for GPU (CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, RAFT)
  • Genomics platforms: DNAnexus, LatchBio
  • Workflow engines: Nextflow (build/run/debug Nextflow & nf-core pipelines — DSL2 modules, executors/containers, HPC/cloud scaling) and pacsomatic (operator toolkit for the nf-core/pacsomatic tumor-normal somatic variant-calling workflow)
  • Microscopy: OMERO
  • Automation: Opentrons
  • Resource detection: Get Available Resources on request or before a clearly resource-sensitive local workload; redacted and without stress tests
  • Workflow mining: Autoskill (local screenpipe-based repeated workflow detection and skill drafting)
  • Agent platform development: Pi Agent (using Pi as a terminal coding harness and building on it with SDK, RPC/JSONL, extensions, custom providers/models, packages, TUI components, and session tooling)

🎓 Research Methodology & Planning (13 skills)

  • Ideation: evidence-aware Scientific Brainstorming and non-scoring Hypothesis Generation that keeps hypotheses labeled as candidates
  • Text-dataset hypothesis software: HypoGeniC/HypoRefine produces candidate textual patterns and task-prediction statistics, not validated scientific hypotheses
  • Autonomous optimization: Arbor (Hypothesis Tree Refinement — iteratively improve a code/model/agent-harness/data artifact against a dev evaluator while a held-out test gate guards against overfitting)
  • Critical analysis: Scientific Critical Thinking and qualitative, low-stakes Scholar Evaluation of works—never ranking people or supporting consequential decisions
  • Scenario analysis: What-If Oracle (4–6 branch possibility exploration, contingency planning, decision stress-testing)
  • Multi-perspective deliberation: Consciousness Council (diverse expert viewpoints, devil's advocate analysis)
  • Cognitive profiling: DHDNA Profiler (extract thinking patterns and cognitive signatures from any text)
  • Funding: Research Grants
  • Discovery: Research Lookup, Paper Lookup (10 academic databases)
  • Market analysis: evidence-traceable Market Research Reports with assumption-led sizing and forecast sensitivity

⚖️ Regulatory & Standards (2 skills)

  • Standards readiness: draft evidence-preparation artifacts for ISO 13485 (medical device QMS), ISO 14971 (device risk management), ISO/IEC 17025 (testing and calibration laboratories), and ISO 15189 (medical laboratories), with per-standard process domains selected by a --standard profile
  • Analytical method validation: plan, evaluate, and document validation, verification, and transfer of analytical procedures (HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, ligand-binding and cell-based assays) under whichever framework governs — ICH Q2(R2)/Q14 and ICH M10 encoded from their openly licensed text, with USP <1220>/<1225>/<1226>, the CLSI EP series, and ISO/IEC 17025 cited by designation and scope only; stdlib-only statistics, no network access
  • Assurance-lane separation: keeps ISO certification, laboratory accreditation, FDA QMSR inspection, CLIA certification, MDSAP, and EU MDR/IVDR evidence boundaries distinct—laboratories are accredited rather than certified, and ISO 15189 accreditation does not satisfy CLIA
  • Never a compliance, audit, assessment, certification, accreditation, or method-release decision; qualified RA/QA, legal, laboratory-director, assessor, and certification-body review is required

📖 For complete details on all skills , see docs/skills.md

💡 Looking for practical examples? Check out docs/examples.md for comprehensive workflow examples across all scientific domains.

Deep dives, benchmarks, and guides from the K-Dense blog that are directly relevant to using the skills in this repository.

Skill benchmarks and deep dives

Why the workflow layer matters

Security and safe deployment

Complementary open-source projects

We welcome contributions to expand and improve this scientific skills repository!

For detailed instructions on adding or updating a skill, see CONTRIBUTING.md. The guide covers repository structure, required SKILL.md frontmatter, Agent Skills specification requirements, versioning, validation, security scanning, and pull request expectations.

✨ Add New Skills

  • Create skills for additional scientific packages or databases
  • Add integrations for scientific platforms and tools

📚 Improve Existing Skills

  • Enhance documentation with more examples and use cases
  • Add new workflows and reference materials
  • Improve code examples and scripts
  • Fix bugs or update outdated information

🐛 Report Issues

  • Submit bug reports with detailed reproduction steps
  • Suggest improvements or new features
  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-skill)
  3. Follow CONTRIBUTING.md and the existing directory structure
  4. Ensure all new skills include valid SKILL.md files with required frontmatter and metadata.version
  5. Test your examples and workflows thoroughly, and add a suite under tests/<skill-name>/ if your skill ships scripts/
  6. Commit your changes (git commit -m 'Add amazing skill')
  7. Push to your branch (git push origin feature/amazing-skill)
  8. Submit a pull request with a clear description of your changes

✅ Adhere to theAgent Skills Specification — Every skill must follow the official spec (valid SKILL.md frontmatter, naming conventions, directory structure)
✅ Include a quoted metadata.version value in every SKILL.md
✅ Increment metadata.version when updating an existing skill
✅ Maintain consistency with existing skill documentation format
✅ Ensure all code examples are tested and functional
✅ Follow scientific best practices in examples and workflows
✅ Update relevant documentation when adding new capabilities
✅ Provide clear comments and docstrings in code
✅ Include references to official documentation

Every skill that ships scripts/ must have a test suite under tests/<skill-name>/ and an entry in tests/skill-requirements.toml. This is enforced — tests/_meta fails a pull request that adds bundled tooling without one, and it also runs a repo-wide structural contract over all skills (frontmatter conformance, SKILL.md length, local links resolving, scripts parsing, no shipped bytecode, no hardcoded local paths, --help behavior).

# Structural contract and coverage guard — seconds, no scientific packages needed
uv run python -m pytest tests/_meta -q

# One skill's suite
uv run --with pytest python -m pytest tests/<skill-name> -q

# Every suite, each in its own throwaway environment
uv run python tests/run_all.py --isolated

The Skill Tests workflow runs the contract plus the standard-library-only suites on every pull request; the full --isolated sweep builds ~100 environments and is run locally or on a schedule.

All skills in this repository are security-scanned using Cisco AI Defense Skill Scanner, an open-source tool that detects prompt injection, data exfiltration, and malicious code patterns in Agent Skills.

If you are contributing a new skill, we recommend running the scanner locally before submitting a pull request:

uv pip install cisco-ai-skill-scanner
skill-scanner scan /path/to/your/skill --use-behavioral

Note: A clean scan result reduces noise in review, but does not guarantee a skill is free of all risk. Contributed skills are also reviewed manually before merging.

Contributors are recognized in our community and may be featured in:

  • Repository contributors list
  • Special mentions in release notes
  • K-Dense community highlights

Your contributions help make scientific computing more accessible and enable researchers to leverage AI tools more effectively!

This project builds on 50+ amazing open source projects. If you find value in these skills, please consider supporting the projects we depend on.

Problem: Skills not loading

  • Verify skill folders are in the correct directory (see Getting Started)
  • Each skill folder must contain a SKILL.md file
  • Restart your agent/IDE after copying skills
  • In Cursor, check Settings → Rules to confirm skills are discovered

Problem: Missing Python dependencies

  • Solution: Check the specific SKILL.md file for required packages
  • Install dependencies: uv pip install package-name

Problem: API rate limits

  • Solution: Many databases have rate limits. Review the specific database documentation
  • Consider implementing caching or batch requests

Problem: Authentication errors

  • Solution: Some services require API keys. Check the SKILL.md for authentication setup
  • Verify your credentials and permissions

Problem: Outdated examples

  • Solution: Report the issue via GitHub Issues
  • Check the official package documentation for updated syntax

Problem:gh skill install or docs link to scientific-skills/ fails (v2.43.0+)

  • As of v2.43.0, skills live under skills/ (not scientific-skills/) to match the Agent Skills layout expected by GitHub CLI
  • Update manual copy paths, bookmarks, and citations from scientific-skills/<name> to skills/<name>
  • Re-run gh skill install K-Dense-AI/scientific-agent-skills after pulling the latest release

Q: Is this free to use?
A: Yes! This repository is MIT licensed. However, each individual skill has its own license specified in the license metadata field within its SKILL.md file—be sure to review and comply with those terms.

Q: Why are all skills grouped together instead of separate packages?
A: We believe good science in the age of AI is inherently interdisciplinary. Bundling all skills together makes it trivial for you (and your agent) to bridge across fields—e.g., combining genomics, cheminformatics, clinical data, and machine learning in one workflow—without worrying about which individual skills to install or wire together.

Q: Can I use this for commercial projects?
A: The repository itself is MIT licensed, which allows commercial use. However, individual skills may have different licenses—check the license field in each skill's SKILL.md file to ensure compliance with your intended use.

Q: Do all skills have the same license?
A: No. Each skill has its own license specified in the license metadata field within its SKILL.md file. These licenses may differ from the repository's MIT License. Users are responsible for reviewing and adhering to the license terms of each individual skill they use.

Q: How often is this updated?
A: We regularly update skills to reflect the latest versions of packages and APIs. Major updates are announced in release notes.

Q: Can I use this with other AI models?
A: The core SKILL.md format follows the open Agent Skills standard. Installation paths, discovery, and optional metadata support vary by host and version, so confirm your target host's current documentation.

Q: Do I need all the Python packages installed?
A: No! Only install the packages you need. Each skill specifies its requirements in its SKILL.md file.

Q: What if a skill doesn't work?
A: First check the Troubleshooting section. If the issue persists, file an issue on GitHub with detailed reproduction steps.

Q: Do the skills work offline?
A: Database skills require internet access to query APIs. Package skills work offline once Python dependencies are installed.

Q: Can I contribute my own skills?
A: Absolutely! We welcome contributions. See the Contributing section for guidelines and best practices.

Q: How do I report bugs or suggest features?
A: Open an issue on GitHub with a clear description. For bugs, include reproduction steps and expected vs actual behavior.

Need help? Here's how to get support:

  • 📖 Documentation : Check the relevant SKILL.md and references/ folders
  • 🐛 Bug Reports : Open an issue
  • 💡 Feature Requests : Submit a feature request
  • 📣 Updates and demos : Follow X, LinkedIn, YouTube, and Reddit to keep up with new skills, tutorials, and Scientific Agent Skills releases
  • 💼 Enterprise Support : Contact K-Dense for commercial support

If you use Scientific Agent Skills in your research or project, please cite the overall collection and, when relevant, the individual skill or skills that materially supported your work.

The collection citation helps others find the repository, understand the broader skill ecosystem used in your workflow, and credit the maintenance effort behind Scientific Agent Skills. Individual skill citations give more precise credit for the specific package, database, or workflow guidance your agent used.

Recommended practice:

  • Always cite Scientific Agent Skills using one of the formats below.

  • Also cite each individual skill that directly contributed to your analysis, code, figures, reports, or research workflow.

  • If a skill wraps or documents an external package, database, or platform, cite that upstream project too when your field's norms require it.

    @software{scientific_agent_skills_2026, author = {{K-Dense Inc.}}, title = {Scientific Agent Skills: A Comprehensive Collection of Scientific Tools for AI Agents}, year = {2026}, url = {https://github.com/K-Dense-AI/scientific-agent-skills}, note = {161 skills covering databases, packages, integrations, and analysis tools} }

    K-Dense Inc. (2026). Scientific Agent Skills: A comprehensive collection of scientific tools for AI agents [Computer software]. https://github.com/K-Dense-AI/scientific-agent-skills

    K-Dense Inc. Scientific Agent Skills: A Comprehensive Collection of Scientific Tools for AI Agents. 2026, github.com/K-Dense-AI/scientific-agent-skills.

    Scientific Agent Skills by K-Dense Inc. (2026) Available at: https://github.com/K-Dense-AI/scientific-agent-skills

Individual Skill Citation

When citing a specific skill, include the skill name, version from metadata.version in that skill's SKILL.md, and the direct skill URL. For example:

@software{scientific_agent_skills_astropy_2026,
  author = {{K-Dense Inc.}},
  title = {Astropy Skill for Scientific Agent Skills},
  year = {2026},
  url = {https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/astropy},
  note = {Version 1.0, part of Scientific Agent Skills}
}

Plain text format:

Astropy skill for Scientific Agent Skills, version 1.0.
K-Dense Inc. (2026).
https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/astropy

We appreciate acknowledgment in publications, presentations, or projects that benefit from these skills.

This project is licensed under the MIT License.

Copyright © 2026 K-Dense Inc. (k-dense.ai)

  • ✅ Free for any use (commercial and noncommercial)
  • ✅ Open source - modify, distribute, and use freely
  • ✅ Permissive - minimal restrictions on reuse
  • ⚠️ No warranty - provided "as is" without warranty of any kind

See LICENSE.md for full terms.

Individual Skill Licenses

⚠️ Important : Each skill has its own license specified in the license metadata field within its SKILL.md file. These licenses may differ from the repository's MIT License and may include additional terms or restrictions. Users are responsible for reviewing and adhering to the license terms of each individual skill they use.

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