科学智能代理
AI Skill Hub 强烈推荐：科学智能代理 是一款优质的Claude技能。在 GitHub 上收获超过 26.2k 颗 Star，AI 综合评分 8.0 分，在同类工具中表现稳健。如果你正在寻找可靠的Claude技能解决方案，这是一个值得深入了解的选择。
📚 深度解析
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
科学智能代理 依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 科学智能代理 的版本更新，及时通知重要功能变化。
📋 工具概览
将任何AI代理转化为AI科学家，开源Claude技能
科学智能代理 是一款基于 Python 开发的开源工具，专注于 claude_skill、agent-skills、ai-scientist 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
📖 中文文档
将任何AI代理转化为AI科学家，开源Claude技能
科学智能代理 是一款基于 Python 开发的开源工具，专注于 claude_skill、agent-skills、ai-scientist 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
- 专为 Claude 系列模型深度优化的扩展技能
- 通过 Claude Code CLI 一键安装，配置零门槛
- 充分利用 Claude 的长上下文和推理能力
- 支持与 MCP 工具组合使用，扩展能力边界
- 在 Claude 中快速解决特定专业领域的问题
- 复杂任务的 AI 辅助分析、推理和报告生成
- 构建个人专属的 AI 技能工具箱
# 方式一：pip 安装（推荐）
pip install scientific-agent-skills
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install scientific-agent-skills
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/K-Dense-AI/scientific-agent-skills
cd scientific-agent-skills
pip install -e .
# 验证安装
python -c "import scientific_agent_skills; print('安装成功')"
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 命令行使用
scientific-agent-skills --help
# 基本用法
scientific-agent-skills input_file -o output_file
# Python 代码中调用
import scientific_agent_skills
# 示例
result = scientific_agent_skills.process("input")
print(result)
# scientific-agent-skills 配置文件示例（config.yml） app: name: "scientific-agent-skills" debug: false log_level: "INFO" # 运行时指定配置文件 scientific-agent-skills --config config.yml # 或通过环境变量配置 export SCIENTIFIC_AGENT_SKILLS_API_KEY="your-key" export SCIENTIFIC_AGENT_SKILLS_OUTPUT_DIR="./output"
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.
📄 Paper: Scientific Agent Skills is described in Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents (arXiv:2609.00065). If you use these skills in your research, please cite the paper.
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.
📦 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
---
⚙️ Prerequisites
- 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.mdfiles for specific requirements)
🎯 Getting Started
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
Update all installed skills
gh skill update --all ```
Installing uv
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 | shWindows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Alternative (via pip):
pip install uvAfter installation, verify it works by running:
uv --versionFor more installation options and details, visit the official uv documentation.
---
Security and safe deployment
- Security in the Science Agent Era: What Every Lab Needs to Know Before Installing Skills — The practical review checklist behind this repo's Security Disclaimer: read the full
SKILL.mdandscripts/, scan before installing, and pin versions instead of tracking a branch. - The Sandboxed AI Scientist: Pairing NVIDIA OpenShell with Scientific Agent Skills — Running these skills inside a policy-governed sandbox; see also the NemoClaw note in Getting Started.
🎥 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](https://youtu.be/lVZbHiwzMEg) | Writing, testing, and packaging a new skill from scratch |
| [Literature Review and Hypothesis Generation](https://youtu.be/wKJp8y4ZyiM) | Searching the literature and generating grounded hypotheses |
| [Draft and Budget an Experimental Protocol](https://youtu.be/Yz2L5s_M_34) | Turning a planned experiment into a costed, written protocol |
| [Draft Responses to Reviewer Comments](https://youtu.be/0MmU-Pmtg1o) | Building a point-by-point rebuttal from reviewer feedback |
| [Can AI Reproduce a Nature Medicine Paper?](https://youtu.be/4WTCK9kSfdk) | An end-to-end reproduction attempt on a published analysis |
---
💡 Quick Examples
Once you've installed the skills, you can ask your AI agent to execute complex multi-step scientific workflows. Here are some example prompts:
🔬 Use Cases
Option 1: npx (supported hosts)
Install Scientific Agent Skills with a single command:
npx skills add K-Dense-AI/scientific-agent-skillsThis 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:
```bash
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-skillsRestart 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):
codex plugins install .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.
🔧 **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
🧪 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
---
🏥 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
Why the workflow layer matters
- The Model Is No Longer the Bottleneck — The case for why a repository like this one exists: frontier models now match specialized scientific software on raw capability (±0.079 ppm on NMR hydrogen shift prediction), so the limiting factor has moved to the workflow around the model — data access, code execution, verification, and auditable output.
- The AI Co-Scientist Is Here. The Bottleneck Is Verification. — A 10-point checklist for evaluating a research agent, built around exposing sources, code, data provenance, and intermediate work rather than a polished final answer — the same reasoning behind the provenance and retrieval-contract requirements in skills like database-lookup and scientific-writing.
- Reproduction, Not Generation, Is AI's Killer App for Science — Why re-running published analyses is the highest-value use of an agent: 78% of papers and 93% of individual analysis tasks reproduced across a 221-study benchmark, because a reproduction can be checked against known numbers while a generated claim cannot.
- Introducing K-Bench 01: Nine Frontier Models, 178 Real Scientific Tasks, and a Lot of Confident Wrong Answers — Nine frontier models on 178 real user tasks, with overclaiming in 40% of runs. Useful calibration for what to check when an agent reports success, and context for the verification boundaries written into the clinical, regulatory, and research-methodology skills above.
🔬 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
---
scientific-agent-skills 是一个专为科研场景设计的 AI Agent 能力库。该项目集成了丰富的科学研究技能，旨在通过标准化的 Agent Skills 协议，为 AI 智能体提供强大的科研辅助能力，使其能够理解并执行复杂的科学任务。
本项目包含 140 项科学与研究技能，涵盖了 100 多个科学及金融数据库。通过统一的数据库查询技能，用户可以直接访问 PubChem、ChEMBL、UniProt、COSMIC、ClinicalTrials.gov 等 78 个公共数据库，并针对 DepMap、Imaging Data Commons、PrimeKG 及美国财政部数据（U.S. Treasury Fiscal Data）提供了专门的技能支持。
在使用本项目前，请确保您的环境满足以下要求：Python 版本需为 3.13+（用于仓库工具链，单个技能的依赖可能支持更广泛的版本）；必须安装 uv 作为 Python 包管理器，用于安装技能所需的依赖；此外，您需要使用任何支持 Agent Skills 协议的 Client（如 Claude 等）来驱动这些技能。
您可以通过多种方式安装技能。推荐使用官方标准方式，通过 npx 命令进行全平台安装，支持 Claude Code、Cursor、Gemini CLI 等主流 Agent；如果您已安装 GitHub CLI (v2.90.0+)，可以使用 `gh skill install` 命令进行交互式安装或直接安装特定的技能（如 scanpy）。
安装完成后，您可以直接向您的 AI Agent 发送复杂的科研工作流指令。例如，您可以要求 Agent 利用已安装的技能进行单细胞 RNA-seq 数据分析，或者构建药物发现流水线（Drug Discovery Pipeline），通过查询 ChEMBL 数据库、结合 RDKit 进行结构分析并进行虚拟筛选，实现端到端的自动化科研流程。
本项目提供了灵活的配置与安装方案。官方推荐使用 `npx skills add K-Dense-AI/scientific-agent-skills` 命令，这是在所有平台（包括 Claude Code、Claude Cowork、Codex、Gemini CLI、Google Antigravity 及 Cursor）上安装 Agent Skills 的标准做法。此外，开发者也可以通过 GitHub CLI 的 `gh skill` 扩展进行高效管理。
本项目设计了极简的集成流程：只需将技能文件复制到您的 skills 目录即可快速启动；具备自动发现机制，您的 Agent 会自动识别并调用相关的技能；同时，每个技能都附带详尽的文档、使用案例与最佳实践，确保科研工作流的顺畅运行。
本项目提供了针对常见科研场景的 FAQ 与故障排除指南。例如，在处理 Single-Cell RNA-seq 分析时，用户可以利用 Scanpy 加载 10X 数据集，并结合 Cellxgene Census 数据进行质量控制（QC）与细胞类型鉴定。通过明确的 Prompt 指引，Agent 可以高效调用相关技能完成复杂任务。
高质量的AI科学家技能库，值得关注
- 构建多智能体协作系统的 Agent 开发者
- 需要从图片、PDF 提取文字的文档自动化场景
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- Python 依赖冲突：建议用 venv / uv 隔离环境
- CLI：直接 npm install -g / pip install，命令行调用
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 专为 Claude 系列模型深度优化的扩展技能
- 通过 Claude Code CLI 一键安装，配置零门槛
- 充分利用 Claude 的长上下文和推理能力
- 支持与 MCP 工具组合使用，扩展能力边界
- 构建多智能体协作系统的 Agent 开发者
- 需要从图片、PDF 提取文字的文档自动化场景
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- Python 依赖冲突：建议用 venv / uv 隔离环境
👥 适合人群
🎯 使用场景
- 在 Claude 中快速解决特定专业领域的问题
- 复杂任务的 AI 辅助分析、推理和报告生成
- 构建个人专属的 AI 技能工具箱
⚖️ 优点与不足
- +GitHub 26.2k Star，社区高度认可
- +MIT 协议，可免费商用
- +AI Skill Hub 精选推荐
- +深度优化 Claude 使用体验
- +CLI 一键安装，极度便捷
- −仅限 Claude 用户使用，受平台限制
- −功能边界受当前 Claude 模型能力约束
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一，可自由商用、修改、分发，仅需保留版权声明。
🔗 相关工具推荐
❓ 常见问题 FAQ
总体来看，科学智能代理 是一款质量优秀的Claude技能，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | scientific-agent-skills |
| 原始描述 | 开源Claude技能：Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science,。⭐26.2k · Python |
| Topics | claude_skillagent-skillsai-scientistbioinformaticschemoinformatics |
| GitHub | https://github.com/K-Dense-AI/scientific-agent-skills |
| License | MIT |
| 语言 | Python |
收录时间：2026-05-27 · 更新时间：2026-05-30 · License：MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。