scientific-skills

     

  

Star History


🔔 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](https://github.com/K-Dense-AI/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 147 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.

Stay up to date: Follow K-Dense on X, LinkedIn, and YouTube for new skills, release announcements, walkthroughs, research workflow demos, and examples you can use with your own AI agent.

A comprehensive collection of 147 ready-to-use scientific and research skills (covering cancer genomics, drug-target binding, molecular dynamics, RNA velocity, 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. 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 & Precision Medicine - Clinical trials, pharmacogenomics, variant interpretation, drug safety, clinical decision support, treatment planning
• •🧠 Healthcare AI & Clinical ML - EHR analysis, physiological signal processing, medical imaging, clinical prediction models
• •🖼️ Medical Imaging & Digital Pathology - DICOM processing, whole slide image analysis, computational pathology, radiology 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 - Literature review, peer review, scientific writing, document processing, posters, slides, schematics, 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 - Hypothesis generation, scientific brainstorming, critical thinking, grant writing, scholar evaluation

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 147 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, U.S. Treasury Fiscal Data, and Hugging Science (curated catalog of scientific datasets, models, and demos across 17 scientific domains on Hugging Face). Multi-database packages like BioServices (~40 bioinformatics services), BioPython (38 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage
• •70+ Optimized Python Package Skills - Explicitly defined skills for RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioPython, pyzotero, BioServices, PennyLane, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), scVelo, TimesFM, and others — with curated documentation, examples, and best practices. Note: the agent can write code using any Python package, not just these; these skills simply provide stronger, more reliable performance 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, scientific writing, peer review, document processing, Paperzilla, PACSOMATIC, Exa Search, posters, slides, schematics, infographics, Mermaid diagrams, and more
• •10+ Research & Clinical Tools - Hypothesis generation, grant writing, clinical decision support, treatment plans, BIDS, regulatory compliance, 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

📋 Table of Contents

• •What's Included
• •Why Use This?
• •Getting Started
• •Security Disclaimer
• •Support Open Source
• •Prerequisites
• •Quick Examples
• •Use Cases
• •Available Skills
• •Contributing
• •Troubleshooting
• •FAQ
• •Support
• •Citation
• •License

🚀 Why Use This?

⚡ Accelerate Your Research

• •Save Days of Work - Skip API documentation research and integration setup
• •Production-Ready Code - Tested, validated examples following scientific best practices
• •Multi-Step Workflows - Execute complex pipelines with a single prompt

🎯 Comprehensive Coverage

• •147 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 - RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioServices, PennyLane, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), scVelo, TimesFM, and others (the agent can use any Python package; these are the pre-documented, higher-performing paths)

🔧 Easy Integration

• •Simple Setup - Copy skills to your skills directory and start working
• •Automatic Discovery - Your agent automatically finds and uses relevant skills
• •Well Documented - Each skill includes examples, use cases, and best practices

🌟 Maintained & Supported

• •Regular Updates - Continuously maintained and expanded by K-Dense team
• •Community Driven - Open source with active community contributions
• •Enterprise Ready - Commercial support available for advanced needs

🎯 Getting Started

Option 1: npx (all platforms)

Install Scientific Agent Skills with a single command:

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

This is the official standard approach for installing Agent Skills across all platforms, including Claude Code, Claude Cowork, Codex, Gemini CLI, Google Antigravity, Cursor, OpenClaw, NVIDIA NemoClaw, Hermes, Pi, and any other agent that supports the open Agent Skills standard.

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 v1.0.0

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

# Check for updates interactively
gh skill update

# Update all installed skills
gh skill update --all

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

You usually don't need anything host-specific. npx skills add (Option 1) installs into the shared ~/.agents/skills/ convention, and any compliant client that scans that directory — including OpenClaw, NVIDIA NemoClaw (an OpenClaw-based secure runtime), and Pi — discovers the skills automatically. Project-scoped installs land in .agents/skills/ and work the same way. To install without the CLI, clone straight into either location:

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

Hermes is the one host that uses its own registry instead of the shared directory, so add the repo as a tap:

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

These skills stay portable across all of them: metadata is single-line JSON (so OpenClaw's line-based reader parses it), credentialed skills declare a top-level required_environment_variables field (so Hermes prompts for keys), and unknown fields are ignored everywhere else. Because 147 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! Your AI agent will automatically discover the skills and use them when relevant to your scientific tasks. You can also invoke any skill manually by mentioning the skill name in your prompt.

⚠️ Security Disclaimer

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 the `SKILL.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:

``bash 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.

All skills are scanned on an approximately weekly basis, and SECURITY.md is updated with the latest results. 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](docs/open-source-sponsors.md)

⚙️ 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.md files for specific requirements)

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 | sh

Windows:

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

Alternative (via pip):

pip install uv

After installation, verify it works by running:

uv --version

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

💡 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:

🧪 Drug Discovery Pipeline

Goal: Find novel EGFR inhibitors for lung cancer treatment

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

🏥 Clinical Variant Interpretation

Goal: Analyze VCF file for hereditary cancer risk assessment

Prompt:

Use available skills you have access to whenever possible. Parse VCF with pysam, annotate variants with Ensembl VEP, query ClinVar for pathogenicity, 
check COSMIC for cancer mutations, retrieve gene info from NCBI Gene, analyze protein impact 
with UniProt, search PubMed for case reports, check ClinPGx for pharmacogenomics, generate 
clinical report with document processing tools, and find matching trials on ClinicalTrials.gov.

Skills Used: pysam, database-lookup, paper-lookup, clinical-reports, docx, pdf

🌐 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.

🔬 Use Cases

🧪 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 VCF files with Ensembl VEP, query ClinVar for pathogenicity
• •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
• •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 & Precision Medicine

• •Clinical Trials: Search ClinicalTrials.gov for relevant studies, analyze eligibility criteria
• •Variant Interpretation: Annotate variants with ClinVar, COSMIC, and ClinPGx for pharmacogenomics
• •Drug Safety: Query FDA databases for adverse events, drug interactions, and recalls
• •Precision Therapeutics: Match patient variants to targeted therapies and clinical trials

🔬 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: Generate comprehensive reports with the PDF, DOCX, PPTX, XLSX, MarkItDown, LiteParse, and clinical-reporting skills

🧪 Laboratory Automation

• •Protocol Design: Create Opentrons protocols for automated liquid handling
• •LIMS Integration: Integrate with Benchling and LabArchives for data management
• •Workflow Automation: Automate multi-step laboratory workflows

📚 Available Skills

This repository contains 147 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.

Skill Categories

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.

• •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, geniml, gtars, deepTools, FlowIO, Polars-Bio, Zarr, TileDB-VCF
• •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)

• •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

• •Spectral processing: matchms, pyOpenMS

• •Clinical databases: via Database Lookup (ClinicalTrials.gov, ClinVar, ClinPGx, COSMIC, FDA, cBioPortal, Monarch, and more)
• •Cancer genomics: DepMap (cancer dependency scores, drug sensitivity)
• •Cancer imaging: Imaging Data Commons (NCI radiology & pathology datasets via idc-index)
• •Healthcare AI: PyHealth, NeuroKit2, Clinical Decision Support
• •Clinical documentation: Clinical Reports, Treatment Plans

• •DICOM processing: pydicom
• •Whole slide imaging: histolab, PathML

• •Data standards: BIDS (Brain Imaging Data Structure for neuroscience and biomedical datasets)
• •Neural recordings: Neuropixels-Analysis (extracellular spikes, silicon probes, spike sorting)

• •Deep learning: PyTorch Lightning, Transformers, Stable Baselines3, PufferLib
• •Classical ML: scikit-learn, scikit-survival, 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: Pymatgen
• •Metabolic modeling: COBRApy
• •Astronomy: Astropy
• •Quantum computing: Cirq, PennyLane, Qiskit, QuTiP

• •Numerical computing: MATLAB/Octave
• •Computational fluid dynamics: FluidSim
• •Discrete-event simulation: SimPy
• •Symbolic math: SymPy

• •Visualization: Matplotlib, Seaborn, Scientific Visualization
• •Geospatial analysis: GeoPandas, 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: EDA workflows
• •Statistical analysis: Statistical Analysis workflows
• •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)

• •Liquid handling: PyLabRobot and Opentrons
• •Cloud lab: Ginkgo Cloud Lab (cell-free protein expression, fluorescent pixel art via autonomous RAC infrastructure)
• •Protocol management: Protocols.io
• •LIMS integration: Benchling, LabArchives

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

• •Protein language models: ESM
• •Glycoengineering: Glycoengineering (N/O-glycosylation prediction, therapeutic antibody optimization)
• •Cloud laboratory platform: Adaptyv (automated protein testing and validation)

• •Literature: Paper Lookup (PubMed, PMC, bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall), Literature Review, Paperzilla
• •Advanced paper search: BGPT Paper Search (25+ structured fields per paper — methods, results, sample sizes, quality scores — from full text, not just abstracts)
• •Web search: Parallel Web, 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: Scientific Writing, Peer Review
• •Document processing: LiteParse, PDF, DOCX, PPTX, XLSX, and MarkItDown
• •Publishing and paper workflows: Venue Templates, PACSOMATIC
• •Presentations: Scientific Slides, LaTeX Posters, PPTX Posters
• •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 Pro (Nano Banana Pro))

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 (38 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)
• •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)

• •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
• •Microscopy: OMERO
• •Automation: Opentrons
• •Resource detection: Get Available Resources
• •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)

• •Ideation: Scientific Brainstorming, Hypothesis Generation
• •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, Scholar Evaluation
• •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: Market Research Reports

• •Medical device standards: ISO 13485 Certification

📖 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.

🤝 Contributing

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.

Ways to Contribute

✨ 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

How to Contribute

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
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

Contribution Guidelines

✅ Adhere to the [Agent Skills Specification](https://agentskills.io/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

Security Scanning

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.

Recognition

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!

Support Open Source

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

🔧 Troubleshooting

Common Issues

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

❓ FAQ

General Questions

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 skills follow the open Agent Skills standard and work with any compatible agent, including Cursor, Claude Code, Codex, Google Antigravity, OpenClaw, NVIDIA NemoClaw, Hermes, and Pi.

Installation & Setup

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.

Contributing

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.

💬 Support

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, and YouTube to keep up with new skills, tutorials, and Scientific Agent Skills releases
• •💼 Enterprise Support: Contact K-Dense for commercial support

📖 Citation

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.

Collection Citation

@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 = {147 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.

📄 License

This project is licensed under the MIT License.

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

Key Points:

• •✅ 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.