Unlock the Power of Claude as Your AI Research Assistant: A Complete Guide to 138 Scientific Skills

Summary: What Are Claude Scientific Skills?

Claude Scientific Skills is an open-source collection of 138 ready-to-use skills developed by the K-Dense team, transforming Claude AI into a versatile research assistant for complex scientific workflows in biology, chemistry, medicine, and more. It integrates 28+ databases like PubMed and ChEMBL, 55+ Python packages such as RDKit and Scanpy, enabling tasks from drug discovery to single-cell RNA-seq analysis with seamless API access and code examples.

Have you ever felt overwhelmed by the sheer volume of tools and databases needed for scientific research? As a recent graduate diving into bioinformatics or drug discovery, you might spend hours just setting up APIs or learning new Python libraries. That’s where Claude Scientific Skills comes in—it’s like having a seasoned lab partner who handles the technical heavy lifting. Built by the K-Dense team, this collection turns Claude into an AI-powered scientist on your desktop, capable of multi-step workflows across disciplines.

In this guide, we’ll break down everything you need to know: from installation steps to real-world examples, use cases, and troubleshooting. Whether you’re analyzing genomic data or optimizing drug leads, these skills can streamline your work and boost productivity. Let’s explore how to make Claude your go-to research tool.

Why Claude Scientific Skills Stand Out for Researchers

What makes Claude Scientific Skills a game-changer for scientific computing? For starters, it accelerates research by eliminating setup hassles. Instead of digging through API docs or configuring integrations, you get production-ready code that’s tested and aligned with best practices. This means you can jump straight into complex pipelines, saving days of prep work.

The collection boasts comprehensive coverage: 138 skills spanning bioinformatics, cheminformatics, clinical research, and beyond. Key highlights include direct access to 28+ databases like OpenAlex for literature, PubMed for publications, bioRxiv for preprints, ChEMBL for chemical data, UniProt for proteins, COSMIC for cancer mutations, and ClinicalTrials.gov for trials. Pair that with 55+ Python packages—think RDKit for molecular analysis, Scanpy for single-cell studies, PyTorch Lightning for machine learning, and scikit-learn for data modeling.

Integration is effortless too. With 15+ tools like Benchling for lab data management, DNAnexus for genomics platforms, LatchBio for workflows, OMERO for microscopy, and Protocols.io for protocols, you can connect your digital and physical lab seamlessly. Add in 30+ analysis and communication aids for literature reviews, peer reviews, scientific writing, document processing, posters, slides, schematics, and citation management—plus 10+ research tools for hypothesis generation, grant writing, clinical decision support, treatment planning, and compliance.

One of the best parts? It’s designed for interdisciplinary work. Skills in multi-omics and systems biology integrate data from RNA-seq, proteomics, and metabolomics, while protein engineering tools use models like ESM for sequence design. From my own experience using similar AI tools in research, this level of bundling prevents the frustration of juggling multiple plugins. It’s maintained by the K-Dense team with community input, and enterprise support is available via K-Dense.ai.

If you’re looking to enhance Claude’s capabilities, this is your toolkit for turning ideas into insights faster.

Step-by-Step Guide: Getting Started with Claude Scientific Skills

Ready to install? The process is straightforward, whether you’re on Claude Code, Cursor IDE, or another platform. We’ll cover prerequisites first to ensure smooth setup.

Essential Prerequisites for Installation

Before diving in, check these requirements:

  • Python Version: Use 3.9 or higher; 3.12 is recommended for optimal compatibility.

  • uv Package Manager: This handles dependencies efficiently. Install it like this:

    • On macOS/Linux: Run curl -LsSf https://astral.sh/uv/install.sh | sh.
    • On Windows: Use PowerShell with powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex".
    • Alternative via pip: pip install uv.
    • Verify: uv --version.

  • Client Software: Claude Code, Cursor, or any MCP-compatible client (e.g., ChatGPT, OpenAI Agent SDK).

  • Operating System: macOS, Linux, or Windows with WSL2.

  • Dependencies: Managed per skill—review each SKILL.md for specifics.

With these in place, you’re set.

Installing on Claude Code (Recommended for Beginners)

Claude Code offers the smoothest experience. If you’re new, start with their quickstart guide.

  1. Install Claude Code:

    • macOS: curl -fsSL https://claude.ai/install.sh | bash.
    • Windows: irm https://claude.ai/install.ps1 | iex.

  2. Register the Marketplace:

    • Run /plugin marketplace add K-Dense-AI/claude-scientific-skills.

  3. Install the Skills:

    • Open Claude Code.
    • Navigate to “Browse and install plugins.”
    • Select “claude-scientific-skills.”
    • Choose “scientific-skills.”
    • Hit “Install now.”

Claude will auto-detect and apply skills based on your prompts. Update regularly for new features.

Setting Up on Cursor IDE

For a one-click option:

Using Any MCP Client

  • Hosted MCP (Easiest): Connect to https://mcp.k-dense.ai/claude-scientific-skills/mcp.
  • Self-Hosted: Check the claude-skills-mcp GitHub repo for deployment instructions.

These methods ensure Claude uses skills automatically when you describe tasks.

Real-World Examples: How to Use Claude Scientific Skills in Action

Let’s see the skills in practice with prompt examples. Each demonstrates multi-step workflows, showing used skills for clarity.

Drug Discovery Workflow Example

Objective: Identify novel EGFR inhibitors for lung cancer.

Sample Prompt:
“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 Involved: ChEMBL, RDKit, datamol, DiffDock, AlphaFold DB, PubMed, COSMIC, scientific visualization.

This generates reports with molecular insights, perfect for early-stage research.

Single-Cell RNA-Seq Analysis Example

Objective: Analyze 10X Genomics data with public integration.

Sample Prompt:
“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 Involved: Scanpy, Cellxgene Census, NCBI Gene, PyDESeq2, Arboreto, Reactome, KEGG, Open Targets.

Expect outputs like cell type clusters and target lists.

Multi-Omics Biomarker Discovery Example

Objective: Predict patient outcomes from integrated data.

Sample Prompt:
“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 Involved: PyDESeq2, pyOpenMS, HMDB, Metabolomics Workbench, UniProt, KEGG, STRING, statsmodels, scikit-learn, ClinicalTrials.gov.

Ideal for biomarker studies.

Virtual Screening Campaign Example

Objective: Find allosteric modulators for protein interactions.

Sample Prompt:
“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 Involved: AlphaFold DB, BioPython, ZINC, RDKit, DiffDock, DeepChem, PubChem, USPTO, MedChem, molfeat.

Great for computational chemistry.

Clinical Variant Interpretation Example

Objective: Assess hereditary cancer risk from VCF files.

Sample Prompt:
“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 ReportLab, and find matching trials on ClinicalTrials.gov.”

Skills Involved: pysam, Ensembl, ClinVar, COSMIC, NCBI Gene, UniProt, PubMed, ClinPGx, ReportLab, ClinicalTrials.gov.

Produces actionable clinical reports.

Systems Biology Network Analysis Example

Objective: Study gene regulatory networks from RNA-seq.

Sample Prompt:
“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 Involved: NCBI Gene, UniProt, STRING, Reactome, KEGG, Torch Geometric, Arboreto, Open Targets, PyMC, GEO.

Supports network biology research.

These prompts show how to leverage skills for end-to-end tasks.

Practical Use Cases: Applying Skills Across Scientific Domains

Claude Scientific Skills excel in real scenarios. Here’s a breakdown by field.

Drug Discovery and Medicinal Chemistry Use Cases

  • Virtual screening from PubChem/ZINC against targets.
  • Lead optimization with RDKit SAR analysis and datamol analogs.
  • ADMET predictions using DeepChem.
  • Molecular docking via DiffDock.
  • Bioactivity data from ChEMBL for SAR patterns.

Bioinformatics and Genomics Use Cases

  • Sequence processing with BioPython and pysam.
  • Single-cell workflows in Scanpy, including GRN inference with Arboreto.
  • Variant annotation using Ensembl VEP and ClinVar.
  • Gene queries across NCBI Gene, UniProt, Ensembl.
  • PPI networks via STRING, pathway mapping to KEGG/Reactome.

Clinical Research and Precision Medicine Use Cases

  • Trial searches on ClinicalTrials.gov with criteria analysis.
  • Variant interpretation with ClinVar, COSMIC, ClinPGx.
  • Drug safety checks from FDA databases.
  • Patient matching to therapies and trials.

Multi-Omics and Systems Biology Use Cases

  • Integrating omics data types.
  • Pathway enrichment in KEGG/Reactome.
  • GRN reconstruction and hub gene identification.
  • Outcome prediction from multi-layer data.

Data Analysis and Visualization Use Cases

  • Statistical tests, power analysis.
  • High-quality figures with matplotlib/seaborn.
  • Biological network visuals in NetworkX.
  • PDF reports via ReportLab.

Laboratory Automation Use Cases

  • Opentrons protocols for liquid handling.
  • Integration with Benchling/LabArchives for LIMS.
  • Automated lab workflows.

These applications make research more efficient.

Full List of Available Skills: Categorized for Easy Reference

Here’s the complete inventory, grouped by category. Each skill includes docs, examples, and best practices in its SKILL.md.

Bioinformatics & Genomics Skills (16+)

Category Skills
Sequence Analysis BioPython, pysam, scikit-bio, BioServices
Single-Cell Analysis Scanpy, AnnData, scvi-tools, Arboreto, Cellxgene Census
Genomic Tools gget, geniml, gtars, deepTools, FlowIO, Zarr
Phylogenetics ETE Toolkit

Cheminformatics & Drug Discovery Skills (10+)

Category Skills
Molecular Manipulation RDKit, Datamol, Molfeat
Deep Learning DeepChem, TorchDrug
Docking & Screening DiffDock
Drug-Likeness MedChem
Benchmarks PyTDC

Proteomics & Mass Spectrometry Skills (2)

  • matchms, pyOpenMS.

Clinical Research & Precision Medicine Skills (12+)

  • Databases: ClinicalTrials.gov, ClinVar, ClinPGx, COSMIC, FDA Databases.
  • AI Tools: PyHealth, NeuroKit2, Clinical Decision Support.
  • Documentation: Clinical Reports, Treatment Plans.
  • Variant Analysis: Ensembl, NCBI Gene.

Medical Imaging & Digital Pathology Skills (3)

  • pydicom, histolab, PathML.

Neuroscience & Electrophysiology Skills (1)

  • Neuropixels-Analysis.

Machine Learning & AI Skills (15+)

Category Skills
Deep Learning PyTorch Lightning, Transformers, Stable Baselines3, PufferLib
Classical ML scikit-learn, scikit-survival, SHAP
Time Series aeon
Bayesian PyMC
Optimization PyMOO
Graph ML Torch Geometric
Dimensionality Reduction UMAP-learn
Statistical Modeling statsmodels

Materials Science, Chemistry & Physics Skills (7)

  • Pymatgen, COBRApy, Astropy, Cirq, PennyLane, Qiskit, QuTiP.

Engineering & Simulation Skills (3)

  • FluidSim, SimPy, Dask, Polars, Vaex.

Data Analysis & Visualization Skills (14+)

  • Matplotlib, Seaborn, Plotly, Scientific Visualization, GeoPandas, NetworkX, SymPy, ReportLab, Data Commons, EDA workflows, Statistical Analysis workflows.

Laboratory Automation Skills (3)

  • PyLabRobot, Protocols.io, Benchling, LabArchives.

Multi-Omics & Systems Biology Skills (5+)

  • KEGG, Reactome, STRING, BIOMNI, Denario, HypoGeniC, LaminDB.

Protein Engineering & Design Skills (2)

  • ESM, Adaptyv.

Scientific Communication Skills (20+)

  • OpenAlex, PubMed, bioRxiv, Literature Review, Perplexity Search, Scientific Writing, Peer Review, XLSX, MarkItDown, Document Skills, Paper-2-Web, Venue Templates, Scientific Slides, LaTeX Posters, PPTX Posters, Scientific Schematics, Citation Management, Generate Image.

Scientific Databases Skills (28+)

Category Skills
Protein UniProt, PDB, AlphaFold DB
Chemical PubChem, ChEMBL, DrugBank, ZINC, HMDB
Genomic Ensembl, NCBI Gene, GEO, ENA, GWAS Catalog
Literature bioRxiv
Clinical ClinVar, COSMIC, ClinicalTrials.gov, ClinPGx, FDA Databases
Pathways KEGG, Reactome, STRING
Targets Open Targets
Metabolomics Metabolomics Workbench
Enzymes BRENDA
Patents USPTO

Infrastructure & Platforms Skills (6+)

  • Modal, DNAnexus, LatchBio, OMERO, Opentrons, ToolUniverse, Get Available Resources.

Research Methodology & Planning Skills (8+)

  • Scientific Brainstorming, Hypothesis Generation, Scientific Critical Thinking, Scholar Evaluation, Research Grants, Research Lookup, Market Research Reports.

Regulatory & Standards Skills (1)

  • ISO 13485 Certification.

Explore docs/scientific-skills.md for details and examples.md for workflows.

Contributing to Claude Scientific Skills: How You Can Help

Want to expand the collection? Contributions are welcome!

Contribution Ideas

  • Add skills for new packages/databases.
  • Enhance docs with examples/use cases.
  • Fix bugs or update info.

Submission Process

  1. Fork the repo.
  2. Create branch: git checkout -b feature/new-skill.
  3. Follow structure/docs patterns.
  4. Include SKILL.md.
  5. Test thoroughly.
  6. Commit: git commit -m 'Add new skill'.
  7. Push: git push origin feature/new-skill.
  8. Pull request with description.

Guidelines: Consistent formatting, working examples, best practices, references.

Contributors get recognized in lists and notes.

Troubleshooting Common Issues with Claude Scientific Skills

  • Skills Not Loading: Update Claude Code; reinstall plugin.
  • Missing Dependencies: Check SKILL.md; install via uv pip install.
  • API Limits: Batch requests; cache data.
  • Auth Errors: Set API keys per SKILL.md.
  • Outdated Examples: Report on GitHub; check package docs.

File issues with steps for help.

Frequently Asked Questions About Claude Scientific Skills

Is Claude Scientific Skills Free?

Yes, MIT-licensed repository. Individual skills have their own licenses in SKILL.md—review them.

Why Bundle All Skills in One Plugin?

It supports interdisciplinary science, allowing seamless cross-field workflows without multiple installs.

Can I Use It Commercially?

MIT allows it, but check per-skill licenses.

How Often Are Updates Released?

Regularly, with major changes in release notes.

Does It Work with Other AI Models?

Optimized for Claude, adaptable via MCP.

Do I Need All Python Packages?

No—install only what’s needed per skill.

What If a Skill Fails?

See troubleshooting; submit GitHub issue.

Can Skills Run Offline?

Package-based yes (post-install); database no.

How to Contribute Skills?

Follow guidelines; welcome additions.

Reporting Bugs?

GitHub issues with details.

Support Resources and Community Engagement

  • Docs: SKILL.md and references/.
  • Issues/Requests: GitHub.
  • Enterprise: K-Dense.ai.
  • MCP: claude-skills-mcp repo or hosted server.

Join Slack: https://join.slack.com/t/k-densecommunity/shared_invite/zt-3iajtyls1-EwmkwIZk0g_o74311Tkf5g for discussions and collaboration.

Citing Claude Scientific Skills in Your Work

Use these formats:

BibTeX

@software{claude_scientific_skills_2025,
  author = {{K-Dense Inc.}},
  title = {Claude Scientific Skills: A Comprehensive Collection of Scientific Tools for Claude AI},
  year = {2025},
  url = {https://github.com/K-Dense-AI/claude-scientific-skills},
  note = {skills covering databases, packages, integrations, and analysis tools}
}

APA

K-Dense Inc. (2025). Claude Scientific Skills: A comprehensive collection of scientific tools for Claude AI [Computer software]. https://github.com/K-Dense-AI/claude-scientific-skills

MLA

K-Dense Inc. Claude Scientific Skills: A Comprehensive Collection of Scientific Tools for Claude AI. 2025, github.com/K-Dense-AI/claude-scientific-skills.

Plain Text

Claude Scientific Skills by K-Dense Inc. (2025)
Available at: https://github.com/K-Dense-AI/claude-scientific-skills

License Information for Claude Scientific Skills

MIT License. Copyright © 2025 K-Dense Inc. (k-dense.ai).

  • Free for commercial/non-commercial use.
  • Open-source for modification/distribution.
  • Permissive with few restrictions.
  • Provided “as is” without warranty.

See LICENSE.md. Per-skill licenses in SKILL.md—adhere to them.

If this guide helps, star the repo on GitHub. It encourages ongoing development. Start integrating Claude Scientific Skills today and elevate your research!