---

name: scientific-skills description: Comprehensive scientific research toolkit with 139 specialized skills for biology, chemistry, medicine, data science, and computational research. Transforms Claude into an AI research assistant with access to scientific databases, analysis tools, and domain-specific workflows. license: MIT version: 2.17.0 metadata: skill-author: K-Dense Inc. original-repo: https://github.com/K-Dense-AI/claude-scientific-skills categories: - scientific-databases - bioinformatics - cheminformatics - machine-learning - data-analysis - scientific-writing skill-count: 139

Claude Scientific Skills

Overview

A comprehensive collection of 139 ready-to-use scientific skills that transform Claude into an AI research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and related fields.

When to Use

Invoke this skill when:

• Working on scientific research tasks
• Need access to specialized databases (PubMed, ChEMBL, UniProt, etc.)
• Performing bioinformatics or cheminformatics analysis
• Creating literature reviews or scientific documents
• Analyzing single-cell RNA-seq, proteomics, or multi-omics data
• Drug discovery and molecular analysis workflows
• Statistical analysis and machine learning on scientific data

Quick Start

// Invoke the main skill catalog
Skill({ skill: 'scientific-skills' });

// Or invoke specific sub-skills directly
Skill({ skill: 'scientific-skills/rdkit' }); // Cheminformatics
Skill({ skill: 'scientific-skills/scanpy' }); // Single-cell analysis
Skill({ skill: 'scientific-skills/biopython' }); // Bioinformatics
Skill({ skill: 'scientific-skills/literature-review' }); // Literature review

Skill Categories

Scientific Databases (28+)

Skill	Description
pubchem	Chemical compound database
chembl-database	Bioactivity database for drug discovery
uniprot-database	Protein sequence and function database
pdb	Protein Data Bank structures
drugbank-database	Drug and drug target information
kegg	Pathway and genome database
clinvar-database	Clinical variant interpretations
cosmic-database	Cancer mutation database
ensembl-database	Genome browser and annotations
geo-database	Gene expression data
gwas-database	Genome-wide association studies
reactome-database	Biological pathways
string-database	Protein-protein interactions
alphafold-database	Protein structure predictions
biorxiv-database	Preprint server for biology
clinicaltrials-database	Clinical trial registry
ena-database	European Nucleotide Archive
fda-database	FDA drug approvals and labels
gene-database	Gene information from NCBI
zinc-database	Commercially available compounds
brenda-database	Enzyme database
clinpgx-database	Pharmacogenomics annotations
uspto-database	Patent database

Python Analysis Libraries (55+)

Skill	Description
rdkit	Cheminformatics toolkit
scanpy	Single-cell RNA-seq analysis
anndata	Annotated data matrices
biopython	Computational biology tools
pytorch-lightning	Deep learning framework
scikit-learn	Machine learning library
transformers	NLP and deep learning models
pandas / polars / vaex	Data manipulation
matplotlib / seaborn / plotly	Visualization
deepchem	Deep learning for chemistry
esm	Evolutionary Scale Modeling
datamol	Molecular data processing
pymatgen	Materials science
qiskit	Quantum computing
pymoo	Multi-objective optimization
statsmodels	Statistical modeling
sympy	Symbolic mathematics
networkx	Network analysis
geopandas	Geospatial analysis
shap	Model explainability

Bioinformatics & Genomics

Skill	Description
gget	Gene and transcript information
pysam	SAM/BAM file manipulation
deeptools	NGS data analysis
pydeseq2	Differential expression
scvi-tools	Deep learning for single-cell
etetoolkit	Phylogenetic analysis
scikit-bio	Bioinformatics algorithms
bioservices	Web services for biology
cellxgene-census	Cell atlas exploration

Cheminformatics & Drug Discovery

Skill	Description
rdkit	Molecular manipulation
datamol	Molecular data handling
molfeat	Molecular featurization
diffdock	Molecular docking
torchdrug	Drug discovery ML
pytdc	Therapeutics data commons
cobrapy	Metabolic modeling

Scientific Communication

Skill	Description
literature-review	Systematic literature reviews
scientific-writing	Academic writing assistance
scientific-schematics	AI-generated figures
scientific-slides	Presentation generation
hypothesis-generation	Hypothesis development
venue-templates	Journal-specific formatting
citation-management	Reference management

Clinical & Medical

Skill	Description
clinical-decision-support	Clinical reasoning
clinical-reports	Medical report generation
treatment-plans	Treatment planning
pyhealth	Healthcare ML
pydicom	Medical imaging

Laboratory & Integration

Skill	Description
benchling-integration	Lab informatics platform
dnanexus-integration	Genomics cloud platform
pylabrobot	Laboratory automation
flowio	Flow cytometry data
omero-integration	Bioimaging platform

Core Workflows

Literature Review Workflow

# 7-phase systematic literature review
# 1. Planning with PICO framework
# 2. Multi-database search execution
# 3. Screening with PRISMA flow
# 4. Data extraction and quality assessment
# 5. Thematic synthesis
# 6. Citation verification
# 7. PDF generation

Drug Discovery Workflow

# Using RDKit + ChEMBL + datamol
from rdkit import Chem
from rdkit.Chem import Descriptors, AllChem

# 1. Query ChEMBL for bioactivity data
# 2. Calculate molecular properties
# 3. Filter by drug-likeness (Lipinski)
# 4. Similarity screening
# 5. Substructure analysis

Single-Cell Analysis Workflow

# Using scanpy + anndata
import scanpy as sc

# 1. Load and QC data
# 2. Normalization and feature selection
# 3. Dimensionality reduction (PCA, UMAP)
# 4. Clustering (Leiden algorithm)
# 5. Marker gene identification
# 6. Cell type annotation

Hypothesis Generation Workflow

# 8-step systematic process
# 1. Understand phenomenon
# 2. Literature search
# 3. Synthesize evidence
# 4. Generate competing hypotheses
# 5. Evaluate quality
# 6. Design experiments
# 7. Formulate predictions
# 8. Generate report

Sub-Skill Structure

Each sub-skill follows a consistent structure:

scientific-skills/
├── SKILL.md                    # This file (catalog/index)
├── skills/                     # Individual skill directories
│   ├── rdkit/
│   │   ├── SKILL.md           # Skill documentation
│   │   ├── references/        # API references, patterns
│   │   └── scripts/           # Example scripts
│   ├── scanpy/
│   ├── biopython/
│   └── ... (139 total)

Invoking Sub-Skills

Direct Invocation

// Invoke specific skill
Skill({ skill: 'scientific-skills/rdkit' });
Skill({ skill: 'scientific-skills/scanpy' });

Chained Workflows

// Multi-skill workflow
Skill({ skill: 'scientific-skills/literature-review' });
Skill({ skill: 'scientific-skills/hypothesis-generation' });
Skill({ skill: 'scientific-skills/scientific-schematics' });

Prerequisites

• Python 3.9+ (3.12+ recommended)
• uv package manager (recommended)
• Platform: macOS, Linux, or Windows with WSL2

Best Practices

1. Start with the right skill: Use the category tables above to find appropriate skills
2. Chain skills for complex workflows: Literature review → Hypothesis → Experiment design
3. Use database skills for data access: Query databases before analysis
4. Visualize results: Use matplotlib/seaborn/plotly skills for publication-quality figures
5. Document findings: Use scientific-writing skill for formal documentation

Integration with Agent Framework

Recommended Agent Pairings

Agent	Scientific Skills
data-engineer	polars, dask, vaex, zarr-python
python-pro	All Python-based skills
database-architect	Database skills for schema design
technical-writer	literature-review, scientific-writing

Example Agent Spawn

Task({
  subagent_type: 'python-pro',
  description: 'Analyze molecular dataset with RDKit',
  prompt: `You are the PYTHON-PRO agent with scientific research expertise.

## Task
Analyze the molecular dataset for drug-likeness properties.

## Skills to Invoke
1. Skill({ skill: "scientific-skills/rdkit" })
2. Skill({ skill: "scientific-skills/datamol" })

## Workflow
1. Load molecular data
2. Calculate descriptors
3. Apply Lipinski filters
4. Generate visualization
5. Report findings
`,
});

Resources

Bundled Documentation

• skills/*/SKILL.md - Individual skill documentation
• skills/*/references/ - API references and patterns
• skills/*/scripts/ - Example scripts and templates

External Resources

• K-Dense AI GitHub
• RDKit Documentation
• Scanpy Documentation
• BioPython Tutorial

Version History

• v2.17.0 - Current version with 139 skills
• Integrated from K-Dense-AI/claude-scientific-skills repository

License

MIT License - Open source and freely available for research and commercial use.