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

Start with the right skill: Use the category tables above to find appropriate skills
Chain skills for complex workflows: Literature review → Hypothesis → Experiment design
Use database skills for data access: Query databases before analysis
Visualize results: Use matplotlib/seaborn/plotly skills for publication-quality figures
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
`,
});

scientific-skills