scientific-skills

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.

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

```javascript

// 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

```

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 |

Literature Review Workflow

```python

# 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

```python

# 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

```python

# 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

```python

# 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

```

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)

```

Direct Invocation

```javascript

// Invoke specific skill

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

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

```

Chained Workflows

```javascript

// Multi-skill workflow

Skill({ skill: 'scientific-skills/literature-review' });

Skill({ skill: 'scientific-skills/hypothesis-generation' });

Skill({ skill: 'scientific-skills/scientific-schematics' });

```

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

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

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

```javascript

Task({

subagent_type: 'python-pro',

description: 'Analyze molecular dataset with RDKit',

prompt: `You are the PYTHON-PRO agent with scientific research expertise.

Analyze the molecular dataset for drug-likeness properties.

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

1. Load molecular data
2. Calculate descriptors
3. Apply Lipinski filters
4. Generate visualization
5. Report findings

`,

});

```

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](https://github.com/K-Dense-AI/claude-scientific-skills)
• [RDKit Documentation](https://www.rdkit.org/docs/)
• [Scanpy Documentation](https://scanpy.readthedocs.io/)
• [BioPython Tutorial](https://biopython.org/wiki/Tutorial)

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

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