oimiragieo/scientific-skills

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({
  task_id: 'task-1',
  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

Iron Laws

1. ALWAYS query scientific databases (PubMed, ChEMBL, UniProt) before performing any analysis — raw analysis without literature and database context produces uninformed conclusions that duplicate prior work.
2. NEVER perform analysis without documenting all steps (data sources, parameters, library versions, transformations) — undocumented research is irreproducible and cannot be peer-reviewed or extended.
3. ALWAYS chain multiple domain-specific skills for complex workflows — single-tool analysis misses interdependencies across biology, chemistry, and clinical domains.
4. NEVER report findings without statistical validation — scientific claims require appropriately sized samples, validated methods, and quantified uncertainty.
5. ALWAYS visualize intermediate results after each major processing step — data errors and outliers surface in visualizations before propagating silently to final conclusions.

Anti-Patterns

| Anti-Pattern | Why It Fails | Correct Approach | | ----------------------------------------------------------- | ----------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | | Performing analysis without querying databases first | Missing context from existing literature duplicates known work and misses prior art | Query PubMed/ChEMBL/UniProt before analysis to ground work in existing scientific knowledge | | Using a single tool for complex multi-domain analysis | Single-tool analysis misses domain boundary interdependencies | Chain multiple domain-specific skills (rdkit for chemistry, scanpy for single-cell, biopython for genomics) | | Skipping intermediate visualization during data processing | Errors and outliers propagate silently from preprocessing to final results | Visualize data distribution and quality metrics after each major transformation step | | Generating hypotheses without reviewing existing literature | Reinvents known solutions and ignores contradictory prior findings | Always invoke literature-review skill first; only generate hypotheses after reviewing existing evidence | | Reporting findings without documenting analysis provenance | Research cannot be reproduced, verified, or extended by other researchers | Log all data sources, version numbers, parameters, and transformation steps in the research report |

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

• New pattern → .claude/context/memory/learnings.md
• Issue found → .claude/context/memory/issues.md
• Decision made → .claude/context/memory/decisions.md

ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

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.