Data & Analytics Agent Skills
Data agent skills make AI agents useful for data work: writing SQL, cleaning datasets, building pipelines, working with spreadsheets, and producing analyses. Each skill is a reviewed SKILL.md file that teaches the agent one workflow well, ready to install in seconds.
Data & Analytics
1,725 skills
k-dense-ai qutip
Simulate and analyze open quantum systems using QuTiP, including master equations, Lindblad dynamics, decoherence, and quantum optics.
30.2k · bundle
k-dense-ai rdkit
Perform cheminformatics tasks including molecular I/O, descriptor calculation, fingerprinting, substructure search, and similarity analysis using the RDKit library.
30.2k · bundle
k-dense-ai simpy
Build discrete-event simulations of systems with processes, queues, resources, and time-based events using SimPy in Python.
30.2k · bundle
k-dense-ai sympy
Perform exact symbolic mathematics in Python — algebra, calculus, equation solving, symbolic linear algebra, and code generation via lambdify or LaTeX.
30.2k · bundle
k-dense-ai depmap
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores, drug sensitivity data, and gene effect profiles to identify cancer-specific vulnerabilities, synthetic lethal interactions, and validate oncology drug targets.
30.2k · bundle
k-dense-ai flowio
Parse FCS (Flow Cytometry Standard) files v2.0-3.1, extract events as NumPy arrays, read metadata and channels, and convert to CSV or DataFrame for flow cytometry data preprocessing.
30.2k · bundle
k-dense-ai geniml
Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
30.2k · bundle
k-dense-ai matlab
Perform numerical computing, matrix operations, data analysis, and scientific visualization using MATLAB or GNU Octave.
30.2k · bundle
k-dense-ai pathml
Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).
30.2k · bundle
k-dense-ai polars
Process data with high-performance DataFrames using Polars' expression-based API, lazy evaluation, and parallel execution for ETL, analytics, and pandas migration.
30.2k · bundle
k-dense-ai scanpy
Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.
30.2k · bundle
k-dense-ai scvelo
Estimate cell state transitions from unspliced/spliced mRNA dynamics using scVelo, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data.
30.2k · bundle
k-dense-ai anndata
Create, read, manipulate, and store annotated data matrices using the AnnData Python package, designed for single-cell genomics and general-purpose annotated data workflows.
30.2k · bundle
k-dense-ai astropy
Perform astronomical data analysis with Astropy: coordinate transformations, unit conversions, FITS I/O, cosmological calculations, time handling, table operations, and WCS transformations.
30.2k · bundle
k-dense-ai cobrapy
Perform constraint-based metabolic modeling with COBRApy: run FBA, FVA, gene knockouts, flux sampling, and manage SBML models for systems biology and metabolic engineering.
30.2k · bundle
k-dense-ai datamol
Simplify molecular cheminformatics with a Pythonic wrapper around RDKit for SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing.
30.2k · bundle
k-dense-ai lamindb
Manage biological datasets and models with LaminDB, an open-source lineage-native lakehouse. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation, collections, branches, storage, and workflow integrations.
30.2k · bundle
k-dense-ai matchms
Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.
30.2k · bundle
k-dense-ai medchem
Apply medicinal chemistry filters for compound triage: drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and a custom query language for library filtering.
30.2k · bundle
k-dense-ai molfeat
Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.
30.2k · bundle
k-dense-ai onekgpd
Query the 1000 Genomes Project dataset at the individual participant level to find variants, carriers, and relatedness information.
30.2k · bundle
k-dense-ai primekg
Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.
30.2k · bundle
k-dense-ai pydicom
Read, write, and modify DICOM medical imaging files, including pixel data extraction, metadata manipulation, anonymization, and format conversion.
30.2k · bundle
k-dense-ai seaborn
Create publication-quality statistical graphics with dataset-oriented plotting, multivariate analysis, and automatic statistical estimation using minimal code.
30.2k · bundle
k-dense-ai arboreto
Infer gene regulatory networks from gene expression data using scalable algorithms (GRNBoost2, GENIE3) with support for distributed computation.
30.2k · bundle
k-dense-ai deepchem
Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.
30.2k · bundle
k-dense-ai fluidsim
Run computational fluid dynamics simulations using Python, including Navier-Stokes equations, shallow water, and stratified flows with pseudospectral methods and HPC support.
30.2k · bundle
k-dense-ai histolab
Process whole slide images for digital pathology: detect tissue, extract tiles, and prepare datasets for deep learning pipelines.
30.2k · bundle
k-dense-ai networkx
Create, analyze, and visualize complex networks and graphs in Python with NetworkX, including graph algorithms, community detection, synthetic network generation, and multiple I/O formats.
30.2k · bundle
k-dense-ai nextflow
Build, run, and debug Nextflow data pipelines and nf-core workflows end to end, covering processes, channels, operators, configuration, testing, and deployment to HPC or cloud.
30.2k · bundle
k-dense-ai pydeseq2
Perform differential gene expression analysis for bulk RNA-seq data using PyDESeq2, supporting formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
30.2k · bundle
k-dense-ai pyhealth
Build clinical deep-learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train with the PyHealth Trainer, and compute clinical metrics.
30.2k · bundle
k-dense-ai pymatgen
Analyze and manipulate crystal structures, compute phase diagrams, and access the Materials Project database using the pymatgen library.
30.2k · bundle
k-dense-ai pyopenms
Analyze proteomics and metabolomics mass spectrometry data with PyOpenMS: read/write MS file formats, process spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines using ready-to-run scripts.
30.2k · bundle
k-dense-ai biopython
Manipulate biological sequences, parse FASTA/GenBank/PDB files, access NCBI databases, run BLAST searches, and perform phylogenetics using the Biopython library.
30.2k · bundle
k-dense-ai deeptools
Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.
30.2k · bundle
Frequently asked questions
What are Data & Analytics agent skills?
Data agent skills make AI agents useful for data work: writing SQL, cleaning datasets, building pipelines, working with spreadsheets, and producing analyses. Each skill is a reviewed SKILL.md file that teaches the agent one workflow well, ready to install in seconds.
Which Data & Analytics skills are most installed?
Popular Data & Analytics skills on SkillMD right now include seaborn, pyhealth, pathml. Rankings shift as installs change; sort this page by "Most downloaded" for the live list.
Do Data & Analytics skills work with Claude Code and Cursor?
Yes. Every skill here ships as a SKILL.md file, an open format that works in Claude Code, Claude.ai, Cursor, Codex, Windsurf, and 60+ other agents. Install one with npx skillmds add <owner>/<name>, or copy the file into your agent's skills directory.