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 geomaster
Process satellite imagery, perform GIS analysis, and apply spatial machine learning across 70+ geospatial topics with code examples in 8 programming languages.
30.2k · bundle
k-dense-ai geopandas
Extends pandas for geospatial vector data analysis, including reading/writing shapefiles, GeoJSON, GeoPackage, and PostGIS, performing spatial joins, geometric operations, coordinate transformations, and creating static or interactive maps.
30.2k · bundle
k-dense-ai liteparse
Parse PDFs, Office files, and images locally with layout-preserved text, bounding boxes, OCR, and page screenshots for RAG and multimodal agents.
30.2k · bundle
k-dense-ai neurokit2
Process and analyze physiological signals including ECG, EEG, EDA, RSP, PPG, EMG, and EOG using Python.
30.2k · bundle
k-dense-ai tiledbvcf
Store, query, and export genomic variant data (VCF/BCF) using TileDB's sparse array technology for scalable population genomics workflows.
30.2k
k-dense-ai torchdrug
Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.
30.2k · bundle
k-dense-ai etetoolkit
Manipulate phylogenetic trees, detect evolutionary events, integrate NCBI taxonomy, and create publication-quality visualizations using the ETE toolkit.
30.2k · bundle
k-dense-ai exa-search
Search the web and extract content from URLs using Exa, with support for academic and scientific sources.
30.2k · bundle
k-dense-ai markitdown
Convert files and office documents to Markdown using Microsoft's MarkItDown tool. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
30.2k · bundle
k-dense-ai matplotlib
Create publication-quality static, animated, and interactive plots with fine-grained control over every element using Matplotlib's pyplot and object-oriented APIs.
30.2k · bundle
k-dense-ai pacsomatic
Validates inputs, generates samplesheets and launch scripts, and optionally executes nf-core/pacsomatic matched tumor-normal workflows from BAM files, supporting local runs and scheduler submission (LSF/Slurm/PBS/SGE).
30.2k · bundle
k-dense-ai polars-bio
Perform high-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames, including overlap, nearest, merge, coverage, complement, subtract, and reading/writing BED, VCF, BAM, GFF, FASTA, and FASTQ formats with streaming and cloud-native support.
30.2k · bundle
k-dense-ai pylabrobot
Control liquid handling robots, plate readers, pumps, and other lab equipment through a unified Python interface across platforms.
30.2k · bundle
k-dense-ai scikit-bio
Analyze biological sequences, alignments, phylogenetic trees, and diversity metrics (alpha/beta, UniFrac) with ordination (PCoA) and PERMANOVA for microbiome and community ecology data.
30.2k · bundle
k-dense-ai scvi-tools
Provides deep generative models for single-cell omics analysis, including probabilistic batch correction, transfer learning, differential expression, and multi-modal integration.
30.2k · bundle
k-dense-ai umap-learn
Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.
30.2k · bundle
k-dense-ai bioservices
Query 40+ bioinformatics services (UniProt, KEGG, ChEMBL, Reactome) with a unified Python interface for cross-database analysis, identifier mapping, and sequence analysis.
30.2k · bundle
k-dense-ai bulk-rnaseq
Orchestrates a complete bulk RNA-seq differential-expression study from raw FASTQ reads through QC, alignment, quantification, differential expression, pathway enrichment, and publication figures.
30.2k · bundle
k-dense-ai statsmodels
Fit statistical models (OLS, GLM, ARIMA, mixed models) with detailed diagnostics, residuals, and inference for econometrics and time series analysis.
30.2k · bundle
k-dense-ai zarr-python
Store and process large N-dimensional arrays with chunking, compression, and parallel I/O, integrating with NumPy, Dask, and Xarray for cloud-native scientific computing.
30.2k · bundle
k-dense-ai parallel-web
Search the web, extract URL content, enrich datasets with web-sourced fields, and run deep research reports, prioritizing academic and scientific sources.
30.2k · bundle
k-dense-ai transformers
Load pre-trained models from Hugging Face Hub, run pipeline inference, generate text, and fine-tune models on NLP, vision, audio, and multimodal tasks using the Transformers library.
30.2k · bundle
k-dense-ai usfiscaldata
Query the U.S. Treasury Fiscal Data REST API for federal financial data including national debt, daily and monthly treasury statements, securities auctions, interest rates, exchange rates, savings bonds, and government revenue and spending statistics. No API key required.
30.2k · bundle
k-dense-ai open-notebook
Self-host an open-source research notebook with AI-powered note generation, multi-speaker podcast creation, and context-aware document chat, supporting 16+ AI providers.
30.2k · bundle
k-dense-ai phylogenetics
Build and analyze phylogenetic trees using MAFFT, IQ-TREE 2, and FastTree, with visualization via ETE3 or FigTree for evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies.
30.2k · bundle
k-dense-ai database-lookup
Query documented public database APIs with explicit endpoints, filters, pagination, and provenance for reproducible retrieval of scientific, regulatory, or financial facts.
30.2k · bundle
k-dense-ai scikit-survival
Perform survival analysis and time-to-event modeling in Python using scikit-survival, including Cox models, random survival forests, gradient boosting, survival SVMs, and evaluation metrics like concordance index and Brier score.
30.2k · bundle
k-dense-ai torch-geometric
Build and train graph neural networks with PyTorch Geometric, covering node/link/graph classification, message passing layers, heterogeneous graphs, and custom datasets.
30.2k · bundle
k-dense-ai cellxgene-census
Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data, enabling efficient access to cell metadata, gene expression slices, summary counts, and embeddings without downloading whole datasets.
30.2k · bundle
k-dense-ai optimize-for-gpu
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, and other RAPIDS libraries for dramatic speedups on numerical, data, ML, graph, and simulation workloads.
30.2k · bundle
k-dense-ai omero-integration
Access microscopy images and metadata via the OMERO Python API: retrieve datasets, analyze pixels, manage ROIs and annotations, and batch-process for high-content screening workflows.
30.2k · bundle
k-dense-ai statistical-power
Calculate sample sizes, minimum detectable effects, and power curves for study planning using closed-form formulas or Monte Carlo simulation.
30.2k · bundle
k-dense-ai molecular-dynamics
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).
30.2k · bundle
k-dense-ai pathway-enrichment
Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Covers over-representation analysis (ORA), Gene Set Enrichment Analysis (GSEA), and single-sample scoring using gseapy, g:Profiler, and Enrichr libraries.
30.2k · bundle
k-dense-ai experimental-design
Design experiments and studies before data collection — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable.
30.2k · bundle
k-dense-ai timesfm-forecasting
Forecast any univariate time series (sales, sensors, energy, vitals, weather) zero-shot using Google's TimesFM foundation model, with point forecasts and prediction intervals from CSV, DataFrame, or array inputs.
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 matplotlib, polars-bio, open-notebook. 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.