Explore skills

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening.

Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

Automate Supabase database queries, table management, project administration, storage, edge functions, and SQL execution via Rube MCP (Composio). Always search tools first for current schemas.

A multi-objective optimization framework featuring NSGA-II, NSGA-III, MOEA/D, Pareto fronts, and constraint handling. It includes benchmarks like ZDT and DTLZ for engineering design and optimization problems.

Infers gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). It's used to analyze transcriptomics data (bulk/single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions, supporting distributed computation for large datasets.

Programmatically query the CELLxGENE Census (61M+ cells) to get expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. It is best for population-scale queries and reference atlas comparisons.

This skill demonstrates using the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval, covering various assays like binding, screening, BLI/SPR, and thermostability, and is triggered by relevant keywords or code imports.

This skill provides the data structure for annotated matrices in single-cell analysis, ideal for .h5ad files or integration with the scverse ecosystem. For analysis workflows, use scanpy; for probabilistic models, scvi-tools; and for population-scale queries, cellxgene-census.

Use this skill for working with Brain Imaging Data Structure (BIDS) datasets, including organizing neuroscience and biomedical data, querying layouts, validating compliance, converting DICOM to BIDS, and creating BIDS derivatives.