Python

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Create new Claude Agent SDK projects with proper setup and configuration. Choose between TypeScript or Python, get the latest SDK version, and automatically verify your application follows best practices.

Integrates the Pyright language server to provide static type checking and code intelligence for Python files. Adds support for .py and .pyi extensions with advanced type analysis.

A comprehensive guide for modern Python development using uv, ruff, ty, and pytest. It covers project setup, dependency management, testing, security tooling (pre-commit hooks, secret detection, vulnerability scanning), and migration from legacy tools like pip, black, mypy, and pre-commit.

Atheris is a comprehensive reference skill for Python fuzzing using Google's Atheris library. It provides installation guides, harness writing patterns, Docker configurations, corpus management strategies, and AddressSanitizer integration for detecting memory corruption in Python code and C extensions.

This skill guides the creation of Model Context Protocol (MCP) servers that extend Claude's capabilities. It supports both Python and TypeScript implementations with patterns for API integration, OAuth authentication, and response optimization. The skill handles the full workflow from project setup to installation in Claude Code and Claude Desktop.

A comprehensive reference skill for building Python FastAPI applications with Supabase. It provides code templates and patterns for authentication, database access via SQLAlchemy/SQLModel, file storage, realtime subscriptions, and testing with pytest.

A comprehensive reference guide for integrating with the Reddit API. Covers setup, authentication (script apps, read-only, and OAuth2 web flow), common operations like fetching posts and comments, posting, voting, streaming, and direct API access using Python (PRAW, httpx) and TypeScript (Snoowrap, fetch).

A Python development guide that teaches best practices for type hints, testing with pytest, linting with ruff, and strict type checking with mypy. Includes project structure recommendations, CI/CD configurations, pre-commit hooks, and design patterns like dependency injection and the Result pattern.

This skill provides comprehensive guidance for working with Azure Cosmos DB, covering partition key design, CRUD operations, consistency levels, batch operations, change feed processing, and cost optimization. It includes code examples for both TypeScript and Python SDKs.

A comprehensive reference skill for AWS DynamoDB development. Provides guidance on single-table design patterns, GSI strategies, TypeScript SDK v3 and Python boto3 code examples, batch operations, transactions, and local development setup with DynamoDB Local.

A comprehensive guide for building autonomous AI agents using Pydantic AI (Python) and Claude Agent SDK (Node.js). Covers agent architecture, tool design, multi-agent coordination, memory management, guardrails, and testing patterns with practical code examples.