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Found 1,567 Skills
Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).
Comprehensive guide for Manim Community - Python framework for creating mathematical animations and educational videos with programmatic control
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices. Use for Python projects, APIs, data processing, or automation scripts.
Write comprehensive code documentation including JSDoc, Python docstrings, inline comments, function documentation, and API comments. Use when documenting code, writing docstrings, or creating inline documentation.
mypy - Static type checker for Python with gradual typing, strict mode, Protocol support, and framework integration
Python 3.11+ performance optimization guidelines (formerly python-311). This skill should be used when writing, reviewing, or refactoring Python code to ensure optimal performance patterns. Triggers on tasks involving asyncio, data structures, memory management, concurrency, loops, strings, or Python idioms.
Guidelines for Python dependency management using uv, the fast Python package installer and resolver.
Python skill router. Use when planning, implementing, or reviewing Python changes and you need to select focused skills for workflow, design, typing/contracts, reliability, testing, data/state, concurrency, integrations, runtime operations, or notebook async behavior.
Use when writing or reviewing tests for Python behavior, contracts, async lifecycles, or reliability paths. Also use when tests are flaky, coupled to implementation details, missing regression coverage, slow to run, or when unclear what tests a change needs.
Use when preparing branches, commits, or PRs for Python changes — scoping work, running validation gates, and ensuring merge readiness. Also use when debugging CI gate failures, resolving lockfile conflicts, or uncertain what checks to run before opening a PR.
Panel data analysis with Python using linearmodels and pandas.