Results for “python-registry”

Write, review, debug, or test Python Xarray workflows for labeled N-dimensional DataArray and Dataset operations, including coordinates, alignment, indexing, groupby, resample, rolling, weighted reduction, Dask-backed execution, and NetCDF/Zarr I/O.

Manages Python package publishing, versioning, and distribution builds on PyPI, including dependency resolution, index configuration, and package health analysis.

Python logging with the standard library logging module and structlog. Covers log levels, handlers, formatters, structured logging, and production best practices for FastAPI/Django applications. USE WHEN: user mentions "python logging", "fastapi logging", "django logging", asks about "how to log in python", "python logging module", "logging configuration python" DO NOT USE FOR: Node.js logging - use `nodejs-logging` instead, Java logging - use `slf4j` or `logback`, structlog-specific - use `structlog` skill for deep dive

Write, review, debug, test, or optimize pandas Series, DataFrame, Index, groupby, merge, reshape, dtype, missing-value, and time-series code.

Write efficient PostgreSQL queries and design schemas with proper indexing and patterns.

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Use this when a Python service, package, or repository needs a maintainable internal structure for modules, domain logic, adapters, config, entrypoints, and tests. Trigger on requests about project layout and internal organization for Python systems.

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Write, review, debug, test, or optimize Python code using PyArrow arrays, schemas, tables, compute kernels, datasets, Parquet, and Arrow IPC.

B-Tree Indexes

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Install, discover, and wire reusable blocks and components into HyperFrames compositions using the CLI.

Execute Python code in a safe sandboxed environment with 100+ pre-installed libraries for data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, and automation.

Python package reorganization with pyproject.toml inside package directory

Crea y gestiona entornos Python reproducibles con venv, pip, Poetry o Conda, aislando dependencias y configurando variables de entorno para evitar conflictos.

Python dataclass best practices: slots, frozen, validation. Trigger when optimizing dataclasses or creating config classes.

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Use this when a Python repository needs package-level planning, import boundaries, module naming, reusable library extraction, or distribution-friendly structure.

Use for writing, reviewing, debugging, or testing Python terminal presentation built with Rich, including Console streams, Text and markup safety, tables, trees, panels, progress, status, Live displays, render protocols, and deterministic output capture. Do not use for CLI argument parsing, structured logging design, browser UI, or machine-readable protocol output that must remain plain.

Python development best practices including PEP 8 style guidelines, type hints, docstring conventions, and common patterns. Use when writing or modifying Python code.

Design, review, debug, and test Python static types including Protocol, generics, variance, overloads, TypedDict, ParamSpec, TypeGuard, and narrowing.

Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration.

Debug Python: pdb REPL + debugpy remote (DAP).

Write, review, debug, test, or optimize Python code using the rustworkx graph library, with explicit handling of graph kind, index lifecycle, payload semantics, and algorithm result mapping.

Debug Python code interactively with pdb and debugpy, covering local breakpoints, post-mortem analysis, and remote attachment to running processes.

Records common pitfalls and assumptions for Python module execution and GitHub Actions sparse checkout setups.

Debug Python with pdb, breakpoint(), post-mortem inspection, and debugpy remote attach.

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

Python Cheat Sheets (/py)

Run a single experiment iteration. Edit the target file, evaluate, keep or discard.

Python programming patterns and best practices

Applies senior Python rules for typing, async, APIs, persistence, tests, packaging, and architecture.

Use this when designing Python scripts for automation, batch processing, file operations, data transformation, or internal workflows that need deterministic structure and safe environment handling.