Results for “yara-python”

Develop precise YARA rules for malware detection by identifying unique byte patterns, strings, and behavioral indicators in executable files while minimizing false positives.

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.

Write, review, debug, or test Python asyncio code involving coroutines, Tasks, TaskGroups, cancellation, timeouts, queues, synchronization, and blocking-call boundaries.

Use when implementing python functionality with production-grade patterns and safeguards.

Generates type-safe, async-first Python 3.11+ code with full type annotations, mypy strict mode, pytest test suites, and validation via black and ruff.

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

Framework for scaling Python applications from a laptop to a cluster. Includes Ray Core for distributed computing, Ray Serve for model serving, Ray Tune for hyperparameter optimization, and Ray Data for distributed data processing.

Create, review, debug, test, or reproduce Python Jupyter notebooks by inspecting format, executing cells top-to-bottom in a clean kernel, and verifying outputs.

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.

Convert Python code to idiomatic Roc. Use when migrating Python projects to Roc, translating Python patterns to idiomatic Roc, or refactoring Python codebases for type safety, functional purity, and native performance. Extends meta-convert-dev with Python-to-Roc specific patterns.

Provides Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

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

Write and review modern Python 3.12+ code with async patterns, performance optimization, and production-ready practices using the latest ecosystem tools.

Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Use when writing or editing typed Python scripts, parsing untyped input, choosing lightweight data shapes, or resolving common checker errors without project-scale type-system research.

Use for writing, reviewing, debugging, testing, or optimizing Python code that inspects, edits, extracts, validates, preserves, or generates Microsoft PowerPoint Open XML presentations, primarily .pptx, using python-pptx, PresentationML/OOXML, Pillow, or supporting Python libraries. Trigger on slides, masters, layouts, placeholders, shapes, text, pictures, tables, charts, notes, themes, hyperlinks, embedded objects, macros, preservation, geometry, rendering verification, and presentation package inspection. Do not use for .ppt binary files, PowerPoint UI automation, VBA execution, slideshow execution, or presentation advice with no Python or file boundary.

Secure Python services against secret leakage, injection, unsafe subprocess calls, and dependency drift. Use when handling env vars, tokens, SQL, file paths, shell commands, auth flows, or Python security gates.

Implements Children's Online Privacy Protection Act (COPPA) compliance under 16 CFR Part 312. Covers verifiable parental consent methods including signed forms, credit card verification, government ID, knowledge-based authentication, and video call. Includes FTC safe harbor programs and enforcement actions. Keywords: COPPA, FTC, children, parental consent, safe harbor, verifiable consent.

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

Use for writing, reviewing, debugging, testing, or analyzing Python SimPy discrete-event simulations. Trigger on Environment, Event, Process, timeout, Resource, PriorityResource, PreemptiveResource, Container, Store, queues, interrupts, simulation clocks, replications, or SimPy monitoring. Do not use for asyncio services, wall-clock schedulers, continuous ODE solvers, or Monte Carlo code without an event-process model.

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

Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

Use for writing, reviewing, debugging, or testing bounded retry policies in Python with Tenacity, including retry predicates, stop and wait strategies, jitter, exception propagation, callbacks, Retrying, and AsyncRetrying. Do not use for generic loops, scheduled jobs, domain polling without a retryable operation, or operations whose side effects are not safe to repeat.

Pythonic 惯用法、PEP 8 标准、类型提示以及构建稳健、高效且可维护的 Python 应用程序的最佳实践。

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

Foundational Python patterns covering core syntax, idioms, type hints, testing, and modern tooling. Use when writing Python code, understanding Pythonic patterns, working with type hints, or needing guidance on which specialized Python skill to use. This is the entry point for Python development.

Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

Apply idiomatic Python patterns and best practices for writing robust, efficient, and maintainable code, covering type hints, error handling, context managers, and data classes.

Python programming patterns and best practices

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

Python language (3.10-3.14). Covers typing, async, and modern patterns. Use when writing Python applications. USE WHEN: user mentions "python", "type hints", "dataclasses", "async/await", asks about "asyncio", "context managers", "match statement", "walrus operator", "PEP 695", "type parameter", "generic" DO NOT USE FOR: FastAPI framework - use `backend-fastapi` skill instead DO NOT USE FOR: Django framework - use Django-specific skill DO NOT USE FOR: Package management - use `python-packaging` skill DO NOT USE FOR: Linting/type checking config - use `python-quality` skill

Convert Python code to idiomatic Scala. Use when migrating Python projects to Scala, translating Python patterns to Scala's hybrid OOP+FP paradigm, or refactoring Python codebases for type safety, JVM integration, and functional programming. Extends meta-convert-dev with Python-to-Scala specific patterns.