Habilidades sympy
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sympy
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Решение символьной математики с SymPy
También disponible en: K-Dense-AI
Нужны точные математические результаты вместо численных приближений. Этот навык предоставляет комплексное руководство по символьным вычислениям с использованием SymPy, включая алгебру, исчисление, матрицы, физику и генерацию кода. Идеально подходит для инженеров, ученых и исследователей, которым нужны точные символьные решения.
Soporta: Claude Codex Code(CC)
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Pruébalo
Usando "sympy". Решите x в квадрате минус 4 равно 0 и найдите производную x в степени 3
Resultado esperado:
- Уравнение x² - 4 = 0
- Решения: x = 2, x = -2
- Производная x³ по x:
- d/dx (x³) = 3x²
- Совет: Используйте simplify() для проверки результатов и factor() для факторизации многочленов.
Auditoría de seguridad
Segurov5 • 1/17/2026
This is a pure documentation skill containing only markdown files with SymPy guidance and Python code examples. No executable scripts, network calls, file system access, or environment variable usage. The skill provides educational content about using the SymPy library for symbolic mathematics. All 498 static findings are FALSE POSITIVES - patterns detected are markdown code formatting, mathematical documentation references, and legitimate educational content about Python programming.
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⚙️ Comandos externos (452)
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Auditado por: claude Ver historial de auditorías →
Puntuación de calidad
45
Arquitectura
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Mantenibilidad
81
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Comunidad
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Seguridad
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Cumplimiento de la especificación
Lo que puedes crear
Решение физических задач
Постановка и решение задач механики, квантовых расчётов и векторного анализа с использованием символьных методов.
Математический вывод
Символьный вывод формул перед преобразованием в числовой код для рабочих конвейеров.
Инструмент для изучения математики
Изучение математических концепций через точные символьные вычисления и пошаговые решения.
Prueba estos prompts
Решить уравнение
Решите уравнение x в квадрате минус 5x плюс 6 равно 0 с помощью SymPy. Покажите пошаговое решение.
Вычислить производную
Найдите производную sin(x) в квадрате по x с помощью SymPy. Затем найдите вторую производную.
Операции с матрицами
Создайте матрицу 3x3 в SymPy и вычислите её собственные значения, собственные векторы и определитель.
Генерировать код
Преобразуйте выражение x в квадрате плюс 2x плюс 1 в функцию NumPy с помощью lambdify. Покажите, как использовать её с массивами.
Mejores prácticas
- Всегда определяйте символы с помощью symbols() перед использованием, чтобы избежать NameError
- Используйте Rational() или S() вместо чисел с плавающей точкой для точной арифметики
- Применяйте предположения к символам (positive=True, integer=True) для получения более простых результатов
- Преобразуйте в функции NumPy с помощью lambdify для производительности в численных циклах
Evitar
- Использование чисел с плавающей точкой, например 0.5, вместо Rational(1, 2) для точных результатов
- Вызов subs() и evalf() повторно в циклах вместо использования lambdify()
- Забывание определить символы перед их использованием в выражениях
- Использование subs() в особенностях вместо limit() для правильной обработки
Preguntas frecuentes
Какие платформы поддерживают этот навык?
Этот навык работает с Claude, Codex и Claude Code. Требуется Python с установленной библиотекой SymPy.
Какова точность результатов?
SymPy предоставляет точные символьные результаты. Используйте evalf() или N() для численных приближений с заданной точностью.
Могу ли я интегрировать с NumPy и SciPy?
Да. Используйте lambdify() для преобразования выражений SymPy в функции NumPy. Хорошо работает с решателями SciPy.
Безопасны ли мои данные при использовании этого навыка?
Да. Этот навык работает локально с использованием библиотеки SymPy. Данные не отправляются на внешние серверы.
Почему мои вычисления медленные?
Сложные символьные операции вычислительно затратны. Используйте численные методы (lambdify) для крупномасштабных вычислений.
Чем это сравнивается с Mathematica или MATLAB?
SymPy предоставляет аналогичные символьные возможности для Python. Он бесплатный и с открытым исходным кодом, но может быть медленнее для очень сложных задач.
Detalles del desarrollador
Autor
davila7Licencia
MIT
Repositorio
https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/sympyRef.
main
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