Habilidades sympy
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sympy
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使用 SymPy 解決符號數學問題
Também disponível em: K-Dense-AI
需要精確的數學結果而非數值近似。本技能提供使用 SymPy 進行符號計算的全面指導，包括代數、微積分、矩陣、物理和程式碼生成。非常適合需要精確符號解的工程師、科學家和研究人員。
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A utilizar "sympy". 解 x 的平方減 4 等於 0，並求 x 的 3 次方的導數
Resultado esperado:
- 方程式 x² - 4 = 0
- 解：x = 2，x = -2
- x³ 對 x 的導數：
- d/dx (x³) = 3x²
- 提示：使用 simplify() 驗證結果，使用 factor() 進行多項式因式分解。
Auditoria de Segurança
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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⚡ Contém scripts (10)
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⚙️ Comandos externos (452)
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📁 Acesso ao sistema de arquivos (6)
🌐 Acesso à rede (5)
Auditado por: claude Ver Histórico de Auditoria →
Pontuação de qualidade
45
Arquitetura
100
Manutenibilidade
81
Conteúdo
20
Comunidade
100
Segurança
91
Conformidade com especificações
O Que Você Pode Construir
物理問題求解
使用符號方法設定和求解力學問題、量子計算和向量分析。
數學推導
在將公式轉換為數值程式碼以用於生產流程之前，先以符號方式推導公式。
數學學習工具
透過精確的符號計算和逐步解題來探索數學概念。
Tente Estes Prompts
解方程式
使用 SymPy 解方程式 x 的平方減 5x 加 6 等於 0。顯示逐步解題過程。
計算導數
使用 SymPy 求 sin(x) 的平方對 x 的導數。然後求二階導數。
矩陣運算
在 SymPy 中建立一個 3x3 矩陣，並計算其特徵值、特徵向量和行列式。
生成程式碼
使用 lambdify 將 x 的平方加 2x 加 1 的表示式轉換為 NumPy 函數。顯示如何與陣列一起使用。
Melhores Práticas
- 永遠在使用前使用 symbols() 定義符號，以避免 NameError
- 使用 Rational() 或 S() 而非浮點數以進行精確運算
- 對符號套用假設（positive=True、integer=True）以獲得更簡化的結果
- 使用 lambdify 轉換為 NumPy 函數以在數值迴圈中提高效能
Evitar
- 使用像 0.5 這樣的浮點數而非 Rational(1, 2) 來獲得精確結果
- 在迴圈中重複呼叫 subs() 和 evalf() 而非使用 lambdify()
- 忘記在使用表示式中的符號之前先定義它們
- 在奇點處使用 subs() 而非使用 limit() 進行正確處理
Perguntas Frequentes
哪些平台支援此技能？
此技能適用於 Claude、Codex 和 Claude Code。它需要已安裝 SymPy 函式庫的 Python 環境。
結果有多精確？
SymPy 提供精確的符號結果。使用 evalf() 或 N() 並指定精確度以獲得數值近似值。
我可以與 NumPy 和 SciPy 整合嗎？
可以。使用 lambdify() 將 SymPy 表示式轉換為 NumPy 函數。與 SciPy 解題器配合良好。
使用此技能時我的資料安全嗎？
可以。此技能使用 SymPy 函式庫在本地執行。不會將資料傳送至外部伺服器。
為什麼我的計算很慢？
複雜的符號運算在計算上非常昂貴。對於大規模計算，使用數值方法（lambdify）。
這與 Mathematica 或 MATLAB 相比如何？
SymPy 為 Python 提供類似的符號功能。它是免費的開源軟體，但對於非常複雜的問題可能會比較慢。
Detalhes do Desenvolvedor
Autor
davila7Licença
MIT
Repositório
https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/sympyReferência
main
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