技能 sympy
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Benötigen Sie exakte mathematische Ergebnisse anstatt numerischer Approximationen. Dieser Skill bietet umfassende Anleitungen für symbolische Berechnungen mit SymPy, einschließlich Algebra, Infinitesimalrechnung, Matrizen, Physik und Code-Generierung. Perfekt für Ingenieure, Wissenschaftler und Forscher, die präzise symbolische Lösungen benötigen.
支持: Claude Codex Code(CC)
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下载技能 ZIP
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在 Claude 中上传
前往 设置 → 功能 → 技能 → 上传技能
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开启并开始使用
测试它
正在使用“sympy”。 Lösen Sie x Quadrat minus 4 gleich 0 und finden Sie die Ableitung von x hoch 3
预期结果:
- Gleichung x² - 4 = 0
- Lösungen: x = 2, x = -2
- Ableitung von x³ nach x:
- d/dx (x³) = 3x²
- Tipp: Verwenden Sie simplify() um Ergebnisse zu überprüfen und factor() für Polynomfaktorisierung.
安全审计
安全v5 • 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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⚡ 包含脚本 (10)
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⚙️ 外部命令 (452)
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审计者: claude 查看审计历史 →
质量评分
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你能构建什么
Physik-Problemlösung
Richten Sie Mechanikprobleme, Quantenberechnungen und Vektoranalysen mit symbolischen Methoden ein und lösen Sie diese.
Mathematische Herleitung
Leiten Sie Formeln symbolisch her, bevor Sie sie für Produktions-Pipelines in numerischen Code umwandeln.
Mathematisches Lernwerkzeug
Erkunden Sie mathematische Konzepte durch exakte symbolische Berechnung und schrittweise Lösungen.
试试这些提示
Gleichung lösen
Lösen Sie die Gleichung x Quadrat minus 5x plus 6 gleich 0 mit SymPy. Zeigen Sie mir die schrittweise Lösung.
Ableitung berechnen
Finden Sie die Ableitung von sin(x) Quadrat nach x mit SymPy. Finden Sie dann die zweite Ableitung.
Matrixoperationen
Erstellen Sie eine 3x3-Matrix in SymPy und berechnen Sie ihre Eigenwerte, Eigenvektoren und Determinante.
Code generieren
Konvertieren Sie den Ausdruck x Quadrat plus 2x plus 1 mit lambdify in eine NumPy-Funktion. Zeigen Sie, wie Sie sie mit Arrays verwenden.
最佳实践
- Definieren Sie Symbole immer mit symbols() vor der Verwendung, um NameError zu vermeiden
- Verwenden Sie Rational() oder S() anstelle von Fließkommazahlen für exakte Arithmetik
- Wenden Sie Annahmen auf Symbole an (positive=True, integer=True), um einfachere Ergebnisse zu erhalten
- Konvertieren Sie mit lambdify in NumPy-Funktionen für Performance in numerischen Schleifen
避免
- Verwendung von Fließkommazahlen wie 0.5 anstelle von Rational(1, 2) für exakte Ergebnisse
- Wiederholtes Aufrufen von subs() und evalf() in Schleifen anstelle von lambdify()
- Vergessen, Symbole zu definieren, bevor sie in Ausdrücken verwendet werden
- Verwendung von subs() an Singularitäten anstelle von limit() für korrekte Handhabung
常见问题
Welche Plattformen unterstützen diesen Skill?
Dieser Skill funktioniert mit Claude, Codex und Claude Code. Er erfordert Python mit der installierten SymPy-Bibliothek.
Wie präzise sind die Ergebnisse?
SymPy liefert exakte symbolische Ergebnisse. Verwenden Sie evalf() oder N() für numerische Approximationen mit angegebenen Präzision.
Kann ich mit NumPy und SciPy integrieren?
Ja. Verwenden Sie lambdify() um SymPy-Ausdrücke in NumPy-Funktionen zu konvertieren. Funktioniert gut mit SciPy-Solvern.
Sind meine Daten bei der Verwendung dieses Skills sicher?
Ja. Dieser Skill wird lokal unter Verwendung der SymPy-Bibliothek ausgeführt. Es werden keine Daten an externe Server gesendet.
Warum ist meine Berechnung langsam?
Komplexe symbolische Operationen sind rechenintensiv. Verwenden Sie numerische Methoden (lambdify) für großskalige Berechnungen.
Wie vergleicht sich dies mit Mathematica oder MATLAB?
SymPy bietet ähnliche symbolische Fähigkeiten für Python. Es ist kostenlos und Open-Source, kann aber bei sehr komplexen Problemen langsamer sein.