nsforge-mcp: Skill for Claude Code

程式碼生成 Skill

⚠️ 生成後必須向用戶展示結果！

• 生成的 Python 函數要用程式碼區塊展示
• 生成的 LaTeX 要渲染給用戶看
• 生成 Markdown 報告後顯示完整內容

工具速查

輸出類型	工具
Python 函數	generate_python_function(name, description, parameters, steps, return_vars)
LaTeX 公式	generate_latex_derivation(steps, title?, include_preamble?)
Markdown 報告	generate_derivation_report(title, given, steps, result, assumptions?, limitations?)
SymPy 腳本	generate_sympy_script(expressions, operations)

調用範例

Python 函數

generate_python_function(
    name="arrhenius_rate",
    description="Calculate rate using Arrhenius equation",
    parameters=[
        {"name": "k_ref", "type": "float", "description": "Reference rate (1/s)"},
        {"name": "E_a", "type": "float", "description": "Activation energy (J/mol)"},
        {"name": "T", "type": "float", "description": "Temperature (K)"}
    ],
    steps=[
        {"description": "Arrhenius equation", "expression": "k_ref * exp(E_a/R * (1/T_ref - 1/T))", "result_var": "k"}
    ],
    return_vars=["k"]
)

LaTeX

generate_latex_derivation(
    steps=[
        {"description": "Base model", "expression": "C = C_0 e^{-kt}"},
        {"description": "Substitute k", "expression": "C = C_0 e^{-k_{ref} e^{...} t}"}
    ],
    title="Temperature-Corrected Elimination"
)

Markdown 報告

generate_derivation_report(
    title="Temperature-Corrected Elimination",
    given=["One-compartment model: $C = C_0 e^{-kt}$"],
    steps=[{"description": "...", "expression": "..."}],
    result="$C(t,T) = ...$",
    assumptions=["First-order elimination"],
    limitations=["Valid for 32-42°C"]
)

SymPy 腳本

generate_sympy_script(
    expressions=[
        {"name": "C_base", "expr": "C_0 * exp(-k*t)", "description": "One-compartment"}
    ],
    operations=[
        {"op": "substitute", "input": "C_base", "var": "k", "replacement": "k_arrhenius"}
    ]
)

先計算再生成

複雜情況先用 SymPy-MCP 計算（如 dsolve_ode），再用 NSForge 生成程式碼。