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。
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