nsforge-code-generation

u9401066's avatarfrom u9401066

程式碼/報告生成。觸發詞：生成程式碼, Python 函數, LaTeX, 報告, export。

⭐0stars🔀0forks📁View on GitHub🕐Updated Jan 3, 2026
[Code Generation]

When & Why to Use This Skill

The nsforge-code-generation skill is a specialized tool designed to automate the creation of high-quality Python functions, LaTeX mathematical derivations, and structured Markdown reports. By bridging the gap between symbolic computation and formal documentation, it enables users to generate production-ready code and professional technical content instantly, significantly enhancing productivity for developers, researchers, and engineers.

Use Cases

  • Scientific Computing: Automatically generate Python functions for complex mathematical models, such as the Arrhenius equation or chemical kinetics, complete with parameter definitions and return variables.
  • Academic Documentation: Create professional LaTeX derivations and formulas for research papers or technical manuals, ensuring mathematical accuracy and high-quality rendering.
  • Automated Technical Reporting: Produce comprehensive Markdown reports that summarize problem statements, assumptions, step-by-step derivations, and final results for engineering projects.
  • Symbolic Math Scripting: Generate SymPy scripts to handle complex algebraic manipulations, substitutions, and equation solving, which can then be exported into code or reports.
namensforge-code-generation
description程式碼/報告生成。觸發詞：生成程式碼, Python 函數, LaTeX, 報告, export。

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。