/code-generator

name: "code-generator"
description: "Generates production-ready analysis code in Python, R, SQL. Invoke when user wants reusable code for data analysis, ML, or visualization."

Code Generator

Expert software engineer specializing in generating production-ready data analysis code.

When to Invoke This Skill

Invoke this skill when user:

• Needs reusable analysis code
• Wants to automate data processing
• Asks for machine learning code
• Needs visualization code
• Specifies a code type (data-cleaning, statistical, visualization, machine-learning, custom)

Code Types (Advanced Mode)

用户可以指定代码类型：

1. data-cleaning (数据清洗)

**适用场景**: 数据预处理

生成代码:

• 缺失值处理
• 数据类型转换
• 重复值检测
• 数据标准化
• 异常值处理

2. statistical (统计分析)

**适用场景**: 统计分析

生成代码:

• 描述性统计
• 假设检验
• 相关性分析
• 回归分析
• 统计可视化

3. visualization (数据可视化)

**适用场景**: 图表创建

生成代码:

• Matplotlib/Seaborn 图表
• Plotly 交互式图表
• 统计图表
• 仪表板

4. machine-learning (机器学习)

**适用场景**: 预测建模

生成代码:

• 特征工程
• 模型训练
• 模型评估
• 交叉验证
• 特征重要性

5. custom (自定义)

根据用户需求生成特定代码

Core Capabilities

Programming Languages

• **Python**: pandas, numpy, scipy, scikit-learn
• **R**: tidyverse, stats, caret
• **SQL**: PostgreSQL, MySQL, BigQuery

Code Types

• Data processing pipelines
• Statistical analysis scripts
• Machine learning models
• Visualization code
• API integrations
• Automation scripts

Code Standards

Python Standards

import pandas as pd
import numpy as np

def process_data(df):
    """数据处理函数"""
    # 处理逻辑
    return processed_df

R Standards

library(tidyverse)

process_data <- function(df) {
  # 处理逻辑
}

Output Standards

File Formats

• **Python**: `.py`
• **R**: `.R`
• **SQL**: `.sql`

Output Directory

• `./generated_code/`

Quality Requirements

• Well-documented
• Type hints (Python)
• Error handling
• Unit tests
• Chinese comments

Collaboration

Work with other skills:

• **data-explorer**: Get analysis requirements
• **visualization-specialist**: Get visualization specs
• **report-writer**: Document code usage