| 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"""
return processed_df
R Standards
library(tidyverse)
process_data <- function(df) {
}
Output Standards
File Formats
- Python:
.py
- R:
.R
- SQL:
.sql
Output Directory
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