category-filtering-and-difficulty-analysis
对Excel数据进行自定义分类统计、交叉分析与可视化，并基于多维度指标（如文本长度、术语密度、正则匹配等）进行综合评分与分级，适用于多类别数据分布统计及文本内容难度/质量评估场景。
npx skills add OpenSenseNova/SenseNova-Skills --skill category-filtering --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Skill Steps ### Step1 加载数据与环境配置 ```python import pandas as pd import matplotlib.pyplot as plt import numpy as np import re # 配置中文字体，确保图表正常显示 plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'WenQuanYi Zen Hei'] plt.rcParams['axes.unicode_minus'] = False def load_excel_data(file_path: str, skip_rows: int = 2): """读取并加载Excel文件中的数据，跳过标题行以获取原始数据""" # 技巧：处理合并单元格可使用 df.ffill() 等方法 df = pd.read_excel(file_path, skiprows=skip_rows) return df ``` ### Step2 定义分类映射函数骨架 ```python def categorize_data(item: str) -> str: """将具体项归类到大类中（分类映射函数骨架）""" if pd.isna(item): return '未知' if item in ['类别A1', '类别A2', '类别A3']: return '大类A' elif item in ['类别B1', '类别B2']: return '大类B' else: return '其他' ``` ### Step3 统一分析与可视化流程（柱状图、饼图、交叉分析） ```python def analyze_and_visualize(df: pd.DataFrame, category_col: str, group_col: str = None, output_path: str = './', top_n: int = None, custom_categorize=None): """统一分析与可视化流程：生成柱状图、饼图、交叉分析堆叠柱状图""" df_clean = df.copy() # 应用自定义分类规则 if custom_categorize: df_clean[f'{category_col}大类'] = df_clean[category_col].apply(custom_categorize) analyze_col = f'{category_col}大类' else: analyze_col = category_col # value_counts + 占比统计 counts = df_clean[analyze_col].value_count
- Skill Steps
- Step1 加载数据与环境配置
- Step2 定义分类映射函数骨架
- Step3 统一分析与可视化流程（柱状图、饼图、交叉分析）
- Step4 多维度评分与分级算法结构
- Step5 生成综合评分分析图表
- Step6 执行完整分析流程
What does the category-filtering-and-difficulty-analysis skill do?
对Excel数据进行自定义分类统计、交叉分析与可视化，并基于多维度指标（如文本长度、术语密度、正则匹配等）进行综合评分与分级，适用于多类别数据分布统计及文本内容难度/质量评估场景。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill category-filtering --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,855 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.