text-normalization-and-large-file-processing
对Excel文件进行文本标准化清洗（如去除异常前缀、提取纯中文字符等），并，最终输出清洗后的Excel文件并提供下载链接。
npx skills add OpenSenseNova/SenseNova-Skills --skill text-normalization --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 > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. Step1 识别并清洗包含前缀符号的异常数值字段，统一转换为整数类型；同时使用正则表达式清洗文本字段，仅保留 Unicode 范围内的中文字符。 ```python import re import numpy as np target_numeric_col = '需要转数字的文本列' # 示例：'获赞' target_text_col = '需要提取中文的列' # 示例：'收货人' # 1. 清洗包含前缀符号的数值字段 prefix_patterns = ['.', 'I ', '■ ', '一 ', '_', '. '] def clean_numeric_with_prefix(value): val_str = str(value).strip() if val_str in ['None', 'nan', '', 'nan']: return np.nan for prefix in prefix_patterns: if val_str.startswith(prefix): val_str = val_str[len(prefix):].strip() break if val_str == '': return np.nan try: return int(val_str) except ValueError: return np.nan # 2. 清洗文本字段，仅保留 Unicode 范围内的中文字符（\u4e00-\u9fff） def clean_chinese_name(name): if pd.isna(name): return name s = str(name) chinese_chars = re.findall(r'[\u4e00-\u9fff]', s) cleaned = ''.join(chinese_chars) return cleaned if cleaned else '' if target_numeric_col in df.columns: df[f'{target_numeric_col}_清洗后'] = df[target_numeric_col].apply(clean_numeric_with_prefix) if target_text_col in df.columns: df[f'{target_text_col}_清洗后'] = df[target_text_col].apply(c
- Skill Steps
What does the text-normalization-and-large-file-processing skill do?
对Excel文件进行文本标准化清洗（如去除异常前缀、提取纯中文字符等），并，最终输出清洗后的Excel文件并提供下载链接。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill text-normalization --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.