Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Bwkyd/wps-skills --skill wps-data-cleangit clone --depth 1 https://github.com/Bwkyd/wps-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bwkyd/wps-skills/wps-data-clean)<a href="https://agentmods.dev/skills/bwkyd/wps-skills/wps-data-clean"><img src="https://agentmods.dev/badge/skills/bwkyd/wps-skills/wps-data-clean/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bwkyd/wps-skills/wps-data-clean"><img src="https://agentmods.dev/badge/skills/bwkyd/wps-skills/wps-data-clean.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00075 | $0.02455 |
| Opus 5.5 | $0.00030 | $0.00982 |
| Sonnet 5.5 | $0.00015 | $0.00491 |
| Haiku 4.5 | $0.00007 | $0.00246 |
Grade A, and why
wps-data-clean scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WPS 数据清洗工具
一键清洗杂乱的表格数据，让脏数据变成可用数据。
When to Use
- 表格数据有大量重复、空行、格式不统一
- 手机号、身份证号、日期格式混乱
- 需要合并/拆分单元格内容
- 从外部系统导出的数据需要整理
- 用户说"帮我清洗/整理这个表格"
When NOT to Use
- 需要写公式 → 使用
wps-formula - 需要数据分析/透视 → 使用
wps-pivot - 需要图表可视化 → 使用
wps-chart
清洗能力清单
✅ 基础清洗
| 操作 | 说明 | 适用场景 |
|---|---|---|
| 去重 | 按指定列删除重复行 | 导入数据有重复记录 |
| 去空行 | 删除全空行或指定列为空的行 | 数据中间有空行 |
| 去空格 | 去除首尾空格、多余空格 | 文本前后有隐藏空格 |
| 去特殊字符 | 清除不可见字符、换行符 | 从网页/PDF复制的数据 |
| 统一大小写 | 全大写/全小写/首字母大写 | 英文数据不统一 |
| 繁简转换 | 繁体中文→简体中文 | 港台数据导入 |
✅ 中国特色数据标准化
| 数据类型 | 清洗规则 | 示例 |
|---|---|---|
| 手机号 | 去空格/横杠，补+86，验证11位 | 138-1234-5678 → 13812345678 |
| 身份证号 | 去空格，验证18/15位，校验末位 | 110101 1990 0101 001X → 11010119900101001X |
| 日期 | 统一为 YYYY-MM-DD 或 YYYY年M月D日 |
2024/3/5 2024.03.05 → 2024-03-05 |
| 金额 | 去逗号/¥/元，统一为数值 | ¥1,234.56元 → 1234.56 |
| 性别 | 统一为 男/女 |
M/F male/female 1/0 → 男/女 |
| 省市区 | 补全省市区层级 | 朝阳区 → 北京市朝阳区 |
| 姓名 | 去空格，去称呼后缀 | 张 三先生 → 张三 |
| 邮箱 | 去空格，转小写，验证格式 | [email protected] → [email protected] |
✅ 结构性清洗
| 操作 | 说明 |
|---|---|
| 拆分列 | 一列拆多列（如姓名→姓+名，地址→省+市+区） |
| 合并列 | 多列合一列（如姓+名→姓名） |
| 取消合并单元格 | 合并单元格→填充每个子单元格 |
| 转置 | 行列互换 |
| 宽表转长表 | 多列月份→日期+值的两列 |
| 长表转宽表 | 日期+值→多列月份 |
工作流程
Step 1: 诊断数据问题
如果用户提供了文件：
pip install openpyxl 2>/dev/null || pip3 install openpyxl 2>/dev/null
读取文件并输出数据诊断报告：
📊 数据诊断报告
═══════════════════════════════════
文件：[文件名]
工作表：[Sheet名]
行数：[X] 行（含表头）
列数：[X] 列
🔍 发现的问题：
┌─────────────────────────────────┐
│ ⚠️ 重复行：XX 行（占比 XX%） │
│ ⚠️ 空行：XX 行 │
│ ⚠️ A列有 XX 个空值 │
│ ⚠️ B列(手机号)格式不统一：XX 个 │
│ ⚠️ C列(日期)格式混乱：XX 个 │
│ ✅ D列(金额)格式正常 │
└─────────────────────────────────┘
📋 建议清洗操作：
1. 删除 XX 行完全重复数据
2. 删除 XX 行空行
3. 标准化手机号为11位数字
4. 统一日期格式为 YYYY-MM-DD
Step 2: 确认清洗方案
向用户展示诊断报告，确认要执行的清洗操作。
Step 3: 执行清洗
根据场景选择技术路线：
| 场景 | 技术路线 | 输出 |
|---|---|---|
| 有 .xlsx 文件在磁盘上 | openpyxl脚本 | 清洗后的新 .xlsx 文件 |
| 用户在WPS中打开了表格 | JSA宏 | 粘贴到WPS宏编辑器运行 |
| 仅描述需求，无文件 | JSA宏模板 | .js文件供用户使用 |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 265 lines · 75 tokens per session scan A 438586a02342
wps-data-clean is a skill published in the GitHub repository Bwkyd/wps-skills (10 stars, last pushed 5mo ago), licensed MIT. It adds 75 tokens to every session and 2,455 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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