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 serejaris/kimi-skills --skill dataset-health-auditgit clone --depth 1 https://github.com/serejaris/kimi-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/serejaris/kimi-skills/dataset-health-audit)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/dataset-health-audit"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/dataset-health-audit/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/serejaris/kimi-skills/dataset-health-audit"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/dataset-health-audit.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 | $0.00107 | $0.01087 |
| Opus 5 | $0.00053 | $0.00544 |
| Sonnet 5 | $0.00021 | $0.00217 |
| Haiku 4.5 | $0.00011 | $0.00109 |
Grade A, and why
dataset-health-audit 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 9d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
data-quality-checker
数据质检工具 —— 对表格数据执行 12 个维度的质量检测，输出每项评分（0-100）、总分和具体修复建议。
能力概览
| 维度 | 说明 |
|---|---|
| 缺失值检测 | 每列的空值/NaN 数量与比例 |
| 重复行检测 | 完全重复的行数与比例 |
| 数据类型一致性 | 同列中混杂不同类型（如数字列混入文字） |
| 数值范围/异常值 | 基于 IQR 方法检测离群值 |
| 格式合规性 | 日期、邮箱、手机号等字段的格式一致性 |
| 唯一性约束 | ID 类字段是否存在重复 |
| 空白字符串 | 前后空格、空字符串、仅空白字符 |
| 常量列 | 仅含单一值的列（信息量为零） |
| 数据分布偏斜 | 数值列的偏度是否过大 |
| 列名规范性 | 列名是否含空格、特殊字符、大小写不一致 |
| 基数异常 | 唯一值数量异常（过高或过低） |
| 跨列一致性 | 日期先后、数值大小等跨列逻辑校验 |
Quick Start
# 基本质检
python3 scripts/data_quality_checker.py data.csv
# 保存报告到 JSON
python3 scripts/data_quality_checker.py data.csv --output report.json
# 指定 ID 列（用于唯一性检查）
python3 scripts/data_quality_checker.py users.csv --id-columns "user_id,email"
# 指定日期列（用于格式检查）
python3 scripts/data_quality_checker.py orders.csv --date-columns "created_at,updated_at"
详细用法
基本调用
python3 scripts/data_quality_checker.py <数据文件> [选项]
参数说明
| 参数 | 缩写 | 必填 | 默认值 | 说明 |
|---|---|---|---|---|
input |
— | 是 | — | 输入文件路径（CSV/TSV/Excel/JSON） |
--output |
-o |
否 | 标准输出 | 输出 JSON 报告路径 |
--id-columns |
-id |
否 | 自动检测 | 应唯一的列名，逗号分隔 |
--date-columns |
-dc |
否 | 自动检测 | 日期类型的列名，逗号分隔 |
--sample |
-s |
否 | 全量 | 采样行数（大文件时使用） |
--encoding |
-e |
否 | utf-8 | 文件编码 |
输出结构（JSON）
{
"file": "data.csv",
"rows": 10000,
"columns": 15,
"overall_score": 78.5,
"grade": "B",
"dimensions": {
"missing_values": {
"score": 85.0,
"issues": [
{"column": "age", "missing_count": 150, "missing_pct": 1.5, "suggestion": "用中位数或众数填充"}
]
},
"duplicates": {
"score": 95.0,
"issues": [...]
}
},
"top_suggestions": [
"列 age 有 1.5% 缺失值，建议用中位数填充",
"发现 200 行完全重复，建议去重"
]
}
评分标准
| 等级 | 分数范围 | 含义 |
|---|---|---|
| A+ | 95-100 | 数据质量优秀，可直接使用 |
| A | 90-95 | 质量良好，少量小问题 |
| B | 80-90 | 质量中等，建议修复后使用 |
| C | 60-80 | 质量较差，需重点清洗 |
| D | 40-60 | 质量很差，大量问题需修复 |
| F | 0-40 | 数据基本不可用，需重新采集或大规模清洗 |
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.
- 9d ago First seen · 111 lines · 107 tokens per session scan A 0fe6832a0fae
dataset-health-audit is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 107 tokens to every session and 1,087 once invoked, about $0.0005 per session on Opus 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 2026-09-03.
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