OpenClaw

data-quality-check

Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.

datadrivenconstruction0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install datadrivenconstruction/data-quality-check

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Data Quality Check for Construction

Overview

Based on DDC methodology (Chapter 2.6), this skill provides comprehensive data quality assessment for construction projects. Poor data quality leads to poor decisions - validate early, validate often.

Book Reference: "Требования к качеству данных и его обеспечение" / "Data Quality Requirements"

"Качество данных определяется пятью ключевыми метриками: полнота, точность, согласованность, своевременность и достоверность." — DDC Book, Chapter 2.6

Quick Start

import pandas as pd

# Load construction data
df = pd.read_excel("bim_export.xlsx")

# Quick quality check
quality_score = {
    'completeness': (1 - df.isnull().sum().sum() / df.size) * 100,
    'unique_ids': df['ElementId'].nunique() == len(df),
    'valid_volumes': (df['Volume_m3'] >= 0).all()
}

print(f"Completeness: {quality_score['completeness']:.1f}%")
print(f"Unique IDs: {quality_score['unique_ids']}")
print(f"Valid volumes: {quality_score['valid_volumes']}")

Data Quality Dimensions

The 5 Quality Metrics

import pandas as pd
import numpy as np
import re
from datetime import datetime, timedelta

class DataQualityChecker:
    """Comprehensive data quality assessment for construction data"""

    def __init__(self, df):
        self.df = df.copy()
        self.results = {}
        self.issues = []

    def check_completeness(self, required_columns=None):
        """Check for missing values (Полнота)"""
        if required_columns is None:
            required_columns = self.df.columns.tolist()

        completeness = {}
        for col in required_columns:
            if col in self.df.columns:
                non_null = self.df[col].notna().sum()
                total = len(self.df)
                completeness[col] = (non_null / total) * 100
            else:
                completeness[col] = 0
                self.issues.append(f"Missing required...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/datadrivenconstruction/data-quality-check
Version: —
Commit: 4b4478f81dd5d5e2372960454acd07b0ef92942c
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Data Quality Check for Construction

## Overview

Based on DDC methodology (Chapter 2.6), this skill provides comprehensive data quality assessment for construction projects. Poor data quality leads to poor decisions - validate early, validate often.

**Book Reference:** "Требования к качеству данных и его обеспечение" / "Data Quality Requirements"

> "Качество данных определяется пятью ключевыми метриками: полнота, точность, согласованность, своевременность и достоверность."
> — DDC Book, Chapter 2.6

## Quick Start

```python
import pandas as pd

# Load construction data
df = pd.read_excel("bim_export.xlsx")

# Quick quality check
quality_score = {
    'completeness': (1 - df.isnull().sum().sum() / df.size) * 100,
    'unique_ids': df['ElementId'].nunique() == len(df),
    'valid_volumes': (df['Volume_m3'] >= 0).all()
}

print(f"Completeness: {quality_score['completeness']:.1f}%")
print(f"Unique IDs: {quality_score['unique_ids']}")
print(f"Valid volumes: {quality_score['valid_volumes']}")
```

## Data Quality Dimensions

### The 5 Quality Metrics

```python
import pandas as pd
import numpy as np
import re
from datetime import datetime, timedelta

class DataQualityChecker:
    """Comprehensive data quality assessment for construction data"""

    def __init__(self, df):
        self.df = df.copy()
        self.results = {}
        self.issues = []

    def check_completeness(self, required_columns=None):
        """Check for missing values (Полнота)"""
        if required_columns is None:
            required_columns = self.df.columns.tolist()

        completeness = {}
        for col in required_columns:
            if col in self.df.columns:
                non_null = self.df[col].notna().sum()
                total = len(self.df)
                completeness[col] = (non_null / total) * 100
            else:
                completeness[col] = 0
                self.issues.append(f"Missing required...

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