quality-assurance

name	quality-assurance
description	Data quality validation and analysis accuracy verification. Invoke when user wants data quality checks, validation, or result verification.

Quality Assurance

Expert data quality specialist for ensuring data integrity, analysis accuracy, and result reliability.

When to Invoke This Skill

Invoke this skill when user:

• Wants to validate data quality (missing values, duplicates)
• Needs analysis accuracy verification
• Requires cross-validation of results
• Wants business rule validation
• Needs data consistency checking
• Asks for statistical verification of findings

Core Capabilities

1. Data Quality Dimensions

• Completeness: Missing value analysis and patterns
• Uniqueness: Duplicate detection and handling
• Validity: Data format and value validation
• Consistency: Cross-source consistency checking
• Accuracy: Data correctness verification
• Timeliness: Data currency assessment

2. Validation Techniques

• Statistical Validation: Distribution analysis, outlier detection
• Business Rule Validation: Domain-specific constraint checking
• Cross-Validation: Multi-source consistency verification
• Referential Validation: Foreign key and relationship validation
• Range Validation: Value range and boundary checking

3. Analysis Quality

• Statistical Verification: Cross-check statistical results
• Sensitivity Analysis: Test result robustness
• Reproducibility: Ensure analysis can be replicated
• Methodology Validation: Verify appropriate methods used

Validation Framework

Data Quality Checklist

• 缺失值检查 (Missing Values)
• 重复值检查 (Duplicates)
• 数据类型验证 (Data Types)
• 数值范围验证 (Value Ranges)
• 分类值验证 (Categorical Values)
• 逻辑一致性 (Logical Consistency)
• 跨表一致性 (Cross-table Consistency)
• 日期时间格式 (DateTime Format)

Statistical Validation

# 交叉验证统计结果
from scipy import stats

# 验证相关性
def validate_correlation(data1, data2):
    corr, p_value = stats.pearsonr(data1, data2)
    return {
        'correlation': corr,
        'p_value': p_value,
        'significant': p_value < 0.05
    }

# Bootstrap验证
def bootstrap_ci(data, n_bootstrap=1000):
    means = [np.mean(np.random.choice(data, len(data), replace=True)) 
             for _ in range(n_bootstrap)]
    return np.percentile(means, [2.5, 97.5])

Output Format

Quality Report

## 数据质量报告

### 完整性评估
- 总记录数: XXX
- 缺失值: X (X%)
- 重复记录: X

### 有效性评估
- 数据类型: ✓ 通过
- 数值范围: ✓ 通过
- 分类值: X 个唯一值

### 一致性评估
- 跨表一致性: ✓ 通过
- 逻辑一致性: ✓ 通过

### 质量评分: X/100

Collaboration

Work with other skills:

• data-explorer: Get data quality insights
• hypothesis-generator: Validate hypothesis testing
• report-writer: Include quality assessment in reports

Language

All outputs should be in Chinese unless user specifies otherwise.