quality-assurance

Testing & Quality

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

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/liangdabiao/claude-data-analysis-ultra-main/blob/HEAD/.claude/skills/quality-assurance/SKILL.md

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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.