{
    "action": "tool_call",
    "reasoning": "I have extensive evidence on ranges, nulls, and date formats. Now I need to check for specific data quality issues: (1) outliers in key measures using statistical methods, (2) inconsistent categorical values, (3) mixed units or formatting issues in text fields, and (4) logical inconsistencies (e.g., systems with chips but no power/cost). Let me start by examining the distribution of key measures to identify statistical outliers using IQR method.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH stats AS (SELECT PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY \"H100 equivalents\") as q1_h100, PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY \"H100 equivalents\") as q3_h100, PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY \"Chip quantity (primary)\") as q1_chips, PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY \"Chip quantity (primary)\") as q3_chips, PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY \"Power Capacity (MW)\") as q1_power, PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY \"Power Capacity (MW)\") as q3_power FROM raw.ai_supercomputers WHERE \"H100 equivalents\" IS NOT NULL OR \"Chip quantity (primary)\" IS NOT NULL OR \"Power Capacity (MW)\" IS NOT NULL) SELECT q1_h100, q3_h100, (q3_h100 - q1_h100) * 1.5 as iqr_h100, q3_h100 + (q3_h100 - q1_h100) * 1.5 as upper_fence_h100, q1_chips, q3_chips, (q3_chips - q1_chips) * 1.5 as iqr_chips, q3_chips + (q3_chips - q1_chips) * 1.5 as upper_fence_chips, q1_power, q3_power, (q3_power - q1_power) * 1.5 as iqr_power, q3_power + (q3_power - q1_power) * 1.5 as upper_fence_power FROM stats",
        "purpose": "Calculate IQR-based outlier thresholds for key measures to identify extreme values",
        "source": "raw"
    }
}