{
    "action": "tool_call",
    "reasoning": "I've already checked for negative values (none found) and null counts. Now I need to examine potential outliers and inconsistencies. Let me look at the distribution of key measures to identify outliers, and check for format/unit inconsistencies in text fields like dates, costs, and power capacity. I'll start by examining the distribution of major numeric fields to spot outliers.",
    "tool": "query_sql",
    "source": "raw",
    "purpose": "Examine distributions and potential outliers in key numeric measures",
    "sql": "SELECT 'Max OP/s (log)' as metric, MIN(`Max OP/s (log)`) as min_val, MAX(`Max OP/s (log)`) as max_val, AVG(`Max OP/s (log)`) as avg_val, MEDIAN(`Max OP/s (log)`) as median_val, STDDEV(`Max OP/s (log)`) as stddev_val, COUNT(*) as non_null_count FROM raw.ai_supercomputers WHERE `Max OP/s (log)` IS NOT NULL UNION ALL SELECT 'H100 equivalents', MIN(`H100 equivalents`), MAX(`H100 equivalents`), AVG(`H100 equivalents`), MEDIAN(`H100 equivalents`), STDDEV(`H100 equivalents`), COUNT(*) FROM raw.ai_supercomputers WHERE `H100 equivalents` IS NOT NULL UNION ALL SELECT 'Power Capacity (MW)', MIN(`Power Capacity (MW)`), MAX(`Power Capacity (MW)`), AVG(`Power Capacity (MW)`), MEDIAN(`Power Capacity (MW)`), STDDEV(`Power Capacity (MW)`), COUNT(*) FROM raw.ai_supercomputers WHERE `Power Capacity (MW)` IS NOT NULL UNION ALL SELECT 'Hardware Cost', MIN(`Hardware Cost`), MAX(`Hardware Cost`), AVG(`Hardware Cost`), MEDIAN(`Hardware Cost`), STDDEV(`Hardware Cost`), COUNT(*) FROM raw.ai_supercomputers WHERE `Hardware Cost` IS NOT NULL"
}