{
    "action": "tool_call",
    "reasoning": "I've already examined basic statistics and null patterns. Now I need to check for specific data quality issues: (1) inconsistent formats in text fields like dates/numbers stored as strings, (2) outliers in numeric distributions, (3) mixed units or scales, (4) duplicate records, (5) referential integrity issues. Let me start by examining the distribution of key numeric fields to identify outliers, and check for duplicate records.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) as total_records, COUNT(DISTINCT id_runs) as distinct_ids, COUNT(*) - COUNT(DISTINCT id_runs) as duplicate_ids, COUNT(DISTINCT CONCAT(task, '|', model, '|', started_at)) as distinct_task_model_time FROM raw.epoch_ai_benchmarks",
        "purpose": "Check for duplicate records by comparing total rows to distinct IDs and distinct task-model-time combinations"
    }
}