I have already obtained comprehensive distribution statistics for all numeric columns across the dataset. Let me present these findings. ```json { "action": "final", "content": "## Numeric Column Distribution Summary\n\nI've analyzed the distribution statistics (min, max, mean, median, and spread) for all numeric columns across the dataset's 43 tables. Here are the key findings:\n\n### Analysis Run & LLM Usage Metrics\n\n**analysis_run_llm_usage table:**\n- **calls**: Range 2-7, mean 4.49, median 4 (fairly symmetric distribution)\n- **cost_usd**: Range $0-$0.14, mean $0.02, median $0 (right-skewed, most runs have minimal cost)\n- **input_tokens**: Range 0-6,566, mean 2,045, median 403 (highly right-skewed with large spread of 2,409)\n- **output_tokens**: Range 0-10,958, mean 1,286, median 286 (extremely right-skewed, spread 1,626)\n\n**analysis_runs table:**\n- **cost_compute_seconds**: Range 0.4-545 seconds, mean 42.6, median 44.8 (relatively symmetric)\n- **cost_storage_bytes**: Range 4.5MB-4.4GB, mean 94MB, median 6MB (highly right-skewed)\n- **queries_executed**: Range 9-104, mean 22.8, median 16 (right-skewed)\n- **total_cost_usd**: Range $0-$0.14, mean $0.05, median $0.06 (fairly symmetric)\n- **total_input_tokens**: Range 0-6,967, mean 3,985, median 5,341 (left-skewed)\n- **total_llm_calls**: Range 0-9, mean 8.74, median 9 (concentrated near maximum)\n- **total_output_tokens**: Range 0-11,119, mean 2,506, median 3,118 (left-skewed)\n- **total_tool_calls**: Range 16-197, mean 41.5, median 30 (right-skewed, spread 40)\n\n### Batch Processing Metrics\n\n**batches table:**\n- **elapsed_ms**: Range 0-1.56M ms (~26 minutes max), mean 59.6 seconds, median 1.3 seconds (extremely right-skewed with massive spread of 192 seconds)\n- **total_bytes**: Range 0-2.7GB, mean 113.5MB, median 1KB (extremely right-skewed, spread 420MB)\n- **total_rows**: Range 0-19.4M rows, mean 941K, median 20 (extremely right-skewed, spread 3.3M)\n\n### Key Patterns Observed\n\n1. **Right-skewed distributions dominate**: Most metrics show mean > median, indicating occasional large values (outliers or heavy processing jobs)\n2. **Token usage varies widely**: Both input and output tokens show high variability (large spreads), suggesting diverse query complexity\n3. **Batch processing is highly variable**: The batches table shows extreme ranges, with most batches being small/fast but some being very large/slow\n4. **Cost metrics are modest**: LLM costs remain under $0.14 per run, with most runs costing very little\n\nThe distribution statistics reveal a system handling both routine small operations and occasional large-scale processing jobs, with LLM usage and batch processing showing the most variability." } ```