I need to identify time dimensions and analyze them for spikes, dips, or breaks. From the evidence, I can see multiple temporal columns across tables, and there are gold tables with temporal aggregations. Let me start by examining the gold tables that aggregate by time to find the most significant patterns. {"action": "tool_call", "reasoning": "The gold tables summary shows temporal aggregations at hourly granularity. I'll query the llm_calls temporal gold table first since it has the most rows (2972) and likely shows the most activity patterns. This will reveal spikes/dips in LLM usage over time.", "tool": "query_sql", "args": {"sql": "SELECT created_at__yyyy_mm_dd_hh, sum_row_count, avg_row_count FROM llm_calls_by_created_at__yyyy_mm_dd_hh_purpose ORDER BY created_at__yyyy_mm_dd_hh", "purpose": "Get hourly LLM call volumes to identify temporal patterns", "source": "gold"}}