I need to explore the dataset systematically to find surprising patterns. Let me start by understanding the overall landscape and then look for counterintuitive relationships. ```json { "action": "tool_call", "reasoning": "First, I'll get a comprehensive profile of the key measures and their distributions to understand what's typical in this AI job market dataset. This will help me identify what's surprising by establishing baselines.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_jobs, MIN(salary_usd) as min_salary, MAX(salary_usd) as max_salary, AVG(salary_usd) as avg_salary, MEDIAN(salary_usd) as median_salary, STDDEV(salary_usd) as stddev_salary, MIN(years_experience) as min_exp, MAX(years_experience) as max_exp, AVG(years_experience) as avg_exp, MEDIAN(years_experience) as median_exp, MIN(benefits_score) as min_benefits, MAX(benefits_score) as max_benefits, AVG(benefits_score) as avg_benefits, MEDIAN(benefits_score) as median_benefits, COUNT(DISTINCT job_title) as distinct_titles, COUNT(DISTINCT company_name) as distinct_companies, COUNT(DISTINCT industry) as distinct_industries FROM raw.ai_job_dataset", "purpose": "Get overall distribution statistics for key measures to establish baselines", "source": "raw" } } ```