juicebox-core-workflow-b

# Juicebox — Advanced Analysis ## Overview Build custom queries, apply multi-dimensional filters, and run cross-dataset analysis on your Juicebox people-intelligence data. Use this workflow when you need to go beyond standard search — comparing candidate pools across roles, analyzing skill density by geography, or identifying talent trends over time. This is the secondary workflow; for basic search and enrichment, see `juicebox-core-workflow-a`. ## Instructions ### Step 1: Build a Custom Query with Filters ```typescript const query = await client.analysis.query({ dataset: 'candidates', filters: [ { field: 'skills', operator: 'contains_any', value: ['TypeScript', 'Rust', 'Go'] }, { field: 'experience_years', operator: 'gte', value: 5 }, { field: 'location.country', operator: 'eq', value: 'US' }, ], sort: { field: 'relevance_score', order: 'desc' }, limit: 100, }); console.log(`Found ${query.total} candidates matching filters`); query.results.forEach(c => console.log(` ${c.name} — ${c.title} (${c.relevance_score}/100)`) ); ``` ### Step 2: Run Cross-Dataset Comparison ```typescript const comparison = await client.analysis.compare({ datasets: ['candidates_q1_2026', 'candidates_q4_2025'], group_by: 'primary_skill', metrics: ['count', 'avg_experience', 'avg_salary_estimate'], }); comparison.groups.forEach(g => console.log(`${g.skill}: Q1=${g.datasets[0].count} vs Q4=${g.datasets[1].count} (${g.delta > 0 ? '+' : ''}${g.delta}%)`) ); ``` ### S...