juicebox-core-workflow-b

Execute Juicebox enrichment and outreach workflow. Trigger: "juicebox enrich", "candidate enrichment", "talent pool".

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juicebox-pack Plugin
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juicebox-pack

Claude Code skill pack for Juicebox (24 skills)

saas packs v1.0.0
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Installation

This skill is included in the juicebox-pack plugin:

/plugin install juicebox-pack@claude-code-plugins-plus

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Instructions

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


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


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}%)`)
);

Step 3: Aggregate Skill Density by Region


const density = await client.analysis.aggregate({
  dataset: 'candidates',
  group_by: 'location.metro_area',
  metric: 'skill_density',
  skill_filter: ['ML Engineering', 'Data Science'],
  top_n: 10,
});
density.regions.forEach(r =>
  console.log(`${r.metro}: ${r.candidate_count} candidates, density=${r.density_score}`)
);

Step 4: Export Analysis Results


const exportJob = await client.analysis.export({
  query_id: query.id,
  format: 'csv',
  fields: ['name', 'email', 'primary_skill', 'experience_years', 'location'],
});
console.log(`Export ready: ${exportJob.download_url} (${exportJob.row_count} rows)`);

Error Handling

Issue Cause Fix
400 Invalid filter Unsupported operator for field type Check field schema with client.schema.fields()
404 Dataset not found Stale dataset ID or typo List datasets with client.datasets.list()
408 Query timeout Too many filters on large dataset Add limit or narrow date range
429 Rate limited Exceeded analysis quota Implement backoff; check plan limits
Partial comparison data One dataset has sparse coverage Expected — use include_nulls: true for completeness

Output

A successful workflow produces filtered candidate lists with relevance scores,

cross-dataset comparison tables showing talent market shifts, and regional

skill-density rankings. Results can be exported as CSV for downstream reporting.

Resources

Next Steps

See juicebox-sdk-patterns for authentication and query builder helpers.

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