JU

juicebox-core-workflow-b

Performs custom data queries and multi-dimensional analysis on Juicebox candidate datasets.

Install

mkdir -p .claude/skills/juicebox-core-workflow-b && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3387" && unzip -o skill.zip -d .claude/skills/juicebox-core-workflow-b && rm skill.zip

Installs to .claude/skills/juicebox-core-workflow-b

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Execute Juicebox enrichment and outreach workflow.
50 charsno explicit “when” trigger
Advanced

Key capabilities

  • Build custom queries with multi-dimensional filters on candidate data.
  • Run cross-dataset comparisons on talent pools.
  • Aggregate skill density by region.
  • Export analysis results in CSV format.
  • Analyze talent trends over time.

How it works

The skill allows building custom queries with filters, running cross-dataset comparisons, and aggregating skill density on Juicebox data, then exporting the results.

Inputs & outputs

You give it
Juicebox people-intelligence data, including filters, datasets for comparison, and skill filters.
You get back
Filtered candidate lists, cross-dataset comparison tables, regional skill-density rankings, and exportable CSV files.

When to use juicebox-core-workflow-b

  • Enriching candidate profiles
  • Analyzing talent skill density
  • Comparing candidate pools across roles
  • Identifying recruitment trends

About this skill

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

IssueCauseFix
400 Invalid filterUnsupported operator for field typeCheck field schema with client.schema.fields()
404 Dataset not foundStale dataset ID or typoList datasets with client.datasets.list()
408 Query timeoutToo many filters on large datasetAdd limit or narrow date range
429 Rate limitedExceeded analysis quotaImplement backoff; check plan limits
Partial comparison dataOne dataset has sparse coverageExpected — 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

  • Juicebox API Docs

Next Steps

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

When not to use it

  • When only basic search and enrichment are needed (use `juicebox-core-workflow-a`).
  • When the dataset is not found or is stale.

Limitations

  • Unsupported operators for field types can cause invalid filter errors.
  • Too many filters on a large dataset can lead to query timeouts.
  • Exceeding analysis quota can result in rate limiting.

How it compares

This skill provides advanced analytical capabilities like cross-dataset comparison and skill density aggregation, which go beyond standard search functions.

Compared to similar skills

juicebox-core-workflow-b side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
juicebox-core-workflow-b (this skill)127dReviewAdvanced
openevidence-core-workflow-b027dReviewAdvanced
pi-share15moReviewBeginner
web-scraper01moNo flagsIntermediate

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