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.zipInstalls 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.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
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
| 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
- 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.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| juicebox-core-workflow-b (this skill) | 1 | 27d | Review | Advanced |
| openevidence-core-workflow-b | 0 | 27d | Review | Advanced |
| pi-share | 1 | 5mo | Review | Beginner |
| web-scraper | 0 | 1mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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