smithery/jeremylongshore

juicebox-ci-integration

Configure Juicebox CI/CD. '

Installation

$ npx skills add smithery/jeremylongshore --skill juicebox-ci-integration

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Skill metadata

Parsed from SKILL.md frontmatter.

Version1.16.0
LicenseMIT
CompatibilityDesigned for Claude Code
Allowed toolsRead, Write, Edit, Bash(npm:*), Grep
Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,419 B
  • docs SUMMARY.md 335 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Juicebox CI Integration

Overview

Set up CI/CD for Juicebox AI data analysis integrations: run unit tests with mocked dataset and analysis responses on every PR, validate live API connectivity for data queries on merge to main. Juicebox provides AI-powered data exploration and visualization, so CI pipelines verify dataset upload logic, analysis execution, and result parsing workflows.

GitHub Actions Workflow

# .github/workflows/juicebox-ci.yml
name: Juicebox CI
on:
  pull_request:
    paths: ['src/juicebox/**', 'tests/**']
  push:
    branches: [main]

jobs:
  unit-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20' }
      - run: npm ci
      - run: npm test -- --reporter=verbose

  integration-tests:
    if: github.ref == 'refs/heads/main'
    needs: unit-tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20' }
      - run: npm ci
      - run: npm run test:integration
        env:
          JUICEBOX_API_KEY: ${{ secrets.JUICEBOX_API_KEY }}

Mock-Based Unit Tests

// tests/juicebox-service.test.ts
import { describe, it, expect, vi } from 'vitest';
import { analyzeDataset, getAnalysisResults } from '../src/juicebox-service';

vi.mock('../src/juicebox-client', () => ({
  JuiceboxClient: vi.fn().mockImplementation(() => ({
    createAnalysis: vi.fn().mockResolvedValue({
      analysisId: 'ana_abc123',
      status: 'processing',
      datasetId: 'ds_xyz',
    }),
    getAnalysis: vi.fn().mockResolvedValue({
      analysisId: 'ana_abc123',
      status: 'completed',
      results: {
        summary: 'Revenue increased 15% QoQ',
        charts: [{ type: 'bar', title: 'Revenue by Quarter' }],
        insights: ['Q4 drove majority of growth', 'APAC region outperformed'],
      },
    }),
    listDatasets: vi.fn().mockResolvedValue({
      datasets: [{ id: 'ds_xyz', name: 'Sales Data', rowCount: 50000 }],
    }),
  })),
}));

describe('Juicebox Service', () => {
  it('creates an analysis from dataset', async () => {
    const result = await analyzeDataset('ds_xyz', 'What drove revenue growth?');
    expect(result.analysisId).toBe('ana_abc123');
    expect(result.status).toBe('processing');
  });

  it('retrieves completed analysis with insights', async () => {
    const results = await getAnalysisResults('ana_abc123');
    expect(results.status).toBe('completed');
    expect(results.results.insights).toHaveLength(2);
  });
});

Integration Tests

// tests/integration/juicebox.integration.test.ts
import { describe, it, expect } from 'vitest';

const hasKey = !!process.env.JUICEBOX_API_KEY;

describe.skipIf(!hasKey)('Juicebox Live API', () => {
  it('lists available datasets', async () => {
    const res = await fetch('https://api.juicebox.ai/v1/datasets', {
      headers: { Authorization: `Bearer ${process.env.JUICEBOX_API_KEY}` },
    });
    expect(res.status).toBe(200);
    const body = await res.json();
    expect(body).toHaveProperty('datasets');
  });
});

Error Handling

CI Issue Cause Fix
401 Unauthorized Invalid API key Regenerate at juicebox.ai account settings
Analysis stuck on processing Large dataset or complex query Increase polling timeout to 120s
Dataset not found (404) Dataset ID changed or deleted Use listDatasets to get a valid ID dynamically
Rate limit (429) Too many concurrent analyses Queue analyses and limit to 2 parallel runs
Empty insights array Insufficient data for AI analysis Ensure test dataset has 100+ rows with varied data

Prerequisites

  • CI secret references, a sandbox workspace, synthetic prospects, protected branches, source-authority checks, and a rollback mechanism.

Instructions

  1. Run mocked tests first, including unknown source, denied scope, suppression, malformed enrichment, quota, and export-block cases.
  2. Run a bounded sandbox integration with idempotency; prohibit production sources/destinations and literal credentials in CI.
  3. Emit aggregate counts, opaque IDs, and policy revisions only; fail for unapproved source, expanded scope, contact-data leakage, or export path.
  4. Canary after protected review, verify contacts_exported=0, and restore the last-known-good revision on failure.

Output

Publish a CI receipt with commit SHA, fixture revision, sandbox workspace, test totals, source/destination/suppression checks, export-count assertion, canary outcome, and rollback reference. Exclude contacts, enrichment fields, and secrets.

Examples

sha=abc123; fixtures=v5; workspace=ci-synthetic; tests=18/18; source=approved; suppression=pass; contacts_exported=0; canary=not-promoted is a valid pre-production receipt.

Resources

Next Steps

See juicebox-deploy-integration.