juicebox-local-dev-loop
Configure Juicebox local development workflow. Use when setting up local testing, mock data, or development environment for Juicebox integration work. Trigger with phrases like "juicebox local dev", "juicebox development setup", "juicebox mock data", "test juicebox locally".
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
skills listSkill Instructions
name: juicebox-local-dev-loop description: | Configure Juicebox local development workflow. Use when setting up local testing, mock data, or development environment for Juicebox integration work. Trigger with phrases like "juicebox local dev", "juicebox development setup", "juicebox mock data", "test juicebox locally". allowed-tools: Read, Write, Edit, Bash(npm:), Bash(pip:), Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io
Juicebox Local Dev Loop
Overview
Configure a fast local development workflow for Juicebox integration with mock data and testing utilities.
Prerequisites
- Juicebox SDK installed
- Node.js or Python environment
- Test API key (sandbox mode)
Instructions
Step 1: Configure Development Environment
# Create development config
cat > .env.development << 'EOF'
JUICEBOX_API_KEY=jb_test_xxxxxxxxxxxx
JUICEBOX_ENVIRONMENT=sandbox
JUICEBOX_LOG_LEVEL=debug
EOF
Step 2: Set Up Mock Data
// mocks/juicebox.ts
export const mockProfiles = [
{
id: 'mock-1',
name: 'Test User',
title: 'Software Engineer',
company: 'Test Corp',
location: 'San Francisco, CA'
}
];
export const mockSearchResponse = {
total: 1,
profiles: mockProfiles,
hasMore: false
};
Step 3: Create Test Utilities
// test-utils/juicebox.ts
import { JuiceboxClient } from '@juicebox/sdk';
export function createTestClient() {
return new JuiceboxClient({
apiKey: process.env.JUICEBOX_API_KEY,
sandbox: true,
timeout: 5000
});
}
export async function withMockSearch<T>(
fn: (client: JuiceboxClient) => Promise<T>
): Promise<T> {
const client = createTestClient();
return fn(client);
}
Step 4: Hot Reload Setup
// package.json
{
"scripts": {
"dev": "nodemon --watch src --exec ts-node src/index.ts",
"test:watch": "vitest watch"
}
}
Output
- Development environment configuration
- Mock data for offline testing
- Test utilities for integration tests
- Hot reload for rapid iteration
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Sandbox Limit | Exceeded test quota | Wait or upgrade plan |
| Mock Mismatch | Schema changed | Update mock data |
| Hot Reload Fail | File lock | Restart dev server |
Examples
Integration Test
import { describe, it, expect } from 'vitest';
import { createTestClient } from './test-utils/juicebox';
describe('Juicebox Search', () => {
it('returns profiles for valid query', async () => {
const client = createTestClient();
const results = await client.search.people({
query: 'engineer',
limit: 5
});
expect(results.profiles.length).toBeGreaterThan(0);
});
});
Resources
Next Steps
With local dev configured, explore juicebox-sdk-patterns for production patterns.
More by jeremylongshore
View allRabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.
evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".
building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").
Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.