Configure Exa CI/CD integration with GitHub Actions and testing. Use when setting up automated testing, configuring CI pipelines, or integrating Exa tests into your build process. Trigger with phrases like "exa CI", "exa GitHub Actions", "exa automated tests", "CI exa".
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
skills listSkill Instructions
name: exa-ci-integration description: | Configure Exa CI/CD integration with GitHub Actions and testing. Use when setting up automated testing, configuring CI pipelines, or integrating Exa tests into your build process. Trigger with phrases like "exa CI", "exa GitHub Actions", "exa automated tests", "CI exa". allowed-tools: Read, Write, Edit, Bash(gh:*) version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io
Exa CI Integration
Overview
Set up CI/CD pipelines for Exa integrations with automated testing.
Prerequisites
- GitHub repository with Actions enabled
- Exa test API key
- npm/pnpm project configured
Instructions
Step 1: Create GitHub Actions Workflow
Create .github/workflows/exa-integration.yml:
name: Exa Integration Tests
on:
push:
branches: [main]
pull_request:
branches: [main]
env:
EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
jobs:
test:
runs-on: ubuntu-latest
env:
EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: '20'
cache: 'npm'
- run: npm ci
- run: npm test -- --coverage
- run: npm run test:integration
Step 2: Configure Secrets
gh secret set EXA_API_KEY --body "sk_test_***"
Step 3: Add Integration Tests
describe('Exa Integration', () => {
it.skipIf(!process.env.EXA_API_KEY)('should connect', async () => {
const client = getExaClient();
const result = await client.healthCheck();
expect(result.status).toBe('ok');
});
});
Output
- Automated test pipeline
- PR checks configured
- Coverage reports uploaded
- Release workflow ready
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Secret not found | Missing configuration | Add secret via gh secret set |
| Tests timeout | Network issues | Increase timeout or mock |
| Auth failures | Invalid key | Check secret value |
Examples
Release Workflow
on:
push:
tags: ['v*']
jobs:
release:
runs-on: ubuntu-latest
env:
EXA_API_KEY: ${{ secrets.EXA_API_KEY_PROD }}
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: '20'
- run: npm ci
- name: Verify Exa production readiness
run: npm run test:integration
- run: npm run build
- run: npm publish
Branch Protection
required_status_checks:
- "test"
- "exa-integration"
Resources
Next Steps
For deployment patterns, see exa-deploy-integration.
More by jeremylongshore
View allOauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.
Rabbitmq 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").
