openevidence-ci-integration
Ci Integration for OpenEvidence. Trigger: "openevidence ci integration".
Best use case
openevidence-ci-integration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Ci Integration for OpenEvidence. Trigger: "openevidence ci integration".
Teams using openevidence-ci-integration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openevidence-ci-integration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How openevidence-ci-integration Compares
| Feature / Agent | openevidence-ci-integration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Ci Integration for OpenEvidence. Trigger: "openevidence ci integration".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# OpenEvidence CI Integration
## Overview
Set up CI/CD for OpenEvidence clinical decision support integrations: run unit tests with mocked evidence query and citation responses on every PR, validate live API connectivity for clinical queries on merge to main. OpenEvidence provides AI-powered medical evidence retrieval and clinical decision support, so CI pipelines verify query formatting, evidence parsing, citation extraction, and response quality scoring.
## GitHub Actions Workflow
```yaml
# .github/workflows/openevidence-ci.yml
name: OpenEvidence CI
on:
pull_request:
paths: ['src/openevidence/**', '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:
OPENEVIDENCE_API_KEY: ${{ secrets.OPENEVIDENCE_API_KEY }}
```
## Mock-Based Unit Tests
```typescript
// tests/openevidence-service.test.ts
import { describe, it, expect, vi } from 'vitest';
import { queryEvidence, extractCitations } from '../src/openevidence-service';
vi.mock('../src/openevidence-client', () => ({
OpenEvidenceClient: vi.fn().mockImplementation(() => ({
query: vi.fn().mockResolvedValue({
answer: 'Current evidence supports early intervention with GLP-1 agonists...',
confidence: 0.92,
citations: [
{ title: 'NEJM 2025 Meta-Analysis', doi: '10.1056/NEJMoa2501234', year: 2025 },
{ title: 'Lancet Diabetes Review', doi: '10.1016/S2213-8587(25)00123', year: 2025 },
],
evidenceLevel: 'high',
}),
listQueries: vi.fn().mockResolvedValue({
queries: [{ id: 'q_abc', question: 'GLP-1 efficacy', status: 'completed' }],
}),
})),
}));
describe('OpenEvidence Service', () => {
it('queries clinical evidence with citations', async () => {
const result = await queryEvidence('GLP-1 agonist efficacy for type 2 diabetes');
expect(result.confidence).toBeGreaterThan(0.9);
expect(result.citations).toHaveLength(2);
});
it('extracts citation DOIs from response', async () => {
const citations = await extractCitations('q_abc');
expect(citations[0].doi).toMatch(/^10\.\d+/);
});
});
```
## Integration Tests
```typescript
// tests/integration/openevidence.integration.test.ts
import { describe, it, expect } from 'vitest';
const hasKey = !!process.env.OPENEVIDENCE_API_KEY;
describe.skipIf(!hasKey)('OpenEvidence Live API', () => {
it('queries clinical evidence', async () => {
const res = await fetch('https://api.openevidence.com/v1/query', {
method: 'POST',
headers: {
'Authorization': `Bearer ${process.env.OPENEVIDENCE_API_KEY}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({ question: 'Aspirin dosing for secondary prevention' }),
});
expect(res.status).toBe(200);
const body = await res.json();
expect(body).toHaveProperty('answer');
expect(body).toHaveProperty('citations');
});
});
```
## Error Handling
| CI Issue | Cause | Fix |
|----------|-------|-----|
| `401 Unauthorized` | Invalid API key | Regenerate at openevidence.com account settings |
| Empty citations array | Query too vague for evidence matching | Use specific clinical terms with condition and intervention |
| Low confidence score | Insufficient published evidence | Check evidence level field and handle `low` confidence gracefully |
| Rate limit (429) | Too many queries in test suite | Add throttling between clinical queries (1 req/sec) |
| Response timeout | Complex query requiring deep search | Increase fetch timeout to 30s for clinical evidence lookups |
## Resources
- [OpenEvidence Platform](https://www.openevidence.com/)
- [OpenEvidence API Documentation](https://docs.openevidence.com/)
- [GitHub Actions Secrets](https://docs.github.com/en/actions/security-guides/encrypted-secrets)
## Next Steps
See `openevidence-deploy-integration`.Related Skills
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