OP

openevidence-core-workflow-a

Integrates OpenEvidence search functionality for clinical literature retrieval and decision support.

Install

mkdir -p .claude/skills/openevidence-core-workflow-a && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4116" && unzip -o skill.zip -d .claude/skills/openevidence-core-workflow-a && rm skill.zip

Installs to .claude/skills/openevidence-core-workflow-a

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 OpenEvidence primary workflow: Clinical Query & Decision Support.
73 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Search clinical literature with evidence-level filters
  • Retrieve structured citations with journal and year metadata
  • Check drug interactions against patient context
  • Lookup specialty guidelines from major bodies
  • Provide confidence scores and evidence grading

How it works

The workflow queries clinical literature databases using specified filters and returns structured evidence with confidence metrics. It also checks drug interactions and retrieves guidelines from sources like ACC/AHA, ESC, and NICE.

Inputs & outputs

You give it
Clinical question, specialty, and evidence level
You get back
Evidence-backed answer with citations and confidence score

When to use openevidence-core-workflow-a

  • Implement clinical evidence lookups
  • Build decision support features
  • Filter literature by specialty
  • Retrieve structured citations

About this skill

OpenEvidence — Evidence Search & Retrieval

Overview

Primary workflow for OpenEvidence clinical evidence integration. Covers the core use case: searching clinical literature with evidence-level filters, retrieving structured citations with journal and year metadata, checking drug interactions against patient context, and looking up specialty guidelines from major bodies (ACC/AHA, ESC, NICE). Responses include confidence scores and evidence grading to support clinical decision making. All queries support specialty filtering to narrow results to relevant domains.

Instructions

Step 1: Search Clinical Evidence

const result = await client.query({
  question: 'What is the recommended treatment for acute migraine in adults?',
  context: 'emergency_department',
  evidence_level: 'high',
  specialty: 'neurology',
  max_citations: 10,
});

console.log('Answer:', result.answer);
console.log(`Confidence: ${result.confidence} | Evidence grade: ${result.grade}`);
result.citations.forEach(c =>
  console.log(`  [${c.journal}] ${c.title} (${c.year}) — Level ${c.evidence_level}`)
);

Step 2: Filter by Specialty and Date

const recent = await client.search({
  keywords: 'GLP-1 receptor agonist cardiovascular outcomes',
  specialty: 'cardiology',
  year_min: 2024,
  evidence_level: 'meta-analysis',
  limit: 20,
});
console.log(`Found ${recent.total} results`);
recent.results.forEach(r => console.log(`  ${r.title} (${r.journal}, ${r.year})`));

Step 3: Check Drug Interactions

const interactions = await client.interactions.check({
  medications: ['metformin', 'lisinopril', 'atorvastatin'],
  patient_context: { age: 65, conditions: ['diabetes', 'hypertension'] },
});

interactions.forEach(i =>
  console.log(`${i.drug1} + ${i.drug2}: ${i.severity} — ${i.description}`)
);
if (interactions.some(i => i.severity === 'major')) {
  console.warn('WARNING: Major interaction detected — review before prescribing');
}

Step 4: Guideline Lookup

const guidelines = await client.guidelines.search({
  condition: 'hypertension',
  source: ['ACC/AHA', 'ESC', 'NICE'],
  year_min: 2023,
});
guidelines.forEach(g =>
  console.log(`${g.source}: ${g.title} (${g.year}) — ${g.recommendation_class}`)
);

Error Handling

IssueCauseFix
401 UnauthorizedInvalid API keyVerify key in Authorization: Bearer header
404 Not FoundUnknown specialty codeUse standard specialty slugs from /specialties
422 ValidationConflicting filter paramsRemove mutually exclusive filters
429 Rate LimitedExceeds 30 queries/minBack off per Retry-After header
Empty citations arrayQuestion too narrowBroaden search terms or lower evidence level

Output

A successful run returns evidence-backed answers with citations, drug interaction severity assessments, and guideline recommendations. Each response includes a confidence score and evidence grade for clinical decision support.

Resources

Next Steps

Continue with openevidence-core-workflow-b for patient case analysis and reporting.

When not to use it

  • Queries requiring patient case analysis
  • Reporting tasks

Limitations

  • Empty citations array if question is too narrow
  • Rate limit of 30 queries per minute

How it compares

Unlike manual literature searches, this workflow provides structured, graded evidence and automated drug interaction checks specifically for clinical decision support.

Compared to similar skills

openevidence-core-workflow-a side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
openevidence-core-workflow-a (this skill)125dReviewIntermediate
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biopython17moReviewIntermediate
reference-sdk17moReviewIntermediate

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