OP

openevidence-observability

Establishes monitoring for OpenEvidence API usage, tracking latency, accuracy, and audit compliance for healthcare AI.

Install

mkdir -p .claude/skills/openevidence-observability && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4795" && unzip -o skill.zip -d .claude/skills/openevidence-observability && rm skill.zip

Installs to .claude/skills/openevidence-observability

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.

Observability for OpenEvidence.
31 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Monitor query response latency
  • Track evidence freshness and citation accuracy
  • Implement audit logging for compliance
  • Configure health alerts for clinical AI operations

How it works

The skill provides instrumentation to track query latency, error rates, and evidence age, while recording audit logs for regulatory compliance without logging patient identifiers.

Inputs & outputs

You give it
Clinical query event
You get back
Metrics and audit logs

When to use openevidence-observability

  • Monitor API latency
  • Track citation accuracy
  • Implement audit logging
  • Configure health alerts

About this skill

OpenEvidence Observability

Overview

OpenEvidence delivers clinical evidence queries where response accuracy and freshness have direct patient safety implications. Monitor query response times to ensure clinicians get timely answers, track evidence freshness to catch stale citations, and audit every query for compliance. Observability must also verify citation accuracy and maintain complete audit logs for regulatory requirements (HIPAA, clinical decision support standards).

Key Metrics

MetricTypeTargetAlert Threshold
Query response time p95Histogram< 3s> 8s
Evidence freshnessGauge< 7 days median> 30 days
Citation accuracy rateGauge> 95%< 90%
API error rateGauge< 0.5%> 2%
Audit log completenessGauge100%< 99.9%
Daily query volumeCounterWithin quota> 90% quota

Instrumentation

async function trackClinicalQuery(queryType: string, fn: () => Promise<any>) {
  const start = Date.now();
  const traceId = crypto.randomUUID();
  try {
    const result = await fn();
    metrics.histogram('openevidence.query.latency', Date.now() - start, { queryType });
    metrics.increment('openevidence.query.total', { queryType });
    auditLog.record({ traceId, queryType, status: 'ok', latency: Date.now() - start });
    return result;
  } catch (err) {
    metrics.increment('openevidence.query.errors', { queryType, error: err.code });
    auditLog.record({ traceId, queryType, status: 'error', error: err.message });
    throw err;
  }
}

Health Check Dashboard

async function openEvidenceHealth(): Promise<Record<string, string>> {
  const latencyP95 = await metrics.query('openevidence.query.latency', 'p95', '5m');
  const errorRate = await metrics.query('openevidence.query.error_rate', 'avg', '5m');
  const freshness = await openEvAdmin.getMedianEvidenceAge();
  return {
    query_latency: latencyP95 < 3000 ? 'healthy' : 'slow',
    error_rate: errorRate < 0.005 ? 'healthy' : 'degraded',
    evidence_freshness: freshness < 7 ? 'healthy' : 'stale',
  };
}

Alerting Rules

const alerts = [
  { metric: 'openevidence.query.latency_p95', condition: '> 8s', window: '10m', severity: 'warning' },
  { metric: 'openevidence.query.error_rate', condition: '> 0.02', window: '5m', severity: 'critical' },
  { metric: 'openevidence.evidence.median_age_days', condition: '> 30', window: '1d', severity: 'warning' },
  { metric: 'openevidence.audit.completeness', condition: '< 0.999', window: '1h', severity: 'critical' },
];

Structured Logging

function logClinicalEvent(event: string, data: Record<string, any>) {
  console.log(JSON.stringify({
    service: 'openevidence', event,
    query_type: data.queryType, duration_ms: data.latency,
    citation_count: data.citations, evidence_age_days: data.evidenceAge,
    // HIPAA: never log patient identifiers or query text
    trace_id: data.traceId, audit_seq: data.auditSeq,
    timestamp: new Date().toISOString(),
  }));
}

Error Handling

SignalMeaningAction
Query timeout > 8sEvidence index overloadedCheck index health, scale read replicas
Citation accuracy dropStale or retracted sourcesTrigger evidence refresh pipeline
Audit log gapLogging pipeline failureCritical — investigate immediately for compliance
429 rate limitQuota approaching limitThrottle non-critical queries, request increase
Evidence age > 30 daysRefresh pipeline stalledCheck ingestion jobs, verify source feeds

Resources

Next Steps

See openevidence-incident-runbook.

Limitations

  • Never log patient identifiers or query text for HIPAA compliance

How it compares

It integrates clinical-specific metrics like evidence freshness and citation accuracy alongside standard API monitoring to ensure patient safety.

Compared to similar skills

openevidence-observability side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
openevidence-observability (this skill)125dNo flagsIntermediate
service-mesh-observability52moNo flagsAdvanced
gcloud-usage17moNo flagsIntermediate
devops-troubleshooter14moNo flagsAdvanced

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