# Observability Testing Patterns

> Observability and monitoring validation patterns for dashboards, alerting, log aggregation, APM traces, and SLA/SLO verification. Use when testing monitoring infrastructure, dashboard accuracy, alert rules, or metric pipelines.

- **Type:** Skill
- **Install:** `agentstack add skill-proffesor-for-testing-agentic-qe-observability-testing-patterns`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [proffesor-for-testing](https://agentstack.voostack.com/s/proffesor-for-testing)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [proffesor-for-testing](https://github.com/proffesor-for-testing)
- **Source:** https://github.com/proffesor-for-testing/agentic-qe/tree/main/.claude/skills/observability-testing-patterns
- **Website:** https://agentic-qe.dev/

## Install

```sh
agentstack add skill-proffesor-for-testing-agentic-qe-observability-testing-patterns
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Observability Testing Patterns

## Browser engine

Dashboard screenshot validation and alert-UI verification go through the **qe-browser** fleet skill (`.claude/skills/qe-browser/`). Vibium is installed by `aqe init`. Typical dashboard regression workflow:

```bash
vibium go "$GRAFANA_URL/d/api-latency"
vibium wait load
node .claude/skills/qe-browser/scripts/assert.js --checks '[
  {"kind": "selector_visible", "selector": ".panel-title"},
  {"kind": "no_console_errors"},
  {"kind": "no_failed_requests"},
  {"kind": "element_count", "selector": ".panel", "op": ">=", "count": 4}
]'
node .claude/skills/qe-browser/scripts/visual-diff.js --name "grafana-api-latency"
```

When testing observability infrastructure, dashboards, or monitoring:
1. VALIDATE data accuracy (source data matches what the dashboard displays)
2. TEST alert rules fire correctly at defined thresholds
3. VERIFY log aggregation completeness (no missing logs across services)
4. TRACE distributed requests end-to-end through APM
5. MEASURE dashboard performance (render time, query latency)
6. CONFIRM SLA/SLO compliance through synthetic monitoring
7. TEST metric pipeline integrity from collection to display

**Quick Pattern Selection:**
- Dashboard shows wrong numbers -> Data accuracy validation
- Alerts not firing -> Alert rule threshold testing
- Missing logs in Kibana -> Log aggregation completeness
- Slow dashboard -> Dashboard performance testing
- Broken traces -> APM trace validation
- SLA disputes -> SLO compliance validation

**Critical Success Factors:**
- Observability is only as good as the data it shows
- A dashboard that lies is worse than no dashboard
- Alert fatigue kills response times; test thresholds carefully

## Quick Reference Card

### When to Use
- Validating dashboard data accuracy (Kibana, Grafana, Datadog)
- Testing alert rule thresholds and notification delivery
- Verifying log aggregation completeness across microservices
- Validating distributed tracing (APM) correctness
- Measuring SLA/SLO compliance
- Testing metric pipeline integrity (collection -> aggregation -> display)

### Testing Levels
| Level | Purpose | Dependencies | Speed |
|-------|---------|--------------|-------|
| Query Validation | Elasticsearch/PromQL query accuracy | Data source | Fast |
| Dashboard Accuracy | Visual matches source data | Full stack | Medium |
| Alert Threshold | Trigger and notification testing | Alerting stack | Medium |
| Pipeline Integrity | End-to-end metric flow | Full pipeline | Slower |
| Performance | Dashboard render time, query latency | Full stack | Slower |

### Critical Test Scenarios
| Scenario | Must Test | Example |
|----------|----------|---------|
| Data Accuracy | Dashboard = source truth | Order count on dashboard = DB count |
| Alert Firing | Threshold triggers alert | Error rate > 5% fires PagerDuty |
| Alert Recovery | Auto-resolve when recovered | Error rate drops below 5% clears alert |
| Log Completeness | All services emit logs | 10 microservices, all logs in Kibana |
| Trace Integrity | Full request path visible | Auth -> API -> DB -> Cache spans |
| SLO Compliance | Error budget tracking | 99.9% availability over 30 days |
| Time Accuracy | Timestamps aligned | Log timestamp matches event time |

### Tools
- **Dashboards**: Kibana, Grafana, Datadog, New Relic
- **Search**: Elasticsearch, OpenSearch, Loki
- **Metrics**: Prometheus, InfluxDB, CloudWatch
- **Tracing**: Jaeger, Zipkin, Datadog APM, OpenTelemetry
- **Alerting**: PagerDuty, OpsGenie, Alertmanager
- **Synthetic**: Datadog Synthetics, Checkly, Playwright

### Agent Coordination
- `qe-integration-tester`: Validate data pipelines, query accuracy, log completeness
- `qe-performance-tester`: Dashboard render performance, query latency
- `qe-visual-tester`: Dashboard visual regression, layout accuracy

---

## Dashboard Data Accuracy Validation

### Compare Source Data to Dashboard
```javascript
describe('Dashboard Data Accuracy', () => {
  it('order count on dashboard matches database', async () => {
    // Step 1: Get ground truth from source database
    const dbResult = await db.query(
      "SELECT COUNT(*) as count FROM orders WHERE created_at >= NOW() - INTERVAL '24 HOURS'"
    );
    const dbCount = parseInt(dbResult.rows[0].count);

    // Step 2: Query Elasticsearch (same data source as dashboard)
    const esResult = await esClient.search({
      index: 'orders-*',
      body: {
        query: {
          range: { created_at: { gte: 'now-24h' } }
        },
        size: 0,
        track_total_hits: true
      }
    });
    const esCount = esResult.hits.total.value;

    // Step 3: Compare
    expect(esCount).toBe(dbCount);
  });

  it('revenue metric on dashboard matches transaction totals', async () => {
    const dbRevenue = await db.query(
      "SELECT SUM(total) as revenue FROM orders WHERE status = 'COMPLETED' AND created_at >= NOW() - INTERVAL '24 HOURS'"
    );
    const expectedRevenue = parseFloat(dbRevenue.rows[0].revenue);

    const esResult = await esClient.search({
      index: 'orders-*',
      body: {
        query: {
          bool: {
            must: [
              { term: { status: 'COMPLETED' } },
              { range: { created_at: { gte: 'now-24h' } } }
            ]
          }
        },
        aggs: {
          total_revenue: { sum: { field: 'total' } }
        },
        size: 0
      }
    });
    const dashboardRevenue = esResult.aggregations.total_revenue.value;

    // Allow small floating point tolerance
    expect(Math.abs(dashboardRevenue - expectedRevenue)).toBeLessThan(0.01);
  });

  it('error rate percentage is calculated correctly', async () => {
    const esResult = await esClient.search({
      index: 'logs-*',
      body: {
        query: { range: { '@timestamp': { gte: 'now-1h' } } },
        aggs: {
          total: { value_count: { field: 'status_code' } },
          errors: {
            filter: { range: { status_code: { gte: 500 } } },
            aggs: { count: { value_count: { field: 'status_code' } } }
          }
        },
        size: 0
      }
    });

    const total = esResult.aggregations.total.value;
    const errors = esResult.aggregations.errors.count.value;
    const expectedErrorRate = (errors / total) * 100;

    // Fetch what the dashboard shows via Kibana API
    const dashboardPanel = await kibanaApi.get('/api/saved_objects/visualization/error-rate-gauge');
    const displayedErrorRate = await evaluateKibanaVisualization(dashboardPanel);

    expect(Math.abs(displayedErrorRate - expectedErrorRate)).toBeLessThan(0.1);
  });
});
```

---

## Elasticsearch Query Result Validation

```javascript
describe('Elasticsearch Query Validation', () => {
  it('validates date histogram aggregation returns correct buckets', async () => {
    // Insert known test data
    const testDocs = [];
    for (let hour = 0; hour  [{ index: {} }, doc])
    });
    await esClient.indices.refresh({ index: 'test-logs' });

    // Run the same query the dashboard uses
    const result = await esClient.search({
      index: 'test-logs',
      body: {
        query: { match_all: {} },
        aggs: {
          requests_over_time: {
            date_histogram: { field: '@timestamp', fixed_interval: '1h' },
            aggs: {
              avg_response: { avg: { field: 'response_time' } },
              error_count: {
                filter: { range: { status_code: { gte: 500 } } }
              }
            }
          }
        },
        size: 0
      }
    });

    const buckets = result.aggregations.requests_over_time.buckets;
    expect(buckets.length).toBe(24);

    // Verify specific bucket values
    const errorBuckets = buckets.filter(b => b.error_count.doc_count > 0);
    expect(errorBuckets.length).toBe(5); // Hours 0, 5, 10, 15, 20
  });

  it('validates term aggregation for top services', async () => {
    const result = await esClient.search({
      index: 'logs-*',
      body: {
        query: { range: { '@timestamp': { gte: 'now-1h' } } },
        aggs: {
          top_services: {
            terms: { field: 'service.keyword', size: 10 }
          }
        },
        size: 0
      }
    });

    const services = result.aggregations.top_services.buckets;
    expect(services.length).toBeGreaterThan(0);

    // Each bucket should have reasonable doc counts
    for (const bucket of services) {
      expect(bucket.key).toBeDefined();
      expect(bucket.doc_count).toBeGreaterThan(0);
    }
  });
});
```

---

## Kibana Dashboard Element Assertions

```javascript
describe('Kibana Dashboard Visual Validation', () => {
  it('validates dashboard panels render without errors', async () => {
    await page.goto(`${kibanaUrl}/app/dashboards#/view/operations-overview`);

    // Wait for all panels to finish loading
    await page.waitForSelector('.embPanel__content', { state: 'visible' });
    await page.waitForFunction(() => {
      const loaders = document.querySelectorAll('.euiLoadingSpinner');
      return loaders.length === 0;
    }, { timeout: 30000 });

    // Check no error icons on any panel
    const errorPanels = await page.locator('.embPanel--error').count();
    expect(errorPanels).toBe(0);

    // Check no "No results found" where data is expected
    const noResultPanels = await page.locator('text="No results found"').count();
    expect(noResultPanels).toBe(0);
  });

  it('validates metric visualization shows correct value', async () => {
    await page.goto(`${kibanaUrl}/app/dashboards#/view/operations-overview`);
    await page.waitForLoadState('networkidle');

    // Get the displayed metric value
    const metricValue = await page.locator('[data-test-subj="metricVis-total-orders"] .mtrVis__value').textContent();
    const displayedCount = parseInt(metricValue.replace(/,/g, ''));

    // Compare with direct ES query
    const esResult = await esClient.count({ index: 'orders-*' });

    expect(displayedCount).toBe(esResult.count);
  });

  it('validates table visualization columns and sorting', async () => {
    await page.goto(`${kibanaUrl}/app/dashboards#/view/operations-overview`);
    await page.waitForLoadState('networkidle');

    // Verify expected columns exist
    const headers = await page.locator('.euiTable th').allTextContents();
    expect(headers).toContain('Service');
    expect(headers).toContain('Error Rate');
    expect(headers).toContain('P95 Latency');

    // Verify sorting works
    await page.click('th:has-text("Error Rate")');
    const firstRow = await page.locator('.euiTable tbody tr:first-child td').allTextContents();
    const secondRow = await page.locator('.euiTable tbody tr:nth-child(2) td').allTextContents();

    const firstErrorRate = parseFloat(firstRow[1]);
    const secondErrorRate = parseFloat(secondRow[1]);
    expect(firstErrorRate).toBeGreaterThanOrEqual(secondErrorRate);
  });
});
```

---

## Alert Rule Testing

```javascript
describe('Alert Rule Validation', () => {
  it('fires alert when error rate exceeds threshold', async () => {
    // Generate errors to exceed the 5% threshold
    const requests = [];
    for (let i = 0; i  5% threshold
        response_time: 200
      });
    }

    await esClient.bulk({
      index: 'logs-payment',
      body: requests.flatMap(doc => [{ index: {} }, doc])
    });
    await esClient.indices.refresh({ index: 'logs-payment' });

    // Wait for alert evaluation cycle (typically 1 minute)
    await sleep(90000);

    // Check alert was fired
    const alerts = await alertManager.getActiveAlerts({
      filter: 'alertname="HighErrorRate" AND service="payment-api"'
    });
    expect(alerts.length).toBeGreaterThan(0);
    expect(alerts[0].labels.severity).toBe('critical');
  });

  it('alert auto-resolves when condition clears', async () => {
    // First trigger the alert
    await injectErrors('payment-api', { count: 50, total: 100 });
    await sleep(90000);

    let alerts = await alertManager.getActiveAlerts({ filter: 'alertname="HighErrorRate"' });
    expect(alerts.length).toBeGreaterThan(0);

    // Now inject healthy traffic to bring error rate below threshold
    await injectSuccessRequests('payment-api', { count: 1000 });
    await sleep(90000);

    // Alert should auto-resolve
    alerts = await alertManager.getActiveAlerts({ filter: 'alertname="HighErrorRate"' });
    expect(alerts.length).toBe(0);
  });

  it('alert notification reaches correct channel', async () => {
    // Subscribe to notification channel
    const notifications = [];
    const subscription = pagerDutyMock.onIncident((incident) => {
      notifications.push(incident);
    });

    // Trigger alert condition
    await injectErrors('critical-service', { count: 50, total: 100 });
    await sleep(120000);

    expect(notifications.length).toBeGreaterThan(0);
    expect(notifications[0].service.name).toBe('critical-service');
    expect(notifications[0].urgency).toBe('high');

    subscription.unsubscribe();
  });

  it('alert does not fire for brief transient spikes', async () => {
    // Inject a brief 30-second spike (alert requires 5 minutes sustained)
    await injectErrors('api-service', { count: 20, total: 50, duration: 30000 });
    await sleep(120000);

    const alerts = await alertManager.getActiveAlerts({ filter: 'alertname="HighErrorRate"' });
    expect(alerts.length).toBe(0); // Should NOT fire for transient spike
  });
});
```

---

## Log Aggregation Completeness

```javascript
describe('Log Aggregation Completeness', () => {
  it('all microservice logs appear in centralized index', async () => {
    const traceId = uuid();
    const services = ['api-gateway', 'auth-service', 'order-service', 'payment-service', 'notification-service'];

    // Generate a log entry with known traceId in each service
    for (const service of services) {
      await serviceLogEmitter.emit(service, {
        level: 'INFO',
        message: `Completeness test - ${traceId}`,
        traceId,
        timestamp: new Date().toISOString()
      });
    }

    // Wait for log pipeline to process (Filebeat -> Logstash -> Elasticsearch)
    await sleep(15000);

    // Query Elasticsearch for the trace ID
    const result = await esClient.search({
      index: 'logs-*',
      body: {
        query: { term: { 'traceId.keyword': traceId } },
        size: 100
      }
    });

    const foundServices = result.hits.hits.map(h => h._source.service);

    // All services should have their log entry in Elasticsearch
    for (const service of services) {
      expect(foundServices).toContain(service);
    }
    expect(foundServices.length).toBe(services.length);
  });

  it('logs retain correct structure after pipeline processing', async () => {
    const testLog = {
      level: 'ERROR',
      message: 'Payment declined',
      traceId: uuid(),
      userId: 'user-123',
      orderId: 'order-456',
      errorCode: 'INSUFFICIENT_FUNDS',
      timestamp: new Date().toISOString()
    };

    await serviceLogEmitter.emit('payment-service', testLog);
    await sleep(10000);

    const result = await esClient.search({
      index: 'logs-*',
      body: { query: { term: { 'traceId.keyword': testLog.traceId } } }
    });

    expect(result.hits.hits.length).toBe(1);
    const indexed = result.hits.hits[0]._source;

    // Verify all fields survived the pipeline
    expect(indexed.level).toBe('ERROR');
    expect(indexed.message).toBe('Payment declined');
    expect(indexed.userId).toBe('user-123');
    expect(indexed.orderId).toBe('order-456');
    expect(indexed.errorCode).toBe('INSUFFICIENT_FUNDS');
  });

  it('detects log volume drops indicating pipeline issues', async () => {
    // Get baseline log volume for the past hour
    const baseline = await esClient.count({
      index: 'logs-*',
      body: { query: { range: { '@timestamp': { gte: 'now-2h', lt: 'now-1h' } } } }
    });

    const current = await esClient.count({
      index: 'logs-*',
      body: { query: { range: { '@timestamp': { gte: 'now-1h' } } } }
    });

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [proffesor-for-testing](https://github.com/proffesor-for-testing)
- **Source:** [proffesor-for-testing/agentic-qe](https://github.com/proffesor-for-testing/agentic-qe)
- **License:** MIT
- **Homepage:** https://agentic-qe.dev/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-proffesor-for-testing-agentic-qe-observability-testing-patterns
- Seller: https://agentstack.voostack.com/s/proffesor-for-testing
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.