Agent skill

maintainx-observability

Implement comprehensive observability for MaintainX integrations. Use when setting up monitoring, logging, tracing, and alerting for MaintainX API integrations. Trigger with phrases like "maintainx monitoring", "maintainx logging", "maintainx metrics", "maintainx observability", "maintainx alerts".

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npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/saas-packs/maintainx-pack/skills/maintainx-observability

SKILL.md

MaintainX Observability

Overview

Implement metrics, structured logging, and alerting for MaintainX integrations to ensure reliability and rapid issue detection.

Prerequisites

  • MaintainX integration deployed
  • Node.js 18+
  • Monitoring platform (Prometheus/Grafana, Datadog, or CloudWatch)

Instructions

Step 1: Prometheus Metrics

typescript
// src/observability/metrics.ts
import { Counter, Histogram, Gauge, Registry } from 'prom-client';

const register = new Registry();

export const metrics = {
  apiRequests: new Counter({
    name: 'maintainx_api_requests_total',
    help: 'Total MaintainX API requests',
    labelNames: ['method', 'endpoint', 'status'],
    registers: [register],
  }),

  apiLatency: new Histogram({
    name: 'maintainx_api_latency_seconds',
    help: 'MaintainX API request latency',
    labelNames: ['method', 'endpoint'],
    buckets: [0.1, 0.25, 0.5, 1, 2.5, 5, 10],
    registers: [register],
  }),

  rateLimitHits: new Counter({
    name: 'maintainx_rate_limit_hits_total',
    help: 'Times rate limited by MaintainX API',
    registers: [register],
  }),

  workOrdersProcessed: new Counter({
    name: 'maintainx_work_orders_processed_total',
    help: 'Work orders processed',
    labelNames: ['action', 'status'],
    registers: [register],
  }),

  syncLag: new Gauge({
    name: 'maintainx_sync_lag_seconds',
    help: 'Seconds since last successful sync',
    registers: [register],
  }),
};

export { register };

Step 2: Instrumented API Client

typescript
// src/observability/instrumented-client.ts
import axios, { AxiosInstance } from 'axios';
import { metrics } from './metrics';

export function createInstrumentedClient(apiKey: string): AxiosInstance {
  const client = axios.create({
    baseURL: 'https://api.getmaintainx.com/v1',
    headers: { Authorization: `Bearer ${apiKey}`, 'Content-Type': 'application/json' },
    timeout: 30_000,
  });

  client.interceptors.request.use((config) => {
    (config as any).__startTime = process.hrtime.bigint();
    return config;
  });

  client.interceptors.response.use(
    (response) => {
      const elapsed = Number(process.hrtime.bigint() - (response.config as any).__startTime) / 1e9;
      const endpoint = response.config.url?.split('?')[0] || 'unknown';

      metrics.apiRequests.inc({
        method: response.config.method?.toUpperCase() || 'GET',
        endpoint,
        status: String(response.status),
      });
      metrics.apiLatency.observe(
        { method: response.config.method?.toUpperCase() || 'GET', endpoint },
        elapsed,
      );
      return response;
    },
    (error) => {
      const status = error.response?.status || 0;
      const endpoint = error.config?.url?.split('?')[0] || 'unknown';

      metrics.apiRequests.inc({
        method: error.config?.method?.toUpperCase() || 'GET',
        endpoint,
        status: String(status),
      });

      if (status === 429) {
        metrics.rateLimitHits.inc();
      }
      throw error;
    },
  );

  return client;
}

Step 3: Structured Logging

typescript
// src/observability/logger.ts

type LogLevel = 'debug' | 'info' | 'warn' | 'error';

interface LogEntry {
  level: LogLevel;
  message: string;
  service: string;
  timestamp: string;
  [key: string]: any;
}

class StructuredLogger {
  private service: string;

  constructor(service: string) {
    this.service = service;
  }

  private log(level: LogLevel, message: string, data?: Record<string, any>) {
    const entry: LogEntry = {
      level,
      message,
      service: this.service,
      timestamp: new Date().toISOString(),
      ...data,
    };
    // JSON output for log aggregation (ELK, CloudWatch, Datadog)
    console.log(JSON.stringify(entry));
  }

  info(message: string, data?: Record<string, any>) { this.log('info', message, data); }
  warn(message: string, data?: Record<string, any>) { this.log('warn', message, data); }
  error(message: string, data?: Record<string, any>) { this.log('error', message, data); }
  debug(message: string, data?: Record<string, any>) { this.log('debug', message, data); }
}

export const logger = new StructuredLogger('maintainx-integration');

// Usage
logger.info('Work order created', { workOrderId: 12345, priority: 'HIGH' });
logger.error('API call failed', { endpoint: '/workorders', status: 500, retryCount: 2 });

Step 4: Health and Metrics Endpoints

typescript
// src/observability/server.ts
import express from 'express';
import { register, metrics } from './metrics';

const app = express();

// Prometheus scrape endpoint
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', register.contentType);
  res.end(await register.metrics());
});

// Health check with metrics
app.get('/health', async (req, res) => {
  const health = {
    status: 'healthy',
    uptime: process.uptime(),
    metrics: {
      totalRequests: await metrics.apiRequests.get(),
      rateLimitHits: await metrics.rateLimitHits.get(),
      syncLagSeconds: (await metrics.syncLag.get()).values[0]?.value || 0,
    },
  };
  res.json(health);
});

app.listen(9090, () => logger.info('Metrics server on :9090'));

Step 5: Alerting Rules (Prometheus)

yaml
# prometheus/alerts.yml
groups:
  - name: maintainx
    rules:
      - alert: MaintainXHighErrorRate
        expr: rate(maintainx_api_requests_total{status=~"5.."}[5m]) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "MaintainX API error rate > 10%"

      - alert: MaintainXHighLatency
        expr: histogram_quantile(0.95, rate(maintainx_api_latency_seconds_bucket[5m])) > 5
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "MaintainX API p95 latency > 5s"

      - alert: MaintainXRateLimited
        expr: rate(maintainx_rate_limit_hits_total[5m]) > 0
        for: 1m
        labels:
          severity: warning
        annotations:
          summary: "MaintainX API rate limiting detected"

      - alert: MaintainXSyncStale
        expr: maintainx_sync_lag_seconds > 900
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "MaintainX sync lag > 15 minutes"

Output

  • Prometheus metrics (request count, latency histogram, rate limit counter, sync lag gauge)
  • Instrumented axios client automatically recording metrics on every API call
  • Structured JSON logging for all operations
  • /metrics endpoint for Prometheus scraping
  • Alerting rules for error rate, latency, rate limits, and sync staleness

Error Handling

Issue Cause Solution
Metrics endpoint 500 prom-client not initialized Ensure Registry is created before metrics
Missing labels Metric name mismatch Check labelNames match inc()/observe() calls
Log volume too high Debug logging in production Set LOG_LEVEL=info in production
Stale sync alert Sync job stopped Check cron schedule, restart sync process

Resources

Next Steps

For incident response, see maintainx-incident-runbook.

Examples

Datadog integration using DogStatsD:

typescript
import StatsD from 'hot-shots';

const dogstatsd = new StatsD({ prefix: 'maintainx.' });

// Record API call
dogstatsd.increment('api.requests', 1, { endpoint: '/workorders', status: '200' });
dogstatsd.histogram('api.latency', 0.45, { endpoint: '/workorders' });

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