monitoring-observability

Comprehensive monitoring setup with metrics collection, log aggregation, alerting, and health checks.

supercent-io/skills-template|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

What it does

Covers Prometheus for metrics instrumentation, Grafana dashboards for visualization, and structured logging with Winston for log aggregation
Includes pre-built alert rules for high error rates, slow response times, pod failures, and resource saturation
Provides advanced health check endpoints that test database, cache, and external API dependencies with latency tracking
Implements

Repository

supercent-io/skills-template

Last updated

Apr 8, 2026

Installation Guide

Select your AI agent

How to use monitoring-observability on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add monitoring-observability

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/supercent-io/skills-template --skill monitoring-observability

Fetches monitoring-observability from supercent-io/skills-template and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/monitoring-observability

Restart Cursor to activate monitoring-observability. Access via /monitoring-observability in your agent's command palette.

⚠

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Instructions

Step 1: Metrics Collection (Prometheus)

Application Instrumentation (Node.js):

import express from 'express'; import promClient from 'prom-client'; const app = express(); // Default metrics (CPU, Memory, etc.) promClient.collectDefaultMetrics(); // Custom metrics const httpRequestDuration = new promClient.Histogram({ name: 'http_request_duration_seconds', help: 'Duration of HTTP requests in seconds', labelNames: ['method', 'route', 'status_code'] }); const httpRequestTotal = new promClient.Counter({ name: 'http_requests_total', help: 'Total number of HTTP requests', labelNames: ['method', 'route', 'status_code'] }); // Middleware to track requests app.use((req, res, next) => { const start = Date.now(); res.on('finish', () => { const duration = (Date.now() - start) / 1000; const labels = { method: req.method, route: req.route?.path || req.path, status_code: res.statusCode }; httpRequestDuration.observe(labels, duration); httpRequestTotal.inc(labels); }); next(); }); // Metrics endpoint app.get('/metrics', async (req, res) => { res.set('Content-Type', promClient.register.contentType); res.end(await promClient.register.metrics()); }); app.listen(3000);

prometheus.yml:

global: scrape_interval: 15s evaluation_interval: 15s scrape_configs: - job_name: 'my-app' static_configs: - targets: ['localhost:3000'] metrics_path: '/metrics' - job_name: 'node-exporter' static_configs: - targets: ['localhost:9100'] alerting: alertmanagers: - static_configs: - targets: ['localhost:9093'] rule_files: - 'alert_rules.yml'

Step 2: Alert Rules

alert_rules.yml:

groups: - name: application_alerts interval: 30s rules: # High error rate - alert: HighErrorRate expr: | ( sum(rate(http_requests_total{status_code=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) ) > 0.05 for: 5m labels: severity: critical annotations: summary: "High error rate detected" description: "Error rate is {{ $value }}% (threshold: 5%)" # Slow response time - alert: SlowResponseTime expr: | histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le) ) > 1 for: 10m labels: severity: warning annotations: summary: "Slow response time" description: "95th percentile is {{ $value }}s" # Pod down - alert: PodDown expr: up{job="my-app"} == 0 for: 2m labels: severity: critical annotations: summary: "Pod is down" description: "{{ $labels.instance }} has been down for more than 2 minutes" # High memory usage - alert: HighMemoryUsage expr: | ( node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes ) / node_memory_MemTotal_bytes > 0.90 for: 5m labels: severity: warning annotations: summary: "High memory usage" description: "Memory usage is {{ $value }}%"

Step 3: Log Aggregation (Structured Logging)

Winston (Node.js):

import winston from 'winston'; const logger = winston.createLogger({ level: process.env.LOG_LEVEL || 'info', format: winston.format.combine( winston.format.timestamp(), winston.format.errors({ stack: true }), winston.format.json() ), defaultMeta: { service: 'my-app', environment: process.env.NODE_ENV },

monitoring-observability

What it does

Category

Repository

Last updated

Installation Guide

How to use monitoring-observability on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

User Story & Requirements Generation

Competitive Analysis

Roadmap Prioritization

Install Skill

Monitoring & Observability

When to use this skill

Instructions

Step 1: Metrics Collection (Prometheus)

Step 2: Alert Rules

Step 3: Log Aggregation (Structured Logging)

`Stakeholder Communication`

`Implementation Guide`

`Best Practices`

`When to Use This`

`Learning Path`

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