error-tracking

Name: error-tracking
Availability: InStock
Author: fghaffar

Instrument services with Sentry, Datadog, or New Relic for error tracking and performance monitoring.

1|Updated Dec 13, 2025

Search Tags:#monitoring #logging #instrumentation #Sentry #error-tracking #application-performance

Ready to run

npx skills add https://github.com/fghaffar/claude-cursor-setup --skill error-tracking-fghaffar

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: error-tracking
Source: https://github.com/fghaffar/claude-cursor-setup/tree/main/skills/error-tracking
Command: npx skills add https://github.com/fghaffar/claude-cursor-setup --skill error-tracking-fghaffar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures consistent error tracking and performance monitoring across your services, so issues are captured and diagnosable rather than hidden.

Core Features & Use Cases

Standardized error handling patterns for Python (FastAPI) and TypeScript (Express)
Centralized integration examples for Sentry, Datadog, or your chosen provider
Guidance on instrumenting code paths and tracing performance

Quick Start

Add initialization for your monitoring library (e.g., Sentry) in your app's startup
Wrap critical operations with try/except and capture exceptions
Use the sample patterns to verify events appear in your monitoring dashboard

Frequently Asked Questions about error-tracking

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I implement error tracking across my services?▼

Error tracking requires initializing a monitoring provider like Sentry, Datadog, or New Relic at application startup, then wrapping critical code paths with try/catch blocks to capture exceptions. This ensures all errors are logged centrally rather than lost to console output, giving you visibility into production issues.

What's the difference between console.error and proper error tracking?▼

Console.error logs to stdout and is lost unless actively monitored. Proper error tracking sends exceptions to a centralized service with context: stack traces, request metadata, user info, and performance data. This enables alerting, error grouping, and root cause analysis across your entire service.

Can I use error tracking with FastAPI and Express?▼

Yes. Error tracking works with both FastAPI (Python) and Express (TypeScript) through standardized patterns: initialize the monitoring library in your app startup, wrap route handlers and background jobs with exception capture, and use structured logging to add context to each error event.

How do I track performance alongside error tracking?▼

Monitoring solutions like Sentry, Datadog, and New Relic support performance instrumentation through spans and tracing. Beyond capturing errors, you instrument database queries, API calls, and background jobs to measure latency and identify bottlenecks, giving a complete view of service health.

Do I need to instrument every code path or just critical operations?▼

Start with critical paths—route handlers, external API calls, and database operations—to establish baseline coverage. The core principle is that all errors must be captured and sent to your monitoring provider, not silently logged or dropped. Expand instrumentation based on performance insights.

What setup is required before adding error tracking?▼

You need a monitoring provider account (Sentry, Datadog, New Relic, or equivalent), an API key or DSN from that provider, and a dependency installed in your project. Then initialize the library in your app's startup sequence before your service begins handling requests.