Error Tracker
Overview
Continuously monitor application logs, exception streams, and error events to detect regressions, group similar errors, prioritize by impact, and route to the right team for resolution.
When to Use
- •Monitoring production application logs in real-time
- •Detecting new error patterns after a deployment
- •Auto-creating tickets for critical bugs that need immediate attention
- •Generating weekly error trend reports for engineering teams
- •Suppressing known/expected errors to reduce noise
Instructions
- •Accept inputs: log source (file path, CloudWatch ARN, Datadog query, Sentry DSN), monitoring interval, severity thresholds, notification channels.
- •Connect to the log source and ingest error events.
- •Parse and normalize error payloads: extract message, stack trace, frequency, first/last seen, affected users.
- •Group similar errors using stack trace fingerprinting to de-duplicate.
- •Classify severity: Critical (production down), High (feature broken), Medium (degraded performance), Low (cosmetic).
- •For new Critical/High errors: create GitHub issue or Jira ticket with full context, stack trace, and reproduction steps.
- •Send Slack/PagerDuty alert for Critical errors.
- •Return error digest: new errors, resolved errors, top 10 by frequency, error rate trend.
Environment
code
LOG_SOURCE=cloudwatch|sentry|datadog|file LOG_GROUP_ARN=arn:aws:logs:us-east-1:123456789:log-group/app GITHUB_TOKEN=ghp_your_token SLACK_WEBHOOK_URL=https://hooks.slack.com/services/... CRITICAL_THRESHOLD=1 HIGH_THRESHOLD=10
Examples
Input:
code
source: sentry project: skillsdotmd-web interval: 5_minutes alert_channel: #ops-errors create_issues: true
Output:
code
Error Digest (last 5 min) New errors: 3 CRITICAL: TypeError: Cannot read property 'id' of undefined (checkout.ts:142) - 47 occurrences HIGH: 500 error on /api/skills endpoint - 12 occurrences GitHub issues created: 2 Slack alerts sent: 1 (critical) Error rate: +23% vs previous 5 min