tsuga-investigate-errors

What problem does it solve?

This Skill helps you quickly determine what is failing for a specific service by measuring error volume, clustering error structures, and identifying anomalous error-pattern increases within a time window.

Core Features & Use Cases

• Authoritative error counting: Uses an aggregation scalar to confirm whether errors are elevated in a specified time range, avoiding false assumptions from log presence.
• Pattern-based error clustering: Groups error occurrences by structure using log pattern clustering to summarize likely causes and variants.
• Anomaly detection for error patterns: Detects which clustered error patterns show anomalous volume changes for a team over time, so you can prioritize investigation.
• Safe log sampling for structure: Extracts key fields (message templates, filenames, targets) from a limited number of matches for structure analysis without dumping raw log lines.

Quick Start

Ask the AI to investigate errors for service "checkout-api" over the last 2 hours and return the elevated error count, dominant error patterns, and recommended next actions.