error-detective

Correlate logs, stack traces, and anomalies to diagnose distributed system errors.

Updated Feb 24, 2026

Search Tags:#monitoring #distributed-systems #root-cause #log-analysis #stack-trace #error-detection

Ready to run

npx skills add https://github.com/chicanoandres702/SentientAIBrowser --skill error-detective-chicanoandres702

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: error-detective
Source: https://github.com/chicanoandres702/SentientAIBrowser/tree/main/.agents/workflows/error-detective
Command: npx skills add https://github.com/chicanoandres702/SentientAIBrowser --skill error-detective-chicanoandres702

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps engineers rapidly identify, diagnose, and understand errors that occur across distributed systems by correlating log messages, stack traces, and anomaly signals.

Core Features & Use Cases

Cross-service error correlation: tie errors across services to root causes.
Log parsing and error extraction: regex patterns for common error formats.
Multi-language stack trace analysis: interpret traces across languages.
Proactive anomaly detection and monitoring queries.

Quick Start

Analyze a recent production issue by providing logs, stack traces, and deployment context to generate a root-cause hypothesis.

Frequently Asked Questions about error-detective

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

FAQPage Schema

How do I correlate distributed system logs and stack traces to find a root cause?▼

To correlate distributed system logs and stack traces for root-cause analysis, provide your logs, traces, and deployment metadata to generate a unified error hypothesis across affected services.

What is cross-service error correlation and how does it work?▼

Cross-service error correlation identifies related failures across distributed systems by parsing log messages and matching multi-language stack traces to pinpoint a single root cause.

Can I parse multi-language stack traces during post-deploy incident debugging?▼

Yes, you can parse multi-language stack traces during post-deploy incident debugging by supplying the error logs and deployment context to extract the cross-service root-cause hypothesis.

Do I need deployment metadata to diagnose production failures?▼

Yes, deployment metadata is required alongside logs and traces to accurately diagnose production failures and generate a reliable root-cause hypothesis for recent incidents.

What's the best way to extract errors from distributed system logs?▼

The best way to extract errors from distributed system logs is applying regex patterns for common error formats, which isolates anomalies and correlates them with stack traces.

Why does proactive anomaly detection fail without centralized log analysis?▼

Proactive anomaly detection fails without centralized log analysis because isolated stack traces and uncorrelated logs prevent the identification of cross-service root causes during production failures.