error-detective

Analyze error messages and stack traces to produce structured root-cause reports.

1|Updated Apr 23, 2026

Search Tags:#debugging #error #root-cause-analysis #stack-trace #pattern-recognition #production-incidents

Ready to run

npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill error-detective-mtsatryan

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Error Detective helps development teams identify and diagnose the root causes of failures across complex software stacks by analyzing error messages, stack traces, and contextual state.

Core Features & Use Cases

Root-cause analysis and troubleshooting across multi-system deployments.
Error pattern recognition to quickly classify incidents and accelerate remediation.
Enhanced stack-trace context with code context, source maps, and related data.
Production incident investigations and automated reporting with evidence and recommendations.

Quick Start

Describe the error you are investigating and provide relevant logs to trigger a full investigation.

Frequently Asked Questions about error-detective

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

FAQPage Schema

How do I find the root cause of an error from a stack trace in production?▼

Root-cause analysis of a stack trace involves evaluating error messages and contextual metadata to isolate failure origins. It returns a structured report containing evidence, confidence levels, and actionable remediation suggestions for production incidents.

How does error pattern recognition work for debugging distributed systems?▼

Error pattern recognition for distributed systems works by analyzing multiple error messages and stack traces to reveal correlations across stacks. This mechanism classifies incidents and accelerates remediation by matching current failures against previously identified structural error patterns.

What do I need to provide for effective production incident root-cause analysis?▼

For effective production incident root-cause analysis, you need to provide error objects and stack traces as required inputs. Including optional system state data and contextual metadata enhances the investigation, resulting in a more accurate structured diagnosis with remediation suggestions.

Can I use stack-trace analysis to troubleshoot failures across multiple software stacks?▼

Yes, you can use stack-trace analysis to troubleshoot failures across multi-system deployments. By analyzing stack traces and contextual state across various software stacks, the process identifies shared patterns and correlations to reveal likely root causes in complex distributed environments.

What's the best way to diagnose application errors when I only have error messages and logs?▼

The best way to diagnose application errors from messages and logs is to perform a structured investigation analyzing the error text alongside available system state. This approach identifies underlying patterns and generates a root-cause report with evidence and targeted remediation suggestions.