error-diagnostics-smart-debug

Analyze error messages and stack traces to generate ranked root cause hypotheses.

Updated Feb 21, 2026

Search Tags:#debugging #troubleshooting #root cause analysis #observability #error analysis #ai-assisted

Ready to run

npx skills add https://github.com/HCMUTE-RTIC/fit-hcmute --skill error-diagnostics-smart-debug

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: error-diagnostics-smart-debug
Source: https://github.com/HCMUTE-RTIC/fit-hcmute/tree/main/.agent/skills/error-diagnostics-smart-debug
Command: npx skills add https://github.com/HCMUTE-RTIC/fit-hcmute --skill error-diagnostics-smart-debug

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill assists in diagnosing and resolving complex software errors by leveraging AI for root cause analysis, hypothesis generation, and suggesting debugging strategies.

Core Features & Use Cases

Automated Error Triage: Parses error messages, stack traces, and system context to identify potential causes.
Observability Integration: Guides the collection and analysis of data from monitoring tools like Sentry, DataDog, and logs.
Hypothesis Generation & Validation: Proposes ranked hypotheses for errors and outlines methods to test them.
Use Case: When a critical production service starts intermittently failing, this Skill can analyze the error patterns, correlate them with recent deployments or infrastructure changes, and suggest specific code sections or configurations to investigate.

Quick Start

Use the error-diagnostics-smart-debug skill to analyze the following error message and provide a root cause analysis.

Frequently Asked Questions about error-diagnostics-smart-debug

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

FAQPage Schema

How do I debug complex software errors using root cause analysis?▼

Root cause analysis for software errors works by parsing error messages, stack traces, and system context to generate ranked hypotheses and actionable fix proposals. You provide structured input detailing the error, reproduction steps, and environment for effective diagnosis.

What is the best way to triage intermittent production failures?▼

Triage intermittent production failures by analyzing error patterns and correlating them with recent deployments or infrastructure changes. This approach identifies specific code sections or configurations to investigate, suggesting targeted debugging strategies.

How does AI-assisted debugging integrate with observability tools?▼

AI-assisted debugging integrates with observability tools by guiding the collection and analysis of monitoring data from platforms like Sentry and DataDog. It analyzes logs and system context to propose ranked hypotheses for errors.

What information do I need to provide for effective error diagnosis?▼

Effective error diagnosis requires structured input detailing the error message, reproduction steps, affected components, and environment. This context allows the analysis to generate accurate root cause hypotheses and actionable fix proposals.

Can AI generate hypotheses for troubleshooting stack traces?▼

AI can generate ranked hypotheses for troubleshooting stack traces by parsing the trace data alongside system context. It proposes potential causes and outlines specific methods to test and validate each hypothesis.

When should I use AI-assisted troubleshooting instead of manual debugging?▼

Use AI-assisted troubleshooting when diagnosing complex software errors that require correlating error patterns with system context or observability data. It automates error triage and suggests targeted debugging strategies for intermittent production service failures.