investigate

Investigate software errors through a four-phase root cause analysis workflow.

Updated Jul 21, 2025
One-click install
npx skills add https://github.com/robertzengcn/aiFetchly --skill investigate-robertzengcn
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: investigate
Source: https://github.com/robertzengcn/aiFetchly/tree/main/.agents/skills/gstack/investigate
Command: npx skills add https://github.com/robertzengcn/aiFetchly --skill investigate-robertzengcn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates guesswork and recurring bugs when debugging broken systems, unexpected errors, or post-deploy regressions by enforcing a structured root cause investigation workflow instead of random, ineffective fixes.

Core Features & Use Cases

  • 4-Phase Structured Workflow: Follows a consistent investigate → analyze → hypothesize → implement process to ensure no fixes are applied without confirmed root causes.
  • Proactive Auto-Activation: Automatically triggers when users report errors, 500s, stack traces, unexpected behavior, or "it worked yesterday" scenarios, no manual skill invocation needed for common troubleshooting.
  • Context-Aware Investigation: Pulls prior investigation history, project learnings, and cross-project patterns to speed up root cause identification and avoid repeating past mistakes.
  • Use Case: If your marketing automation service is throwing 500 errors on lead export after a recent update, this Skill guides you to trace the root cause (e.g., missing API permission, schema change) instead of just restarting the service.

Quick Start

Use the investigate skill to find the root cause of the 500 error we're seeing on the /api/leads endpoint after the last deploy.

Frequently Asked Questions about investigate

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

FAQPage Schema
How do I find the root cause of a 500 error instead of just restarting the service?

Root cause analysis for 500 errors requires following a structured investigate-analyze-hypothesize-implement workflow to trace underlying issues like missing API permissions or schema changes rather than applying ineffective band-aid fixes to broken software systems.

What is the best way to debug unexpected software regressions after a deployment?

Debugging post-deploy regressions is best handled by enforcing a systematic four-phase investigation process that analyzes stack traces and prior project learnings to confirm the root cause before implementing any fixes for functionality that previously worked.

Why do my troubleshooting fixes result in recurring bugs and unexpected behavior?

Recurring bugs happen when unstructured debugging workflows lead to symptom-level fixes, which can be resolved by enforcing a mandatory root cause investigation workflow that ensures fixes address the underlying issue rather than temporary band-aid patches.

How do I perform step-by-step error investigation on a broken software system?

Step-by-step error investigation follows a structured workflow: investigate the reported error, analyze the context against prior history, hypothesize the root cause, and implement the confirmed fix to prevent unexpected behavior from recurring.

Can I automatically trigger incident response for stack traces without manual invocation?

Yes, proactive auto-activation triggers the incident response workflow automatically when users report errors, stack traces, or unexpected behavior, pulling cross-project patterns to speed up troubleshooting without requiring manual skill invocation.

When should I use a structured root cause analysis workflow for software troubleshooting?

Structured root cause analysis should be used whenever you encounter 500 errors, stack traces, unexpected regressions, or broken software functionality to ensure systematic troubleshooting eliminates the underlying issue rather than just treating symptoms.