abczsl520/debug-methodology
Systematic debugging methodology for AI agents and developers. Prevents common anti-patterns like patch-chaining and wrong-environment restarts.
Provides a four-phase debugging workflow (STOP → THINK → TEST → DETECT) with decision trees and environment checklists that force root-cause analysis before patching. Integrates as an OpenClaw agent skill via ClawHub, automatically injecting the methodology into AI agent sessions when debugging scenarios are detected. Combines production incident learnings with established practices from systems engineering experts like Brendan Gregg and Julia Evans.
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License
MIT
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Last pushed
Mar 06, 2026
Commits (30d)
0
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