What problem does it solve? When an AI agent hits a tool error, an unexpected query result, or a change that silently fails to take effect, it often falls into guess-and-retry loops that waste time and corrupt state. This Skill enforces a disciplined debugging process so every fix is grounded in verified evidence rather than speculation. ## Core Features & Use Cases - Five-step discipline: List verifiable symptoms, form 2-3 falsifiable hypotheses, gather evidence with read-only tools, lock the root cause, then fix only what the evidence points to. - Anti-pattern guardrails: Explicitly bans blind retries, random field-name changes, and bypass hacks like wrapping errors in conditional checks. - Use Case: A Python plugin registers successfully but the client shows no effect. Instead of re-registering, the agent checks the parent extension FID, inspects the plugin list output, and discovers a typo in the event signature BeforeSave_1, then fixes, re-registers, and verifies. ## Quick Start Load the systematic-debugging skill and walk me through why my plugin registration succeeded but nothing shows up in the client.