What problem does it solve?
When an AI agent gets stuck in a loop—retrying the same fix, defending a failed approach, or spiraling into repeated apologies—its output quality degrades rapidly. This Skill breaks that cycle by forcing a structured reset before the next tool call.
Core Features & Use Cases
- Loop Detection Triggers: Activates on recognizable failure patterns like the same fix tried 3+ times, contradictory debugging theories, or repeated mistakes in a row.
- Guided Reset Protocol: Provides affirmations to read before the next action, followed by a three-step re-grounding process: state verified facts, name the unchecked assumption, and take one small test step.
- Behavioral Rules and Gotchas: Enforces discarding failed theories (not working code), limits user-facing acknowledgment to one sentence, and warns against using the reset on first-time difficulties.
- Use Case: An agent debugging a failing test has tried five variations of the same fix with no progress. It invokes this Skill, re-reads the actual error output, identifies the assumption it never verified, and runs a single targeted check that reveals the real cause.
Quick Start
Use the affirmations skill to reset your approach since the same fix has failed three times and debugging is going in circles.