experimental-self-prompting

This approach creates context-free prompts to reframe stalled analysis, mitigating anchoring bias and context-window pollution. It provides a fresh perspective for complex code reviews without relying on external models.

A context-free prompt for adversarial analysis is a self-crafted instruction set understandable without prior conversation knowledge. It reframes target system assessments independently to prevent context-window pollution and bias.

Yes, self-prompting applies to pre-commit verification by generating independent analysis prompts for complex systems. It mandates manual confirmation of each finding due to a roughly 20% false-positive rate.

No, external models are explicitly blocked to prevent hallucinations. The skill self-generates comprehensive prompts internally to maintain a safe, context-free verification workflow for the target system.

Limitations include a roughly 20% false-positive rate, requiring manual verification of all findings before action. It is designed for stalled analysis and should not replace standard review processes without independent confirmation.