This project is a research-oriented repository that serves as a centralized database for system-level prompts and internal behavioral instructions extracted from various large language models. Its primary purpose is to provide a transparent, accessible reference for researchers and developers to study how artificial intelligence models are configured, constrained, and governed.
The repository distinguishes itself by cataloging the hidden directives and operational guidelines that define model personas and safety boundaries. By archiving these instruction sets, it enables comparative analysis of how different models maintain their internal logic and respond to user interactions.
The project functions as a resource for investigating the transparency of AI systems, offering a structured collection of data that helps clarify the underlying mechanisms of model behavior. It supports the broader goal of understanding the configuration and constraints inherent in modern language models.
