This project is a centralized repository for the collection and analysis of system instructions and behavioral configurations extracted from large language models and AI-powered software. It serves as a research archive that documents the internal directives, operational constraints, and safety protocols that define how various artificial intelligence agents interact with users.
The repository distinguishes itself through a crowdsourced approach to data aggregation, maintaining a historical record of configuration changes across a wide range of proprietary models and coding assistants. By organizing these findings into structured, version-controlled datasets, it enables security researchers and developers to audit model alignment, investigate potential information disclosure risks, and observe the structural patterns used in production-grade prompt engineering.
The project covers a broad capability surface, including the study of hidden behavioral constraints and the auditing of autonomous agent guidelines. It utilizes standardized, human-readable tabular storage to ensure that the collected data remains accessible for comparative analysis. The entire dataset is presented through a searchable, static web interface that tracks updates and modifications over time.
