.janicre: A Minimal Structural Specification Language for LLM-Aware Software Systems

Large language models (LLMs) promise repository-scale assistance, yet real projects often exceed 128\,k-token windows and scatter key logic across many files and languages. Existing approaches like RAG or long-context prompting either leak code, fail at cross-file reasoning, or incur quadratic cost. We introduce \texttt{.janicre}, a minimal intermediate representation designed specifically for LLM understanding. It converts diverse programming artifacts—Python, JavaScript, HTML, etc.—into a single JSON–YAML manifest that expresses the system’s \emph{purpose, hierarchy, semantics, theory, and constraints} in a unified way. This schema allows LLMs to grasp what is being built, how modules relate, and why specific algorithms apply—without exposing private code. We formalise the schema, analyse its $\mathcal{O}(\log N)$ token complexity, and propose an empirical evaluation plan to compare code understanding and reasoning against standard retrieval or full-file prompting. **This work provides a new framework for large-scale software analysis using LLMs through structured intermediate representations.**