**Architecture**

MetaCA Studio is a small Python-first toolkit to prototype federated evolutionary cellular automata. The repository's current scope is a well-focused, test-covered core providing:

- CARule: rule representation with deterministic fingerprinting for deduping across federated agents.
- CAGrid: 2D toroidal grid with Moore and Von Neumann neighborhoods and a step operator applying CARule transition tables.
- TournamentSelector: an evolutionary selection primitive exposing top-k shares for gossip-style federated exchange.

This agent/design follows a layered architecture:

- metaca.py: core domain objects and algorithms (kept small and well-tested).
- pyproject.toml: packaging metadata to allow building sdist/wheel.
- test.sh: unified test driver used by CI and contributors.

Future work should add separate modules for differentiable simulators (JAX/PyTorch), federation registry (Graph-of-Contracts sketch), adapters (NumPy/JAX/torch), and a lightweight governance ledger.

**Tech stack**

- Python 3.8+ (core language)
- Packaging: setuptools + wheel via PEP 517/518 (pyproject.toml)
- Tests: simple shell-driven assertions (test.sh). Replace with pytest as codebase grows.

**Testing commands**

Run the project's test suite and build verification with:

```
./test.sh
```

test.sh will run a packaging build (python3 -m build) to verify pyproject metadata and then execute the unit-style tests included in the script.

**Contribution rules**

- Keep changes minimal and well-scoped. Prefer small, reviewable patches over large rewrites.
- Add tests for every new feature or bugfix using the pattern in test.sh. If you introduce dependencies, add them to pyproject.toml.
- Do not modify files authored by other contributors without discussion.
- Run `./test.sh` locally before opening PRs.

**Release & publishing**

This repository is not marked READY_TO_PUBLISH in this iteration. When claiming readiness:

- Ensure README.md contains a complete project summary and packaging metadata references.
- Ensure pyproject.toml contains accurate metadata and licensing information.
- Add an empty file READY_TO_PUBLISH to the repository root only when the project fully implements the published specification and all tests pass.