build(agent): molt-b#d1f4fd iteration

README.md

@@ -1,21 +1,21 @@
-# CosmosMesh Privacy-Preserving Federated (MVP Scaffold)
+# CosmosMesh: Privacy-Preserving Federated Mission Planning (MVP)
 
-This repository contains a minimal MVP scaffold for the CosmosMesh project described in the original spec. It provides:
+CosmosMesh is a modular, offline-first coordination platform for heterogeneous space assets (rovers, drones, habitat modules, orbiting satellites) operating in deep-space fleets with intermittent communication.
-- A Python package scaffold named cosmosmesh_privacy_preserving_federated_
-- A basic pytest suite with a tiny smoke test
-- A pyproject.toml build configuration for packaging with setuptools
-- A lightweight test runner script (test.sh) that validates packaging and tests
-- Documentation to help future contributors understand how to extend the MVP
 
-How to run locally
+This repository hosts an MVP scaffold intended to demonstrate the core idea: privacy-preserving, federated planning via a compositional optimization layer atop a mesh communication substrate. The MVP includes:
-- Build the package: python3 -m build
+- A minimal Python package that exposes a tiny ADMM-like solver placeholder for distributed optimization.
-- Run tests: pytest -q
+- A simple smoke-test to verify packaging and basic API surface.
-- Run the full test script: bash test.sh
+- A lightweight test harness and packaging flow to validate build/install workflows.
 
-Project structure (high level)
+How to run the tests and build locally:
-- src/cosmosmesh_privacy_preserving_federated_/__init__.py
+- Run tests and packaging: ./test.sh
-- tests/test_basic.py
+- View the PM: package metadata is defined in pyproject.toml; README is included as the long description in packaging metadata.
-- pyproject.toml
+
-- AGENTS.md
+Notes:
-- README.md
+- This MVP is intentionally small. The real system would implement: data contracts, delta-sync, secure aggregation, DID-based identities, adapters, and a global assembly layer.
-- test.sh (added in a subsequent step)
+- The repository is structured to be extended incrementally with additional adapters, simulators, and governance features.
+
+This README will evolve as the MVP grows.
+
+Ready to publish marker:
+- When you’re ready to publish, a READY_TO_PUBLISH file will be created in the repo root.

pyproject.toml

@@ -6,6 +6,8 @@ build-backend = "setuptools.build_meta"
 name = "cosmosmesh-privacy-preserving-federated"
 version = "0.0.1"
 description = "Minimal MVP scaffold for CosmosMesh privacy-preserving federated mission planning."
+readme = "README.md"
+requires-python = ">=3.8"
 
 [tool.setuptools.packages.find]
 where = ["src"]

src/cosmosmesh_privacy_preserving_federated_/__init__.py

@@ -3,3 +3,12 @@
 def add(a: int, b: int) -> int:
     """Simple helper used for smoke tests in this scaffold."""
     return a + b
+
+# Expose a minimal ADMM-like solver as part of the MVP scaffold
+try:
+    from .admm import admm_step  # type: ignore  # noqa: F401
+except Exception:
+    # Optional for environments where admm.py isn't present yet
+    def admm_step(local_vars, shared_vars, rho=1.0):  # type: ignore
+        """Fallback stub if admm is not available."""
+        return local_vars, shared_vars

src/cosmosmesh_privacy_preserving_federated_/admm.py

@@ -0,0 +1,32 @@
+"""Minimal ADMM-like solver stub for CosmosMesh MVP.
+
+This module provides a tiny, asynchronous-friendly placeholder for an
+ADMM-like optimization step used in federated mission planning. The real MVP
+would implement a fuller asynchronous update with stale-gradient tolerance and
+deterministic reconciliation. This stub is intentionally small and deterministic
+to keep tests fast and side-effect free.
+"""
+from typing import Dict, Tuple
+
+
+def admm_step(local_vars: Dict[str, float], shared_vars: Dict[str, float], rho: float = 1.0) -> Tuple[Dict[str, float], Dict[str, float]]:
+    """Perform a single ADMM-like step.
+
+    This is a toy update that nudges each local variable toward the corresponding
+    shared variable by a factor determined by rho. In a real implementation, this
+    would also update dual variables and handle asynchronous, delayed messages.
+
+    Args:
+        local_vars: Per-agent local variables.
+        shared_vars: Global/shared variables (aggregated signals).
+        rho: Penalty parameter controlling the step size toward shared_vars.
+
+    Returns:
+        A tuple of (updated_local_vars, updated_shared_vars).
+    """
+    updated_local: Dict[str, float] = {}
+    for k, v in local_vars.items():
+        sv = shared_vars.get(k, 0.0)
+        updated_local[k] = v - rho * (v - sv)
+    # In this MVP, we do not mutate shared_vars; real ADMM would update duals.
+    return updated_local, dict(shared_vars)