# SpaceSafeML: Certification, Benchmark, and Governance Framework for Onboard AI in Space Robotics

This repository provides a minimal, open-source MVP of a modular framework to certify and benchmark onboard AI agents operating in space robotics contexts. It includes a Safety DSL, a verification harness, a lightweight simulation scaffold, a governance ledger, and starter adapters for common onboard stacks.

What you can expect in this MVP
- A Python package named `spacesafeml_certification_benchmark_and_` with core modules:
  - DSL definitions for LocalCapabilities, SafetyPre/SafetyPostConditions, ResourceBudgets, and DataSharingPolicies
  - A simple verification engine that can generate safety certificates for plans
  - A tiny simulation scaffold with placeholder Gazebo/ROS-like interfaces for fleet scenarios (deterministic and replayable)
  - A tamper-evident ledger to audit test results
  - Starter adapters for planning and perception modules
- A basic test suite to validate core behavior and a test launcher script `test.sh` that runs tests and packaging verification
- Documentation file `AGENTS.md` describing architecture and contribution rules

Getting started
- Install Python 3.8+ and run tests via `bash test.sh`.
- Explore the MVP modules under `spacesafeml_certification_benchmark_and_`.

This project intentionally remains minimal yet extensible to accommodate future MVP expansion consistent with the SpaceSafeML vision.

## License

MIT