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+# Contribution Guidelines
+
+This document outlines the contribution protocols for the scientific software engineering project focusing on bio-inspired control architectures for brittle-star-like robots. The primary objective of this project is to produce scientific insight, rather than a commercial product.
+
+## 1. Scientific Context & Methodology
+
+* **Research Focus:** The goal is to study how controller modularity affects learning speed, coordination, and fault tolerance in brittle-star locomotion.
+* **Hypothesis-Driven Design:** Clear hypotheses must dictate a structured methodology and rigorous evaluation. All design decisions must be formally documented prior to implementation.
+* **Scaffolding Approach:** Development must start with simple setups before progressively increasing the complexity of environments and morphologies.
+* **Evaluation of Results:** Negative results possess scientific validity when thoroughly analyzed. If a controller fails to learn locomotion, providing a comprehensive analysis of the failure is considered a strong scientific contribution.
+* **Reproducibility:** Contributors must utilize fixed library versions. Configuration systems (such as json, gin, or yaml) must be employed to ensure reproducible runs.
+
+## 2. Clean Code & Code Quality
+
+Code readability is paramount, as code is read far more frequently than it is written.
+
+* **Naming Conventions:** Variables and functions must utilize consistent, intention-revealing names. A long, descriptive name is strictly preferred over a short name accompanied by a comment.
+* **Function Design:** Functions must be modular and adhere to the single responsibility principle. Arguments must be minimized, and boolean flag arguments controlling behavior should be avoided.
+* **Commenting:** Code must document the "how," while comments are strictly reserved for documenting the "why". Commented-out code is prohibited and must be deleted via version control.
+* **YAGNI:** Contributors must adhere to the "You Aren't Gonna Need It" (YAGNI) principle and actively avoid premature optimization.
+* **Notebooks:** Jupyter Notebooks are strictly limited to quick prototyping, tutorials, demonstrations, or post-processing analysis. They are explicitly forbidden for general software development because they discourage modularity.
+
+## 3. Version Control & Repository Structure
+
+* **Git Practices:** Commits must be frequent and small. Each commit should relate to exactly one piece of functionality.
+* **Branching Strategy:** The `dev` branch serves as the integration branch for pushing and merging code. Only stable releases may be pushed to the `main` branch.
+* **Artifact Management:** Data files, trained models, and large datasets must never be committed directly to Git. Git Large File Storage (LFS) must be used for tracking large files.
+* **Repository Layout:** The repository must maintain the following core directories: `src/` for algorithms, `env/` for MuJoCo wrappers, `config/` for experiment configurations, `experiments/` for scripts, `docs/` for Doxygen or ReadTheDocs documentation, and `tests/` for unit tests.
+
+## 4. Architecture & Tooling
+
+* **Algorithms & Frameworks:** Proximal Policy Optimization (PPO) is the recommended baseline algorithm. CleanRL should be used as a starting point and adapted for continuous action spaces. All Artificial Neural Network (ANN) controller architectures must be implemented using Flax.
+* **Simulation:** The simulation environment utilizes a MuJoCo brittle star. XML MuJoCo structures must remain realistic and respect morphological constraints.
+* **Experiment Tracking:** Weights & Biases (wandb) must be utilized for tracking and logging all experiments.
+* **Code Styling:** All code must conform to the chosen style guide (i.e. Google standard). This is enforced using build tools and pre-commit hooks such as flake8, black, or isort.