# Copyright © 2023-2026 ValidMind Inc. All rights reserved.
# Refer to the LICENSE file in the root of this repository for details.
# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

- id: code_overview
  title: Codebase Overview
  guidelines:
    - Describe the overall structure of the source code repository.
    - Identify main modules, folders, and scripts.
    - Highlight entry points for training, inference, and evaluation.
    - State the main programming languages and frameworks used.
  contents:
    - content_type: text
      content_id: code_structure_summary
- id: model_overview
  title: Model Overview
  guidelines:
    - Describe the overall structure of the source code repository.
    - Identify main modules, folders, and scripts.
    - Highlight entry points for training, inference, and evaluation.
    - State the main programming languages and frameworks used.
  contents:
    - content_type: text
      content_id: model_overview
- id: environment_setup
  title: Environment and Dependencies
  guidelines:
    - List Python packages and system dependencies (OS, compilers, etc.).
    - Reference environment files (requirements.txt, environment.yml,
      Dockerfile).
    - Include setup instructions using Conda, virtualenv, or containers.
  contents:
    - content_type: text
      content_id: setup_instructions
- id: data_interface
  title: Data Ingestion and Preprocessing
  guidelines:
    - Specify data input formats and sources.
    - Document ingestion, validation, and transformation logic.
    - Explain how raw data is preprocessed and features are generated.
  contents:
    - content_type: text
      content_id: data_handling_notes
- id: model_implementation
  title: Model Implementation Details
  guidelines:
    - Describe the core model code structure (classes, functions).
    - Link code to theoretical models or equations when applicable.
    - Note custom components like loss functions or feature selectors.
  contents:
    - content_type: text
      content_id: model_code_description
- id: training_pipeline
  title: Model Training Pipeline
  guidelines:
    - Explain the training process, optimization strategy, and hyperparameters.
    - Describe logging, checkpointing, and early stopping mechanisms.
    - Include references to training config files or tuning logic.
  contents:
    - content_type: text
      content_id: training_logic_details
- id: evaluation_pipeline
  title: Evaluation and Validation Code
  guidelines:
    - Describe how validation is implemented and metrics are calculated.
    - Include plots and diagnostic tools (e.g., ROC, SHAP, confusion matrix).
    - State how outputs are logged and persisted.
  contents:
    - content_type: text
      content_id: evaluation_logic_notes
- id: inference_pipeline
  title: Inference and Scoring Logic
  guidelines:
    - Detail how the trained model is loaded and used for predictions.
    - Explain I/O formats and APIs for serving or batch scoring.
    - Include any preprocessing/postprocessing logic required.
  contents:
    - content_type: text
      content_id: inference_mechanism
- id: configuration_management
  title: Configuration and Parameters
  guidelines:
    - Describe configuration management (files, CLI args, env vars).
    - Highlight default parameters and override mechanisms.
    - Reference versioning practices for config files.
  contents:
    - content_type: text
      content_id: config_control_notes
- id: testing_and_validation
  title: Unit and Integration Testing
  guidelines:
    - List unit and integration tests and what they cover.
    - Mention testing frameworks and coverage tools used.
    - Explain testing strategy for production-readiness.
  contents:
    - content_type: text
      content_id: test_strategy_overview
- id: logging
  title: Logging and Monitoring Hooks
  guidelines:
    - Describe logging configuration and structure.
    - Highlight real-time monitoring or observability integrations.
    - List key events, metrics, or alerts tracked.
  contents:
    - content_type: text
      content_id: logging_notes
- id: version_control
  title: Code and Model Versioning
  guidelines:
    - Describe Git usage, branching, tagging, and commit standards.
    - Include model artifact versioning practices (e.g., DVC, MLflow).
    - Reference any automation in CI/CD.
  contents:
    - content_type: text
      content_id: version_tracking_description
- id: security_and_compliance
  title: Security and Access Control
  guidelines:
    - Document access controls for source code and data.
    - Include any encryption, PII handling, or compliance measures.
    - Mention secure deployment practices.
  contents:
    - content_type: text
      content_id: security_policies_notes
- id: execution_examples
  title: Example Runs and Scripts
  guidelines:
    - Provide working script examples (e.g., `train.py`, `predict.py`).
    - Include CLI usage instructions or sample notebooks.
    - Link to demo datasets or test scenarios.
  contents:
    - content_type: text
      content_id: runnable_examples
- id: known_issues_and_todos
  title: Known Issues and Future Improvements
  guidelines:
    - List current limitations or technical debt.
    - Outline proposed enhancements or refactors.
    - Reference relevant tickets, GitHub issues, or roadmap items.
  contents:
    - content_type: text
      content_id: issues_and_improvements_log