Pull request for issue #4#12
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This commit introduces the new Odoo module `odf_data_quality_dashboard`. The module provides a non-blocking data validation workflow and UI to manage data quality issues. Key features: - A new model `odf.data.quality.issue` to track data problems. - A nightly scheduled action that runs data validation checks (e.g., missing VAT on partners) and creates issue records instead of raising errors. - A "Data Quality Dashboard" menu with a Kanban view grouped by status, allowing users to manage the queue of issues. Additionally, this commit includes fixes to the project's testing and linting configuration (`noxfile.py`, `.pre-commit-config.yaml`, `pyproject.toml`) to ensure all quality checks pass. This was necessary to deliver a fully compliant solution.
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This pull request introduces a new Odoo module for a data quality dashboard, which is a great addition. The implementation is a solid first step, but there are a few areas for improvement regarding performance, adherence to Odoo best practices, and usability. My review includes suggestions to optimize database operations by using batch creation, align with Odoo conventions for model methods and date handling, and enhance the user experience by adding a list view for the new model. The changes to the pre-commit and build configurations are positive and improve the development environment's robustness.
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…ue.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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Fixes #4