harkanatta ormar .cursorrules file for HTML

<Rules>
    <GeneralPrinciples>
        <Simplicity>
            <Rule>Write code that does the job without unnecessary complexity (KISS). Avoid overengineering or adding extra features unless absolutely necessary (YAGNI).</Rule>
            <Rule>Keep responses concise and direct by default unless comprehensive explanations are explicitly requested.</Rule>
        </Simplicity>
        <Focus>
            <Rule>Each function or script should have a single clear goal. Avoid making a script or function handle multiple unrelated tasks (Curly's Law).</Rule>
            <Rule>Stick to the main task and ensure your results directly answer the research questions posed.</Rule>
        </Focus>
        <Collaboration>
            <Rule>Ask questions to remove ambiguity and confirm assumptions, ensuring accuracy in responses and solutions.</Rule>
            <Rule>If you don’t know something, acknowledge it clearly and seek clarification or additional information.</Rule>
        </Collaboration>
    </GeneralPrinciples>
    <ProjectStructure>
        <DirectoryStructure>
            <Rule>Organize projects into clear folders like `data/`, `scripts/`, and `output/` to maintain clarity and separation of tasks.</Rule>
            <Rule>Ensure raw data remains untouched in `/data/` while transformed or cleaned outputs are saved separately in `/output/`.</Rule>
        </DirectoryStructure>
        <FileManagement>
            <Rule>Use simple naming conventions for files to track versions (e.g., `data_cleaned_v1.csv`).</Rule>
            <Rule>Maintain a logical and consistent structure in R Markdown files for analysis and reports.</Rule>
        </FileManagement>
    </ProjectStructure>
    <CodePractices>
        <CleanCode>
            <Rule>Write clean and readable code that avoids unnecessary complexity. Code should be easy to follow without extensive comments.</Rule>
            <Rule>Follow consistent naming conventions for variables and functions to improve readability and understanding.</Rule>
        </CleanCode>
        <Reuse>
            <Rule>Refrain from duplicating code by creating functions for repeated tasks (DRY principle).</Rule>
            <Rule>Write modular scripts that can be reused and adapted without significant modification.</Rule>
        </Reuse>
        <ErrorHandling>
            <Rule>Ensure error handling is integrated into scripts to gracefully manage unexpected scenarios.</Rule>
            <Rule>Document assumptions and transformations clearly in the scripts to enhance transparency and reproducibility.</Rule>
        </ErrorHandling>
    </CodePractices>
    <DataAnalysis>
        <StatisticalMethods>
            <Rule>Use tools appropriate for ecological and biological research, such as `vegan` for multivariate analysis and `BBI` for ecological indices.</Rule>
            <Rule>Validate data integrity before analysis, using checks like `stopifnot()` or equivalent methods.</Rule>
        </StatisticalMethods>
        <Reproducibility>
            <Rule>Document data sources, transformations, and analysis methods in R Markdown for seamless replication.</Rule>
            <Rule>Use Docker for environment consistency, ensuring all dependencies are clearly listed and documented.</Rule>
        </Reproducibility>
    </DataAnalysis>
    <VersionControl>
        <GitUsage>
            <Rule>Track changes using Git with clear and specific commit messages focused on individual tasks or fixes.</Rule>
            <Rule>Avoid adding large data files to version control unless absolutely necessary. Instead, use external storage or references.</Rule>
        </GitUsage>
    </VersionControl>
    <Visualization>
        <Figures>
            <Rule>Create publication-quality visuals using `ggplot2`, ensuring clarity and relevance to the research questions.</Rule>
            <Rule>Use consistent scales, labels, and legends across all figures and tables to maintain visual coherence.</Rule>
        </Figures>
        <Outputs>
            <Rule>Save all graphs, tables, and visualizations in a dedicated `/output/` folder with descriptive filenames.</Rule>
            <Rule>Embed visualizations and summaries directly in R Markdown for seamless inclusion in reports.</Rule>
        </Outputs>
    </Visualization>
    <Documentation>
        <Inline>
            <Rule>Use inline comments sparingly but effectively to explain complex transformations or critical steps in the code.</Rule>
            <Rule>Document any assumptions or decisions made during data cleaning, analysis, or visualization.</Rule>
        </Inline>
        <Reports>
            <Rule>Write clear, concise narratives in R Markdown files to accompany analysis and results.</Rule>
            <Rule>Ensure all figures and tables are properly referenced in the accompanying text for clarity.</Rule>
        </Reports>
    </Documentation>
    <Collaboration>
        <Consistency>
            <Rule>Maintain consistency with established project structures, styles, and analytical methods when contributing to a collaborative effort.</Rule>
            <Rule>Align contributions with the tone, style, and expectations of the journal or team guidelines.</Rule>
        </Consistency>
        <Editing>
            <Rule>Focus on clarity and precision when reviewing or editing, ensuring all results are clearly explained and linked to research questions.</Rule>
            <Rule>Proofread thoroughly before finalizing or submitting any work to catch errors or inconsistencies.</Rule>
        </Editing>
    </Collaboration>
</Rules>