What problem does it solve? Rendering R Markdown or Python analysis reports often fails repeatedly due to missing packages, S4 object API changes, wrong paths, or memory limits, forcing tedious multi-round debugging sessions. This Skill collapses that cycle into a single autonomous invocation that diagnoses errors, applies fixes, and re-renders without asking the user for guidance. ## Core Features & Use Cases - Pre-flight validation: Verifies input files, required R/Python packages, and disk space before rendering to avoid wasted runs. - Error pattern matching and auto-fix: Maps common failures (Seurat v5 Assay5 coercion, missing metadata columns, duplicate chunk labels, out-of-memory) to concrete fixes, then re-renders up to 5 times. - Local and SBATCH rendering: Chooses between direct rmarkdown::render for small reports and SLURM SBATCH job submission for large reports exceeding 16GB RAM. - Output validation: Checks HTML size, embedded plot counts, and NA/NULL artifacts, then optionally delivers results to gStore. - Use Case: A bioinformatician asks to render a Seurat single-cell report on the FGCZ cluster; the Skill detects a missing JoinLayers() call causing an Assay5 coercion error, patches the Rmd, submits an SBATCH job, validates the HTML, and reports the result. ## Quick Start Render the report.Rmd file in the current analysis directory, automatically fixing any errors and validating the HTML output.
