What problem does it solve? Starting a new analysis often leads to inconsistent notebook naming, missing context, and undocumented methodology that collaborators cannot follow. This Skill enforces a consistent convention for creating analysis notebooks so every exploration is reproducible, readable, and properly organized. ## Core Features & Use Cases - Standardized Naming and Structure: Creates date-prefixed notebooks (YYYYMMDD_descriptive_name.qmd) in the analysis/ directory with correct Quarto YAML headers for Python (py_general kernel) or R kernels. - Prose-First Documentation: Enforces writing conventions where notebooks open with the research question, introduce every plot and table, justify methodological choices, and close with conclusions. - Path and Output Conventions: Directs data access through relative paths and routes small outputs to output/ and large files to code/scratch/. - Use Case: When starting a new exploration like splice site conservation analysis, invoke this Skill to scaffold a properly named Quarto notebook with the right kernel, setup chunks, and documentation structure, then render it with render_notebook. ## Quick Start Create a new analysis notebook for exploring differential gene expression results using the Python kernel.
