What problem does it solve? Producing consistent, publication-quality figures for topological data analysis (TDA) papers is error-prone when sizes, colors, and save conventions are chosen ad hoc. This Skill scaffolds matplotlib code that strictly follows the trajectory_tda/viz conventions so every figure matches the project's publication standards. ## Core Features & Use Cases - Standardized styling: Enforces use of PUBLICATION_RC, DPI, FIGSIZE_* constants, and STATE_COLORS from trajectory_tda/viz/constants.py — never ad-hoc sizes or colors. - Figure templates: Provides templates for persistence diagrams, barcodes, null-distribution histograms, and landscape comparison plots. - Output conventions: Routes production figures to papers/PXX/figures/ with fig{N}_ naming and working figures to figures/{domain}/ with date-prefixed names. - Use Case: When writing paper P01-B, ask for a persistence diagram figure and receive matplotlib code that imports the correct constants, applies annotation standards (exact p-values, no top/right spines, frameless legends), and saves both PDF and PNG outputs. ## Quick Start Ask the assistant to generate a publication-ready persistence diagram figure for paper P01-B following the trajectory_tda viz conventions.
