What problem does it solve? When analyzing a person's writing or content history over months or years, their interests change — but it is hard to tell whether a change is a genuine migration, gradual drift, or just temporary exploration. This Skill turns a timestamped, category-labeled corpus into a quantitative timeline of interest shifts, identifying when shifts happened, how large they were, and which era best represents the current voice. ## Core Features & Use Cases - Adaptive temporal windowing: Builds windows based on item counts rather than fixed calendar periods so every window has a stable category distribution. - Divergence-based shift detection: Computes Jensen-Shannon Divergence between adjacent windows and cumulative drift from baseline, then applies PELT change point detection via the ruptures library. - Shift validation and era segmentation: Classifies each shift as permanent, transient, or minor fluctuation using magnitude, duration, and reversion checks, then characterizes each era with dominant categories and Shannon entropy. - Use Case: Given two years of categorized Reddit posts, detect that the author migrated from gaming content to personal finance around a specific month, confirm the shift was permanent, and produce a report identifying the current voice era for downstream voice modeling. ## Quick Start Analyze my categorized post history for interest shifts over time and write the findings to docs/analysis/13-taxonomic-shift-detection.md.
