Shows the pacing of character appearances throughout the manuscript by counting literal name mentions in each chapter. Use this to identify if a main character vanishes for long stretches.
Word Count per Location
Shows which locations dominate the manuscript. Location data is read strictly from the where: or location: tags in each scene's frontmatter, or defaults to its parent folder name.
Character Co-Occurrence
Shows which characters share the most "screen time" by counting the number of scenes where both are mentioned together. Use this to identify your strongest character pairings and isolated subplots.
Book-Wide Lexical Health
These metrics check if your vocabulary is rich and varied, or if you are over-repeating words.
Unique Words % (Local):
Checks if you are echoing the same words too closely together. This explicitly tells you that, on average, of your words in a local window are unique. Example of a low score: "He opened the door and walked through the door, closing the door."
Repetitive
Varied
Words Before Repetition (Scene):
Checks the overall size of your vocabulary across a scene. This explicitly tells you that, on average, you write before you start heavily repeating yourself. Example of a low score: A 1,000 word dialogue scene where characters only say "Yes", "No", and "Okay".
Limited Vocab
Rich Vocab
Lexical Health Map
Plots every scene's vocabulary diversity. The green outlined box represents your Genre Target. Scenes falling below the green box are highly repetitive. Scenes inside or above the green box have excellent vocabulary range.
Scene Deep Dive
Project Configuration
Proseview is configured entirely by a plain text file: .proseview.yaml.
You can edit these settings here, or
open the file in your editor.
General Goals
Analysis Targets
Story Elements
Characters:14
Locations:Auto-discovered
Backup Management
Proseview stores snapshots of your scenes locally to support undo/redo and file history.