character-analysis

Character arc analysis evaluates development by processing StoryGraph JSON data to score arcs from 0-100, assessing start, midpoint, end, transformation, and emotional journey. It checks for action-trait conflicts and maps relationship dynamics across story acts.

Mapping character relationships involves processing story graph data to generate strength-rated relationship diagrams. The analysis highlights missing tension within the relational network and provides concrete suggestions to deepen under-developed character connections across acts.

Checking character motivation clarity involves analyzing story graph data to rate motivations as clear, vague, or missing. The process awards bonuses for narrative impact and provides targeted writing suggestions to strengthen weak character motivations.

You can analyze story characters without external APIs by processing StoryGraph JSON data internally. The analysis computes arc scores, generates relationship maps, and provides writing suggestions entirely through local processing without requiring external dependencies or components.

Character analysis works with StoryGraph JSON data by assessing each character's arc, consistency, and relational network across acts. It processes the structured narrative data to evaluate development stages and compute completeness scores for multi-act story structures.

Character consistency checks flag actions conflicting with established traits or arc stages by evaluating story graph data. The analysis identifies mismatches between character behavior and their defined progression, highlighting inconsistencies that break narrative coherence across acts.