story-review

Automated narrative content review uses multi-agent assessment to evaluate structure, consistency, and prose. It loads rubric guidelines and spawns subagents like consistency-checker to identify issues and output a unified findings report with severity levels.

A multi-agent story review workflow dispatches subagents such as story-architect and narrative-writer to analyze chapters in parallel. It enforces a unified findings schema and rubric sources to ensure cross-agent results are aggregatable, sortable, and comparable.

Yes, you can review specific chapters, scenes, outlines, or prompts. The system supports full, lean, and solo modes, allowing targeted narrative analysis on specified passages while maintaining structural and character consistency checks.

Yes, rubric sources are required for AI narrative analysis. You can supply rubric guidelines as an external file or embedded data, which the system loads to enforce consistent evaluation criteria across all dispatched review subagents.

Evaluating platform compliance and prose quality in web novels is best handled by multi-agent assessment. It checks structural, character, prose, and platform-fit dimensions against loaded rubric guidelines, outputting severity-ranked findings for quick iteration.

Automated story review requires predefined rubric sources to function and cannot operate without them. While it identifies structural, consistency, and prose issues across chapters, it enforces a rigid findings schema that may not capture subjective creative nuances.