novel-analyzer

Analyze complete novels into metadata, chunk analyses, and reports.

7|1|Updated Dec 8, 2025
One-click install
npx skills add https://github.com/waylon256yhw/novel-analysis-skill --skill novel-analyzer
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: novel-analyzer
Source: https://github.com/waylon256yhw/novel-analysis-skill/tree/main
Command: npx skills add https://github.com/waylon256yhw/novel-analysis-skill --skill novel-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill orchestrates a four-stage workflow to deeply analyze long-form novels, generating metadata, chunked analyses, a Chinese synthesis report, and optional character dossiers, reducing manual effort.

Core Features & Use Cases

  • Parallel chunking: automatically split large texts and run multiple agents in parallel to speed up analysis.
  • Character tracking: optional tracking of a specified character across chunks to build a cohesive dossier.
  • Chinese reporting: outputs a Chinese depth report with original quotes preserved.
  • Outputs and archiving: emits metadata.json, chunk_*.json, report.md, and optional character profile files for synthesis.

Quick Start

Start the workflow by providing a novel text file path to trigger the four-stage analysis and retrieve all outputs.

Frequently Asked Questions about novel-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze a long novel efficiently without manual reading?

To analyze a long novel efficiently, this skill uses a parallel chunking workflow that splits large texts and runs multiple agents simultaneously to produce structured metadata, chunked analyses, and a synthesized Chinese report.

Can I generate character profiles for a specific character across a full book?

Yes, you can generate character profiles by enabling optional character tracking, which monitors a specified character across all text chunks to build a cohesive dossier with original quotes preserved.

What is the best way to perform deep reading and literary analysis on long texts?

The best way to perform deep reading on long texts is using a four-stage parallel workflow that orchestrates multiple general-purpose agents to ensure coherent synthesis, robust outputs, and structured literary analysis.

Does this novel analysis workflow support archiving structured outputs?

Yes, the novel analysis workflow supports archiving by emitting structured artifacts including metadata.json, chunk_*.json files, report.md, and optional character profile files for downstream synthesis.

How do I get a Chinese synthesis report from a complete novel text?

To get a Chinese synthesis report, provide a novel text file path to trigger the four-stage analysis, which automatically generates a depth report in Chinese while preserving original quotes from the source material.