lab-readthrough

Name: lab-readthrough
Availability: InStock
Author: Orda-by-Move

Run a cross-batch consistency check across multiple episodes for narrative defects.

Updated May 28, 2026

Search Tags:#writing #consistency #narrative #editing #quality-assurance #novel

Ready to run

npx skills add https://github.com/Orda-by-Move/jhs-novel-lab-plugin --skill lab-readthrough

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lab-readthrough
Source: https://github.com/Orda-by-Move/jhs-novel-lab-plugin/tree/main/skills/lab-readthrough
Command: npx skills add https://github.com/Orda-by-Move/jhs-novel-lab-plugin --skill lab-readthrough

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the problem of cross-batch inconsistencies and structural defects that individual episode-level diagnostics fail to detect, such as timeline errors, echo effects, and dropped plot threads.

Core Features & Use Cases

Multi-Lens Analysis: Simultaneously evaluates continuity, echo patterns, pacing, plot consistency, and character voice across multiple episodes.
Adversarial Gatekeeping: Enforces a strict PASS/REVISE gate, requiring structural revisions for any critical defects before proceeding to evaluation.
Use Case: After completing a 10-episode batch, use this skill to ensure that the narrative flow, character tone, and foreshadowing remain consistent across the entire arc.

Quick Start

Use the lab-readthrough skill to perform a full cross-batch consistency check on the current novel project.

Frequently Asked Questions about lab-readthrough

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

FAQPage Schema

How do I check for plot holes and consistency issues across multiple novel episodes?▼

Cross-batch consistency checks identify narrative defects, pacing issues, and plot holes spanning multiple episodes. An adversarial gate evaluates continuity, echo patterns, pacing, foreshadowing, and character voice simultaneously to enforce strict PASS or REVISE outcomes before proceeding.

What is adversarial gatekeeping for long-form webnovel production?▼

Adversarial gatekeeping enforces a strict PASS or REVISE gate requiring structural revisions for any critical defects before proceeding to evaluation. It applies to long-form webnovel production pipelines demanding strict adherence to structural and character design documentation across episode batches.

How do I run a multi-lens diagnostic on a 10-episode novel batch?▼

Running a multi-lens diagnostic requires parallel execution of five specialized lenses validating continuity, echo, pacing, foreshadowing, and character voice. This ensures narrative flow and character tone remain consistent across an entire story arc after completing an episode batch.

Does cross-batch consistency checking work for dropped plot threads and timeline errors?▼

Cross-batch consistency checking works specifically for timeline errors, echo effects, and dropped plot threads that individual episode-level diagnostics fail to detect. It flags these structural defects across multiple episodes to maintain narrative flow and structural integrity.

When should I not use episode-level diagnostics for novel editing?▼

Episode-level diagnostics should not be used when you need to detect cross-batch inconsistencies spanning multiple episodes. They fail to identify timeline errors, echo effects, and dropped plot threads, requiring an adversarial cross-batch consistency check instead.