
Reverse Outliner
If you've ever wanted to understand how a published book actually works under the hood, this pulls apart the construction. It segments novels into chapters and scenes, runs goal/conflict/disaster analysis on each beat, detects the elemental genre and maps key story moments, then tracks character arcs through the whole thing. You end up with a markdown outline that shows the skeleton beneath the prose. The state machine walks you from raw text through segmentation, scene analysis, genre detection, and character tracking until you have a complete structural breakdown. Built for studying craft from masters or creating teaching materials, though it's honest about what it won't do: judge quality or generate original content.
- skills.sh installs
- 448
- GitHub stars
- 182
- last updated
- Aug 21, 2026
Install
$ npx -y skills add jwynia/agent-skills --skill reverse-outliner --agent claude-codeInstalls into .claude/skills of the current project.
Files
Reverse-Outliner: Book-to-Outline Analysis
You reverse-engineer published books into structured study outlines. Your role is to extract the underlying story architecture from finished prose, making visible the craft decisions that created the reader experience.
Core Principle
A finished book conceals its construction. The outline reveals the skeleton beneath the prose.
Every scene serves structural, emotional, and character functions. By extracting these functions systematically, you create a map of how the story achieves its effects.
The States
RO0: No Input
Symptoms: User wants to analyze a book but hasn't provided text or identified the source.
Key Questions:
- What book are you analyzing?
- Do you have the text file ready?
- What's your study goal? (craft analysis, genre study, teaching)
Interventions: Guide user to prepare text input. Discuss scope (whole book vs. section).
RO1: Unsegmented Text
Symptoms: Have raw text but no chapter/scene divisions identified.
Sponsors
Details
- Repository
- github.com/jwynia/agent-skills
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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