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Reverse Outliner

Community
jwynia
reverse-outliner

Reverse-engineer published books into structured scene-by-scene outlines for study. Use when analyzing craft, learning story structure from masters, or creating teaching materials from existing works.

Overview

Publisherjwynia
Repositoryagent-skills
Skill namereverse-outliner
Stars
159
Forks
20
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jwynia on GitHub. Read the source before you install it.

Installation

Install the Reverse Outliner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jwynia/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/creative/fiction/structure/reverse-outliner .claude/skills/reverse-outliner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reverse Outliner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Reverse Outliner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Reverse Outliner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Key Questions:

  • Does the book have explicit chapter markers?
  • Are scene breaks marked with whitespace, symbols, or POV shifts?
  • What's the typical scene length for this genre?

Interventions: Run segment-book.ts to identify chapters and scenes.

RO2: Segmented, Unanalyzed

Symptoms: Chapters/scenes identified but no structural analysis performed.

Key Questions:

  • How many scenes total?
  • Ready to begin scene-by-scene analysis?

Interventions: Run analyze-scene-batch.ts for G/C/D analysis.

RO3: Genre Unidentified

Symptoms: Scenes analyzed but genre-specific Key Moments not mapped.

Key Questions:

  • What's the primary elemental genre?
  • Are there secondary genres?
  • Which Key Moments framework applies?

Interventions: Run detect-genre.ts, then map Key Moments.

RO4: Characters Untracked

Symptoms: Scenes and genre mapped but character arcs not traced.

Key Questions:

  • Who is the protagonist?
  • Which 3-5 secondary characters are most significant?
  • Which arc type does each follow?

Interventions: Run track-characters.ts to identify and trace arcs.

RO5: Ready for Synthesis

Symptoms: All analysis complete, ready to generate outline.

Key Questions:

  • What output depth? (summary, standard, detailed)
  • Include all scenes or significant only?

Interventions: Run generate-outline.ts to produce markdown output.

RO6: Outline Complete

Symptoms: Markdown outline generated and available.

Key Questions:

  • Does the outline capture the book's structure?
  • Are there gaps or scenes that need manual review?

Interventions: Manual refinement, export, or comparison studies.

Diagnostic Process

  1. Determine current state by checking what files/analysis exist
  2. Identify next intervention based on state table above
  3. Run appropriate tool to advance to next state
  4. Validate output before proceeding
  5. Iterate until RO6 reached

Available Tools

segment-book.ts

Segments raw book text into chapters and scenes.

bash
deno run --allow-read scripts/segment-book.ts book.txt [options]

Options:

  • --chapter-pattern <regex> - Custom chapter detection pattern
  • --scene-break <marker> - Custom scene break marker
  • --output <file> - Output JSON file (default: stdout)

Output: JSON with chapters, scenes, line ranges, word counts.

analyze-scene-batch.ts

Applies scene-sequencing analysis (Goal/Conflict/Disaster) to all scenes.

bash
deno run --allow-read scripts/analyze-scene-batch.ts segments.json book.txt [options]

Options:

  • --depth quick|standard|detailed - Analysis depth
  • --output <file> - Output JSON file

Output: JSON with G/C/D analysis per scene.

detect-genre.ts

Identifies primary and secondary elemental genres from text patterns.

bash
deno run --allow-read scripts/detect-genre.ts book.txt [options]

Options:

  • --sample-size <n> - Number of scenes to sample (default: 10)
  • --output <file> - Output JSON file

Output: JSON with genre detection and Key Moments mapping.

track-characters.ts

Identifies protagonist and major characters, tracks their arcs.

bash
deno run --allow-read scripts/track-characters.ts segments.json book.txt [options]

Options:

  • --protagonist <name> - Specify protagonist name
  • --max-secondary <n> - Max secondary characters (default: 5)
  • --output <file> - Output JSON file

Output: JSON with character arcs and key scene references.

generate-outline.ts

Synthesizes all analysis into structured markdown outline.

bash
deno run --allow-read --allow-write scripts/generate-outline.ts [options]

Options:

  • --segments <file> - Segments JSON
  • --scenes <file> - Scene analysis JSON
  • --genre <file> - Genre detection JSON
  • --characters <file> - Character tracking JSON
  • --depth summary|standard|detailed - Output depth
  • --output <file> - Output markdown file

reverse-outline.ts (Orchestrator)

Runs full pipeline from book.txt to outline.md.

bash
deno run --allow-read --allow-write scripts/reverse-outline.ts book.txt [options]

Options:

  • --output <dir> - Output directory (default: ./reverse-outlines/{book-name}/)
  • --depth quick|standard|detailed - Analysis depth
  • --protagonist <name> - Specify protagonist
  • --genre <type> - Override genre detection

Output: Directory containing outline.md and analysis/ folder with all intermediate JSON.

Anti-Patterns

Surface-Level Breakdown

Problem: Outline lists what happens but not why. Fix: For each scene, ask: what structural function does this serve? What would break if it were removed?

Genre-Blind Analysis

Problem: Applying thriller patterns to romance or vice versa. Fix: Always detect genre first; use genre-appropriate Key Moments.

Protagonist Assumption

Problem: Assuming first POV character is protagonist. Fix: Track goal-attachment and arc presence across all POV characters.

Scene Boundary Guessing

Problem: Treating paragraph breaks as scene breaks. Fix: Use multiple detection strategies; prefer conservative segmentation with manual review.

What You Do NOT Do

  • Generate original story content
  • Judge the book's quality
  • Compare to other books unless asked
  • Skip states (each builds on previous)
  • Modify the source text

Output Persistence

This skill writes primary output to files so work persists across sessions.

Output Discovery

Before doing any other work:

  1. Check for context/output-config.md in the project
  2. If found, look for this skill's entry
  3. If not found, create output at ./reverse-outlines/{book-name}/

Primary Output

For this skill, persist:

  • outline.md - Final markdown outline
  • analysis/segments.json - Chapter/scene segmentation
  • analysis/scenes.json - Scene-by-scene G/C/D analysis
  • analysis/genre.json - Genre detection results
  • analysis/characters.json - Character arc tracking

Conversation vs. File

Goes to FileStays in Conversation
Segment dataClarifying questions
Scene analysisDiscussion of methodology
Genre detectionOptions for ambiguous cases
Character arcsReal-time feedback
Final outlineWriter's exploration

Integration Graph

Inbound (From Other Skills)

Source SkillSource StateLeads to StatePurpose
story-senseSS7: Ready for EvaluationRO0Analyze published work for comparison to own
dna-extractionEX7: Extraction CompleteRO5Compare extracted functions to detected structure

Outbound (To Other Skills)

This StateLeads to SkillTarget StatePurpose
RO6: Outline Completestory-zoomZ2Map published book against own structure
RO6: Outline Completescene-sequencingSQ1Use as reference for scene structure
RO6: Outline Completecharacter-arcCA1Use as reference for arc design
RO6: Outline Completegenre-conventionsGC1Study genre execution

Complementary Skills

SkillRelationship
scene-sequencingCore G/C/D analysis patterns reused
genre-conventionsGenre detection patterns sourced
character-arcArc type identification patterns sourced
dna-extractionFunction taxonomy borrowed
story-zoomOutput format compatible for comparison
revisionSimilar structural analysis approach

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Reverse Outliner AI skill do?

Reverse-engineer published books into structured scene-by-scene outlines for study. Use when analyzing craft, learning story structure from masters, or creating teaching materials from existing works.

Why use Reverse Outliner on TypingMind?

Because you install it once and use it with any model. Reverse Outliner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Reverse Outliner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jwynia/agent-skills/tree/main/skills/creative/fiction/structure/reverse-outliner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Reverse Outliner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Reverse Outliner?

As many as you like. As long as a model supports skills, you can use Reverse Outliner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Reverse Outliner AI skill free?

Yes. It is published on GitHub by jwynia under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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