reverse-outliner

📁 jwynia/agent-skills 📅 Jan 20, 2026
29
总安装量
29
周安装量
#7191
全站排名
安装命令
npx skills add https://github.com/jwynia/agent-skills --skill reverse-outliner

Agent 安装分布

claude-code 24
opencode 22
gemini-cli 21
antigravity 19
codex 19
trae 19

Skill 文档

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.

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.

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.

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.

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.

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.

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 File Stays in Conversation
Segment data Clarifying questions
Scene analysis Discussion of methodology
Genre detection Options for ambiguous cases
Character arcs Real-time feedback
Final outline Writer’s exploration

Integration Graph

Inbound (From Other Skills)

Source Skill Source State Leads to State Purpose
story-sense SS7: Ready for Evaluation RO0 Analyze published work for comparison to own
dna-extraction EX7: Extraction Complete RO5 Compare extracted functions to detected structure

Outbound (To Other Skills)

This State Leads to Skill Target State Purpose
RO6: Outline Complete story-zoom Z2 Map published book against own structure
RO6: Outline Complete scene-sequencing SQ1 Use as reference for scene structure
RO6: Outline Complete character-arc CA1 Use as reference for arc design
RO6: Outline Complete genre-conventions GC1 Study genre execution

Complementary Skills

Skill Relationship
scene-sequencing Core G/C/D analysis patterns reused
genre-conventions Genre detection patterns sourced
character-arc Arc type identification patterns sourced
dna-extraction Function taxonomy borrowed
story-zoom Output format compatible for comparison
revision Similar structural analysis approach