reverse-outliner

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Chinese

Reverse-Outliner: Book-to-Outline Analysis

Reverse-Outliner:书籍转大纲分析工具

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.
你需要将已出版书籍逆向处理为结构化的研究大纲,从成品文本中提取其底层故事架构,让构建读者阅读体验的写作技巧决策清晰可见。

Core Principle

核心原则

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.
成品书籍会隐藏其构建逻辑,而大纲则能揭示文本背后的故事骨架。
每个场景都承担着结构、情感和人物塑造的功能。通过系统提取这些功能,你可以绘制出故事如何实现其效果的蓝图。

The States

状态定义

RO0: No Input

RO0:无输入状态

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).
特征: 用户希望分析某本书,但尚未提供文本或明确书籍来源。
核心问题:
  • 你要分析哪本书?
  • 你是否已准备好文本文件?
  • 你的研究目标是什么?(写作技巧分析、体裁研究、教学材料制作)
处理措施: 引导用户准备文本输入,讨论分析范围(整本书或特定章节)。

RO1: Unsegmented Text

RO1:未分割文本状态

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.
特征: 已获取原始文本,但尚未识别章节/场景划分。
核心问题:
  • 该书是否有明确的章节标记?
  • 场景转换是否通过空白、符号或视角切换来标记?
  • 该体裁的典型场景长度是多少?
处理措施: 运行
segment-book.ts
脚本识别章节和场景。

RO2: Segmented, Unanalyzed

RO2:已分割未分析状态

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.
特征: 已识别章节/场景,但尚未进行结构分析。
核心问题:
  • 总共有多少个场景?
  • 是否准备好开始逐场景分析?
处理措施: 运行
analyze-scene-batch.ts
脚本进行G/C/D(目标/冲突/灾难)分析。

RO3: Genre Unidentified

RO3:未识别体裁状态

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.
特征: 已完成场景分析,但尚未映射体裁专属的关键节点。
核心问题:
  • 主要基础体裁是什么?
  • 是否有次要体裁?
  • 适用哪种关键节点框架?
处理措施: 运行
detect-genre.ts
脚本,然后映射关键节点。

RO4: Characters Untracked

RO4:未追踪人物状态

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.
特征: 已完成场景和体裁映射,但尚未追踪人物弧光。
核心问题:
  • 谁是主角?
  • 哪3-5个次要人物最重要?
  • 每个人物遵循哪种弧光类型?
处理措施: 运行
track-characters.ts
脚本识别并追踪人物弧光。

RO5: Ready for Synthesis

RO5:准备合成状态

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.
特征: 所有分析已完成,准备生成大纲。
核心问题:
  • 输出深度如何?(摘要版、标准版、详细版)
  • 是否包含所有场景还是仅重要场景?
处理措施: 运行
generate-outline.ts
脚本生成Markdown格式大纲。

RO6: Outline Complete

RO6:大纲完成状态

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.
特征: 已生成Markdown格式大纲并可使用。
核心问题:
  • 大纲是否准确呈现了书籍的结构?
  • 是否存在遗漏或需要人工审核的场景?
处理措施: 人工优化、导出或进行对比研究。

Diagnostic Process

诊断流程

  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
  1. 确定当前状态:检查已有的文件/分析内容
  2. 确定下一步处理措施:基于上述状态表
  3. 运行对应工具:推进至下一状态
  4. 验证输出:确认无误后再继续
  5. 迭代:直至达到RO6状态

Available Tools

可用工具

segment-book.ts

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.
将原始书籍文本分割为章节和场景。
bash
deno run --allow-read scripts/segment-book.ts book.txt [options]
选项:
  • --chapter-pattern <regex>
    - 自定义章节检测正则表达式
  • --scene-break <marker>
    - 自定义场景转换标记
  • --output <file>
    - 输出JSON文件(默认:标准输出)
输出: 包含章节、场景、行范围、字数统计的JSON文件。

analyze-scene-batch.ts

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.
对所有场景应用场景序列分析(目标/冲突/灾难,即G/C/D)。
bash
deno run --allow-read scripts/analyze-scene-batch.ts segments.json book.txt [options]
选项:
  • --depth quick|standard|detailed
    - 分析深度
  • --output <file>
    - 输出JSON文件
输出: 包含每个场景G/C/D分析结果的JSON文件。

detect-genre.ts

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.
从文本模式中识别主要和次要基础体裁。
bash
deno run --allow-read scripts/detect-genre.ts book.txt [options]
选项:
  • --sample-size <n>
    - 采样场景数量(默认:10)
  • --output <file>
    - 输出JSON文件
输出: 包含体裁检测结果和关键节点映射的JSON文件。

track-characters.ts

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.
识别主角和主要人物,追踪他们的弧光。
bash
deno run --allow-read scripts/track-characters.ts segments.json book.txt [options]
选项:
  • --protagonist <name>
    - 指定主角姓名
  • --max-secondary <n>
    - 次要人物最大数量(默认:5)
  • --output <file>
    - 输出JSON文件
输出: 包含人物弧光和关键场景引用的JSON文件。

generate-outline.ts

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
将所有分析结果合成为结构化Markdown大纲。
bash
deno run --allow-read --allow-write scripts/generate-outline.ts [options]
选项:
  • --segments <file>
    - 分割结果JSON文件
  • --scenes <file>
    - 场景分析JSON文件
  • --genre <file>
    - 体裁检测JSON文件
  • --characters <file>
    - 人物追踪JSON文件
  • --depth summary|standard|detailed
    - 输出深度
  • --output <file>
    - 输出Markdown文件

reverse-outline.ts (Orchestrator)

reverse-outline.ts(编排器)

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.
运行完整流程,从book.txt生成outline.md。
bash
deno run --allow-read --allow-write scripts/reverse-outline.ts book.txt [options]
选项:
  • --output <dir>
    - 输出目录(默认:./reverse-outlines/{book-name}/)
  • --depth quick|standard|detailed
    - 分析深度
  • --protagonist <name>
    - 指定主角
  • --genre <type>
    - 覆盖自动体裁检测结果
输出: 包含outline.md和analysis/文件夹的目录,其中analysis/文件夹包含所有中间JSON文件。

Anti-Patterns

反模式

Surface-Level Breakdown

表面层级拆解

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?
问题: 大纲仅罗列事件,未说明背后原因。 修复: 针对每个场景,思考:该场景承担什么结构功能?如果删除它会破坏什么?

Genre-Blind Analysis

无视体裁的分析

Problem: Applying thriller patterns to romance or vice versa. Fix: Always detect genre first; use genre-appropriate Key Moments.
问题: 将惊悚片的分析模式套用于爱情小说,反之亦然。 修复: 始终先检测体裁;使用符合体裁的关键节点框架。

Protagonist Assumption

预设主角

Problem: Assuming first POV character is protagonist. Fix: Track goal-attachment and arc presence across all POV characters.
问题: 假设第一个出现的视角人物就是主角。 修复: 追踪所有视角人物的目标关联度和弧光表现。

Scene Boundary Guessing

猜测场景边界

Problem: Treating paragraph breaks as scene breaks. Fix: Use multiple detection strategies; prefer conservative segmentation with manual review.
问题: 将段落换行视为场景转换。 修复: 使用多种检测策略;优先采用保守分割并辅以人工审核。

What You Do NOT Do

以下是你不需要执行的操作:

  • 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
  • 生成原创故事内容
  • 评判书籍质量
  • 主动与其他书籍对比(除非被要求)
  • 跳过状态(每个状态都基于前序状态构建)
  • 修改源文本

Output Persistence

输出持久化

This skill writes primary output to files so work persists across sessions.
本技能会将主要输出写入文件,确保跨会话工作成果不丢失。

Output Discovery

输出查找方式

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}/
在开始任何工作之前:
  1. 检查项目中的
    context/output-config.md
    文件
  2. 如果存在,查找本技能的条目
  3. 如果不存在,将输出创建在
    ./reverse-outlines/{book-name}/
    目录下

Primary Output

主要输出

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
对于本技能,需持久化保存:
  • outline.md - 最终Markdown格式大纲
  • analysis/segments.json - 章节/场景分割结果
  • analysis/scenes.json - 逐场景G/C/D分析结果
  • analysis/genre.json - 体裁检测结果
  • analysis/characters.json - 人物弧光追踪结果

Conversation vs. File

对话内容与文件存储的划分

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
存储到文件的内容保留在对话中的内容
分割数据澄清性问题
场景分析结果方法论讨论
体裁检测结果模糊场景的选项说明
人物弧光数据实时反馈
最终大纲作者的探索性讨论

Integration Graph

集成关系图

Inbound (From Other Skills)

入站集成(来自其他技能)

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
源技能源状态跳转至状态用途
story-senseSS7: 准备评估RO0分析已出版作品,与自身作品对比
dna-extractionEX7: 提取完成RO5将提取的功能与检测到的结构进行对比

Outbound (To Other Skills)

出站集成(至其他技能)

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
当前状态跳转至技能目标状态用途
RO6: 大纲完成story-zoomZ2将已出版书籍与自身结构进行映射对比
RO6: 大纲完成scene-sequencingSQ1用作场景结构参考
RO6: 大纲完成character-arcCA1用作弧光设计参考
RO6: 大纲完成genre-conventionsGC1研究体裁执行方式

Complementary Skills

互补技能

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
技能关系
scene-sequencing复用核心G/C/D分析模式
genre-conventions体裁检测模式来源
character-arc弧光类型识别模式来源
dna-extraction功能分类体系来源
story-zoom输出格式兼容,可用于对比
revision采用相似的结构分析方法