Agent skill

Story Short Analyze

by uu201 in uu201/character-arc

短篇网文拆文。拆解爆款短篇小说(番茄短篇 / 故事会 / 知乎盐选 / 追妻 / 世情 / 重生 / 虐渣等通俗题材)的故事核、结构、情感线、反转设计、写作手法、共鸣层次。

MITAuto-check passedWriting & Content

Install Story Short Analyze

skills CLI
$ npx skills add uu201/character-arc --skill story-short-analyze -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install uu201/character-arc story-short-analyze --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/uu201/character-arc.git skills-src && mkdir -p .claude/skills && cp -r skills-src/resources/skills/oh-story-claudecode/story-short-analyze .claude/skills/story-short-analyze && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
story-short-analyze
GitHub stars
583
Token cost
~2.2k tokens
SKILL.md length
473 words
Files
21 (incl. references)
Skills in repo
26
Repo updated
First seen
Licence
MIT

At a glance

短篇网文拆文。拆解爆款短篇小说(番茄短篇 / 故事会 / 知乎盐选 / 追妻 / 世情 / 重生 / 虐渣等通俗题材)的故事核、结构、情感线、反转设计、写作手法、共鸣层次。

  • Works in 3 steps: :确认拆解对象 + 字数路由 + 续跑检查 → 6:拆文流程 → :门控验收(Stage 6 之后、写 stages_completed[6] 之前)
  • Writing & Content work in your project
  • SKILL.md covers Phase 1:确认拆解对象 + 字数路由 + 续跑检查, 输出目录, Stage 2-6:拆文流程 and Phase 7:门控验收(Stage 6 之后、写…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Story Short Analyze is an agent skill from uu201/character-arc. 短篇网文拆文。拆解爆款短篇小说(番茄短篇 / 故事会 / 知乎盐选 / 追妻 / 世情 / 重生 / 虐渣等通俗题材)的故事核、结构、情感线、反转设计、写作手法、共鸣层次。 单一全量拆解管道:跑完 Stage 2-6 产出完整拆文报告,落盘到 拆文库/{书名}/, 下游 story-short-write 同时读拆文报告 + 情节节点 + 写作手法 + 原文 + meta.json 写下一篇。 触发方式:/story-short-analyze、/短篇拆文、「拆短篇」「拆这篇短文」「短篇拆文」 「精细拆解短篇」「8000 字短篇拆解」「番茄短篇拆文」「故事会拆解」「盐言故事拆解」 「分析这篇短篇」——均进入同一管道。

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including reference files (for example `references/anti-ai-writing.md`, `references/banned-words.md` and `references/character-basics.md`).

It sits in Writing & Content. The repository describes itself as: CharacterArc(弧光) AI 小说创作应用,集项目设定、角色关系、剧情大纲、章节写作与多模型 AI 协作于一体. The licence is MIT.

When your agent uses it

  • Writing & Content work in your project

Example prompts

  • “/story-short-analyze”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. :确认拆解对象 + 字数路由 + 续跑检查
  2. 6:拆文流程
  3. :门控验收(Stage 6 之后、写 stages_completed[6] 之前)

What it can do on your machine

Read from SKILL.md and the folder at commit 292f947. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Story Short Analyze loads about 2.2k tokens when it runs, and up to ~75k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 473 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~75k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from uu201/character-arc at commit 292f947, republished under its MIT licence (© uu201). 473 words, ~2,229 tokens.

Download SKILL.mdSave it as .claude/skills/story-short-analyze/SKILL.md (or your agent's skills folder). This skill also uses 20 other files; get the full folder from GitHub.
name
story-short-analyze
description
短篇网文拆文。拆解爆款短篇小说(番茄短篇 / 故事会 / 知乎盐选 / 追妻 / 世情 / 重生 / 虐渣等通俗题材)的故事核、结构、情感线、反转设计、写作手法、共鸣层次。 单一全量拆解管道:跑完 Stage 2-6 产出完整拆文报告,落盘到 `拆文库/{书名}/`, 下游 `story-short-write` 同时读拆文报告 + 情节节点 + 写作手法 + 原文 + _meta.json 写下一篇。 触发方式:/story-short-analyze、/短篇拆文、「拆短篇」「拆这篇短文」「短篇拆文」 「精细拆解短篇」「8000 字短篇拆解」「番茄短篇拆文」「故事会拆解」「盐言故事拆解」 「分析这篇短篇」——均进入同一管道。
version
3.0.0

story-short-analyze:短篇网文拆文

你是短篇小说结构分析师。

核心:短篇靠共鸣和爆点驱动。拆文就是看它用什么故事核、怎么铺垫、在哪里引爆,把 分析叙事写进 拆文报告.md,结构计数写进 _meta.json,下一篇短篇直接读这些写。


Phase 1:确认拆解对象 + 字数路由 + 续跑检查

1.1 拿到原文

问用户:「你要拆哪篇?(标题+平台/来源)」

无文本时:用户没有提供原文文件路径、也没有在对话中贴出原文,引导用户提供 ——「请提供这篇短篇的原文文件路径,或直接把原文贴给我。」

1.2 字数探针(长短篇路由)

拿到原文后立刻数字数:

word_count = 全文字数
  ├─ < 15,000          → 直接进入 short 管道
  ├─ 15,000 - 20,000   → 灰区:询问用户「字数 {N},介于短/长之间,按短篇还是长篇拆?」
  └─ > 20,000          → 提示「此文字数 {N} 偏长,建议改用 /story-long-analyze。
                           仍要按短篇拆请明确回复『按短篇继续』」

为什么必须探针:短篇与长篇的节点密度、情感曲线节奏、共鸣层数差异显著;用短篇 管道拆 100k+ 长篇会把节点采样过疏,模型把单卷误判成全书。

1.3 题材识别
用户提到具体题材(追妻 / 重生 / 虐文 / ...)?
  ├─ 是 → 加载 genre-catalog.md 对应题材的「短篇视角」章节作为拆文标尺
  └─ 否 → 关键词扫描确定题材;扫不到则 genre_detected = "通用",用通用模板(Stage 2-6)

题材识别关键词参考:

  • 追妻火葬场 / 渣男后悔 → 追妻
  • 重生复仇 / 前世今生 → 重生复仇
  • 死后视角 / 灵魂旁观 → 死人文学
  • 小三 / 出轨 / 知三当三 → 小三
  • 世情 / 现实 / 婆媳 → 世情
  • 仙侠 / 修仙 / 门派 → 仙侠

题材作为「对照标尺」加载——见 references/genre-catalog.md 等文件首段「## 用作 拆文标尺时」说明。

1.4 续跑检查(lightweight resume)

进入管道前检查 拆文库/{书名}/_meta.json:

存在 _meta.json?
  ├─ 否 → 直接进入新一轮拆解
  └─ 是 → 询问用户三选一:
       (a) 覆盖:归档旧产出到 拆文库/{书名}/_archive_{时间戳}/ 后从 Stage 2 重跑
       (b) 续跑:读 _meta.json.last_stage_in_progress(非空 → 从该 Stage 整段重跑)
                 或读 _meta.json.stages_completed[](从 max+1 续跑)
       (c) 取消

完整 resume 契约见 references/output-contract.md。


输出目录

输出到 拆文库/{书名}/(项目根目录下)。用户指定了其他路径时按用户指定路径输出。

标准输出文件树:

拆文库/{书名}/
├── 原文/                # 原文备份(管道前置步骤产出)
├── 拆文报告.md           # 人类可读综合报告(Stage 2-6 所有可读段)
├── 情节节点.md           # Stage 2 情节节点清单(独立成文,方便定位)
├── 写作手法.md           # Stage 4 写作手法分析(独立成文,方便复用)
└── _meta.json           # 管道元数据 + 结构计数(resume + Phase 7 数值依据)

下游契约:story-short-write 同时读全套产出——拆文报告.md 取分析叙事, 情节节点.md 看节奏锚点,写作手法.md 抄手法,原文/ 抄语感,_meta.json 看题材识别和结构计数。完整字段定义见 references/output-contract.md。

Stage → 文件映射
Stage落地文件
2拆文报告.md(故事核+结构+梗概段) + 情节节点.md
3拆文报告.md(情感曲线+爆点段)
4拆文报告.md(反转段) + 写作手法.md
5拆文报告.md(人物+首尾段)
6拆文报告.md(综合段) + _meta.json.structure_counts(数值计入元数据)
原文备份(管道前置步骤)

拆解开始前,必须先备份原文:

  1. 检查 拆文库/{书名}/原文/ 目录是否已存在
  2. 如果不存在,从用户提供的源路径复制原文文件到 拆文库/{书名}/原文/
  3. 如果用户未提供源文件路径(直接在对话中贴文本),将原始文本保存到 拆文库/{书名}/原文/原文.md
  4. 备份完成后验证 原文/ 目录下文件非空(>0 bytes)
  5. 此步骤确保即使拆文过程中出现异常,原始材料不会丢失

备份完成后初始化 _meta.json:写入 version、word_count、genre_detected、 created_at、stages_completed: []、last_stage_in_progress: null。


Stage 2-6:拆文流程

5 阶段管道

预期耗时提示:短篇拆文通常 10-30 分钟;同类对比或平台适配会更久。若文本很短, 先降采样提取关键节点,不要为满足节点数量硬拆。

阶段名称输入输出完成标志
2结构+情节节点全文故事核 + 故事梗概 + 功能分段(4-6段,必须含开端/发展/高潮/结局)+ 情节节点清单。节点密度按字数分档,见 material-decomposition.md「情节节点提取」的字数分档表。结构划分 ≥4 段 + 故事核已提取
3情感线+爆点故事核+结构划分+情节节点数据情感曲线(≥5节点)+ 爆点分析(6维度)+ 期待感分析。爆点分析 6 维度齐全
4反转+写作手法节点+情感数据前置反转检查 + 反转机制(铺垫≥2条)+ 写作手法(≥5项维度:POV/对话/时间/信息/其他)。写作手法 ≥5 项
5人物+开头结尾情节节点+全文所有人物(分类+功能标签+功能评估)+ 开头分析(前50/100字)+ 结尾分析(收束检查)。人物功能评估完成
6综合评估 + _meta.json 写计数全部数据五维评分 + 爆点性 + 话题性 + 共鸣分析(≥3层)+ 可复用结构(≥3条)+ 节奏速报 + 算出并写入 _meta.json.structure_counts。五维评分完成 + 爆点性/话题性已分析 + 共鸣≥3层 + 可复用≥3条 + 节奏速报已包含 + _meta.json.structure_counts 各字段达 Phase 7.2 阈值

管道执行顺序:2 → 3 → 4 → 5 → 6(严格串行,每阶段依赖前一阶段数据)。可选模块 (同类对比、平台适配、详细节奏)可在 Stage 6 后执行。

Stage 写盘协议(crash safety):每个 Stage 开始前先把 _meta.json.last_stage_in_progress 置为当前 Stage 编号;该 Stage 所有目标文件写完后再做 non-empty / 最小长度检查,通过 才清空 last_stage_in_progress 并 append 到 stages_completed[]。半成品文件不被 信任,resume 时该 Stage 整段重跑。完整协议见 references/output-contract.md 「写入顺序 (crash safety)」段。

非标文本分段:对话体、聊天记录、帖子体、书信体等非标准章节格式,先按时间/说话人 切换/信息揭示点分段,再映射到开端、发展、高潮、结局;不要机械按自然段数量切分。

详细模板见 output-templates.md,方法论见 material-decomposition.md,输出契约见 output-contract.md。


Phase 7:门控验收(Stage 6 之后、写 stages_completed[6] 之前)

Stage 6 内容写完后,不立刻 append 6 到 stages_completed[]。先跑三道门控:

7.1 拆文报告 AI 腔自检

扫描 拆文报告.md 全文 against references/banned-words.md 词表 + references/anti-ai-writing.md 句式规则。 扫描时跳过源文引用——以 > 开头的引用行、以及表格中「关键台词 / 原文引用」列的引号直引不计入,只扫分析师本人写的措辞。

  • 命中 → 不写 stages_completed[6],列出命中位置,提示用户人工修订拆文报告 本身的 AI 腔(不是源文——源文里有 AI 腔正常报告即可,但报告本身不能写成 AI 腔)。
  • 未命中 → 继续 7.2。

守门员定位:本节门控的是「我们写的拆文报告」,不是「源文是不是 AI 写的」。

Show full SKILL.md (199 more words)Show less
7.2 _meta.json.structure_counts 数值校验

按 references/output-contract.md 「Phase 7.2」表 逐项检查 _meta.json 里 Stage 6 写入的结构计数:

字段最低值
structure_counts.beats≥ 4
structure_counts.hooks≥ 3
structure_counts.setup_clues≥ 3
structure_counts.character_archetypes≥ 2
structure_counts.reusable_structures≥ 3
structure_counts.reversal_type在枚举内(视角/身份/动机/时间线/信息/认知)
genre_detected非空

任一项不达标 → 阻断;列出未达标字段,提示用户回到对应 Stage 补足。

7.3 output-templates.md [BLOCK] 项扫描

扫描 output-templates.md 中所有 [BLOCK] 标注项,确认对应产出段已完成。任一缺失 → 阻断。[WARN] 项不阻断,但写入 拆文报告.md 末尾的「待补」清单供用户决定。

7.4 通过

7.1 + 7.2 + 7.3 全通过 → 清空 _meta.json.last_stage_in_progress,append 6 到 stages_completed[],提示用户「拆解完成,可调用 /story-short-write 写下一篇」。


质量门控概要

各阶段完成后需通过质量检查。逐项 checklist 见 output-templates.md 质量门控必填字段。

质量标准的阈值、数值与计算方式的唯一权威定义见 material-decomposition.md 质量标准。

强阻断 / 警告区分:见 output-templates.md 每条 checklist 末尾的 [BLOCK] / [WARN] 标注。[BLOCK] 不通过 → Phase 7.3 阻断。


流程衔接

流水线: 短篇 位置: 拆文(第 2/3 步)

时机跳转到命令
准备开写story-short-write(同时读 拆文报告.md + 情节节点.md + 写作手法.md + 原文/ + _meta.json)/story-short-write
需要市场数据story-short-scan/story-short-scan
字数 > 20k 更适合长篇story-long-scan → story-long-analyze/story-long-scan

参考资料

核心方法论(拆文时必须加载)
文件何时加载
references/output-contract.md全程:Stage→文件映射 / _meta.json schema(含 structure_counts)/ 下游消费规范 / Phase 7 门控接入点
references/output-templates.md拆文时:输出模板 + 结构库 + 质量门控(含 [BLOCK]/[WARN] 标注)
references/material-decomposition.md拆文方法论:情节节点提取 + 写作手法 + 情感线 + 节奏分析 + 共鸣分析 + 人物规则 + 质量标准唯一权威
references/quality-checklist.md评估源文质量时:短篇拆书的质量自检清单(评估对象的好坏,不是评估拆文报告本身)
references/anti-ai-writing.mdPhase 7.1:扫描拆文报告本身的 AI 腔(不是源文滤镜)
references/banned-words.mdPhase 7.1:拆文报告禁用词速查
按需加载(拆解对应题材 / 维度时作为对照标尺)
文件何时加载
references/deconstruction-examples.md校准拆文方法时:3 个完整案例作为参照
references/zhihu-style.md拆解知乎盐言故事时作为平台特性对照
references/genre-catalog.md拆解特定题材时:加载对应题材的「短篇视角」章节作为标准模式
references/hooks-chapter.md拆解章节钩子设计时作为钩子类型对照
references/hooks-suspense.md拆解悬念设计时作为悬念分类对照
references/hooks-paragraph.md拆解段落钩子时作为 11 种段落级钩子对照
references/character-basics.md拆解人物基础设定时作为人设要素对照
references/character-design-methods.md拆解人物内在矛盾时作为三层标签反差对照(contradiction_axis 来源)
references/character-relations.md拆解人物关系网时作为关系类型对照
references/genre-core-mechanics.md拆解题材核心梗与循环机制时作为机制对照
references/genre-readers.md拆解读者心理与期待管理时作为读者画像对照
补充资料(拆 Stage 6「可复用结构」时按需对照)

题材写作公式:references/genre-writing-formulas.md(21 大题材公式作为 「这篇是否合标」的对照标尺) 通用写作技法:references/genre-writing-techniques.md(情绪操控 / 感情线 / 震惊场景 / 喜剧机制——拆 reusable_structures.fail_mode 时引用 L329 「禁忌」列) 市场数据:references/real-market-data.md(跨平台写作差异对照表)

所有 references 在 story-short-analyze 中都是对照标尺——用源文与文件描述的 标准模式做对比,找出该篇用了哪种、做得多到位,不是按文件指引写新作品。


语言

  • 跟随用户的语言回复,用户用什么语言就用什么语言回复
  • 中文回复遵循《中文文案排版指北》

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SKILL.md and 20 other files (references) in resources/skills/oh-story-claudecode/story-short-analyze of uu201/character-arc.

  • SKILL.md
  • references/anti-ai-writing.md
  • references/banned-words.md
  • references/character-basics.md
  • references/character-design-methods.md
  • references/character-relations.md
  • references/deconstruction-examples.md
  • references/genre-catalog.md
  • references/genre-core-mechanics.md
  • references/genre-readers.md
  • references/genre-writing-formulas.md
  • references/genre-writing-techniques.md
  • references/hooks-chapter.md
  • references/hooks-paragraph.md
  • references/hooks-suspense.md
  • references/material-decomposition.md
  • references/output-contract.md
  • references/output-templates.md
  • references/quality-checklist.md
  • references/real-market-data.md
  • … and 1 more

Open the folder on GitHubat commit 292f947

Compare with similar skills

Story Short Analyze next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Story Short Analyze this skilluu201/character-arc583—~2.2kAutomated safety check: PassMIT
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HumanizerAzure-Samples/interview-coach-agent-framework17338 repos~5.8kAutomated safety check: PassMIT
Avoid AI Writingconorbronsdon/avoid-ai-writing4.9k3 repos~8.1kAutomated safety check: PassMIT
JavaScript Concept Fact Checkerleonardomso/33-js-concepts67k1 repos~5kAutomated safety check: PassMIT
User-Facing Text Cleanupguillaumemeyer/watermarks-remover24k—~3.5kAutomated safety check: PassMIT

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Questions about Story Short Analyze

What does Story Short Analyze do?

短篇网文拆文。拆解爆款短篇小说(番茄短篇 / 故事会 / 知乎盐选 / 追妻 / 世情 / 重生 / 虐渣等通俗题材)的故事核、结构、情感线、反转设计、写作手法、共鸣层次。. Story Short Analyze is an agent skill from uu201/character-arc.

When should I use Story Short Analyze?

Story Short Analyze fits situations like: writing & Content work in your project.

How do I install Story Short Analyze in Claude Code?

Run `npx skills add uu201/character-arc --skill story-short-analyze -a claude-code`. Or copy the skill folder (resources/skills/oh-story-claudecode/story-short-analyze in uu201/character-arc) into .claude/skills/story-short-analyze in your project. Claude Code loads it when a task matches its description.

How do I install Story Short Analyze in Codex?

Run `npx skills add uu201/character-arc --skill story-short-analyze -a codex`. Or copy the skill folder (resources/skills/oh-story-claudecode/story-short-analyze in uu201/character-arc) into .agents/skills/story-short-analyze in your project. Codex loads it when a task matches its description.

Can I use Story Short Analyze in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add uu201/character-arc --skill story-short-analyze -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/story-short-analyze, .gemini/skills/story-short-analyze, .github/skills/story-short-analyze and .opencode/skills/story-short-analyze in your project.

What does Story Short Analyze need to run?

SKILL.md names no scripts, command-line tools or credentials: Story Short Analyze is instructions for the agent only.

Does Story Short Analyze access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Story Short Analyze safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Story Short Analyze use?

Story Short Analyze is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Story Short Analyze use?

About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 73k tokens, read only when the agent opens those files.

What are the alternatives to Story Short Analyze?

Skills that share tags, products or a category with Story Short Analyze: Social (coreyhaines31/marketingskills, 54k stars), Humanizer (Azure-Samples/interview-coach-agent-framework, 173 stars), Avoid AI Writing (conorbronsdon/avoid-ai-writing, 4.9k stars) and JavaScript Concept Fact Checker (leonardomso/33-js-concepts, 67k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Story Short Analyze?

uu201 (a GitHub user) maintains it in uu201/character-arc, which has 583 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 9, 2026.

Source: uu201/character-arc on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.