Writer Perspective Distiller#

"The voice you hear on first read is usually wrong."

Purpose#

Distill a writer's core writing posture into a callable perspective skill. The output is not an imitator — it's a switch that places you (or the AI) into the state the writer occupies when she writes. That state has three components: core beliefs, syntactic discipline, and a blacklist.

Inputs (in order of importance)#

InputRequiredNotes1–3 fiction worksRequiredAt least one complete. Reveals how she handles "people".Non-fiction / essays / interviews / correspondenceCriticalFiction shows the narrator's voice; non-fiction shows the author's analytic voice. They often diverge. Skipping non-fiction almost always produces a wrong distillation.Biographical contextImportantGeneration, geography, education, mobility, who she inherits from, who she draws boundaries against.User-flagged key passagesCriticalPrevents AI surface-read. Usually a passage the user feels "she is most herself" in.Use-case scenariosMediumBusiness writing? Academic papers? Fiction narration? Essays? Determines the compression direction.

The first two are non-negotiable. With only fiction, you distill a narrator's voice and risk conflating the author with her characters.

Analysis Flow (six sequential readings)#

1. Syntactic layer — vocabulary preferences, sentence-length distribution, rhythm, punctuation habits, register mix (formal / colloquial / academic).
2. Object layer — who does she write about? Whom does she NOT write about? Who is her implicit listener?
3. Attitude layer — her stance toward tragedy, comedy, history, intimacy, failure, time. Inferred from what she does, not from what she says.
4. Historical positioning — whom does she inherit from? Which tradition does she continue? From which surface-similar author does she draw a clear line?
5. Blacklist — what devices would she NEVER use? What emotions would she NEVER write?
6. Core-belief compression — one sentence: the thing in her head while writing that she will not deviate from.

Distillation Discipline (iron rules)#

1. Core beliefs must be confirmed by the user#

The AI is forbidden from drawing conclusions unilaterally. Flow:

• After the third reading, AI proposes 2–3 candidate beliefs, each with a textual basis and a counter-example.
• User selects / rejects / corrects.
• No proceeding to step 6 without user confirmation.

2. A blacklist is mandatory#

Listing only positives produces shallow output. The sharpest understanding lives in counter-examples. A blacklist needs at least 6 entries.

3. Distinguish from neighboring authors#

If author A shares a surface style with author B (e.g., "1990s intellectual irony"), explicitly mark where A diverges from B — usually at the belief layer, not the syntactic layer.

4. No copying sentence patterns as "homage"#

Borrowing structure is fine (e.g., "open with a generational frame"). Borrowing specific sentences is a plagiarism risk. The distilled perspective must state: Do not reuse her specific sentences; reuse only her method of constructing sentences.

5. Test-driven completion#

Before shipping, run a quality gate:

• Take a passage of default-AI prose (a paper, a business document).
• Have the distilled voice rewrite it.
• User reads and judges: "Is this her?" If not, return to step 3 and redo.

6. At least two user checkpoints#

• First — after surface reading, present belief candidates.
• Second — after blacklist completion, have the user add what they consider the sharpest counter-example.

A SKILL.md with fewer than two checkpoints does not ship.

Output: SKILL.md Template#

text

---
name: <author-slug>-perspective
description: |
  1–3 lines: who the author is; compressed core belief; use case.
  If for personal reference, add "LOCAL ONLY — Do not publish" with privacy notes.
---

# <Author Name> · <Verb-form Naming>

> One-sentence core belief (quote block)

## Core Belief
100–200 words. Three things: what she believes; what she does NOT believe; how the two coexist.

## Style Internals
4–6 belief-level phrases (the underlying tone, not operational rules).

## Syntactic Discipline
6–8 specific, operational do's.

## Argumentative / Rhetorical Habits
4–6 moves specific to this author.

## Black Humor / Rhetorical Rules (if applicable)
2–4 entries.

## Counter-Examples / Avoidance List
6–10 don'ts. **The most important section.**

## Application Flow
3–6 steps for rewriting a passage.

## One-Sentence Compression
The overall judgment that emerges from the whole document.

Common Pitfalls#

• Surface read mistaken for deep read — first-impression voice is usually wrong. Human correction is required.
• Cosplaying a similar author — identical surface vocabulary can mask completely opposite beliefs. Distinguish via blacklist.
• Borrowing sentences as homage = plagiarism risk — borrow structure, not sentences. State this explicitly.
• Treating political labels as writing tone — political position ≠ writing posture; the two often diverge.
• Distilling without non-fiction — looking only at fiction conflates the narrator with the author.
• Publishing without user correction — beliefs written unilaterally by AI usually capture an unimportant facet.
• Positive lists without counter-examples — "what she does" is insufficient; "what she refuses" must follow.

Application Flow (when invoked)#

1. User provides inputs (per §Inputs).
2. AI runs the first 3 readings, lists belief candidates → first checkpoint.
3. User selects / corrects candidates.
4. AI runs the final 3 readings, builds the blacklist → second checkpoint.
5. User sharpens.
6. AI drafts the SKILL.md → runs the test-driven quality gate.
7. If test passes → output <author-slug>-perspective/SKILL.md.
8. If test fails → return to step 2.

One-Sentence Compression#

Distill an author's writing posture into a perspective skill that must pass through two human checkpoints — so the voice rests on her beliefs, not on the AI's guess about her surface sound.

---

中文版 · Chinese Version#

「Surface read 第一次读出的声音通常是错的。」

用途#

把一位作家的笔法蒸馏成一份可在写作时调用的 perspective skill。蒸馏的产物不是模仿器，是 进入她写作时的状态的开关——核心信念 + 句法纪律 + 黑名单。

输入物（按重要性排序）#

输入必需说明1–3 部虚构作品必需至少一部完整。能看到她处理"人"的方式。非虚构 / 散文 / 访谈 / 通信极重要虚构里看到的是"叙事时的声音"，非虚构里看到的是"分析时的声音"，两者经常偏移。少了非虚构基本必错。生平定位重要世代、地缘、教育、流动史、她传承自谁、跟谁划清界限。用户人为指认的关键段落极重要防止 AI 表面读。这通常是用户读过觉得"她最像她自己"的一段。用途场景中等改写商务稿？学术论文？小说叙事？散文？决定 SKILL.md 的压缩方向。

至少要有前两项。只有虚构没有非虚构的情况下，蒸馏出的 voice 只是"叙事者声音"，会把作家与她笔下的人物混淆。

分析流程（六遍读，必须按顺序）#

1. 句法层：词汇偏好、句长分布、节奏、标点习惯、半文白 vs 口语 vs 学术的混合比。
2. 对象层：她写谁？她 不 写谁？谁是她笔下隐含的"听众"？
3. 态度层：对悲剧 / 喜剧 / 历史 / 亲密 / 失败 / 时代的态度——通过她 做什么动作 显示，不是通过她说什么。
4. 历史定位：传承自谁？她明显在接续哪一条传统？她跟哪个表面相似的作家划清界限？
5. 黑名单：她 绝不会 用的手法、绝不会写的情绪。这一步比正面列表重要。
6. 核心信念压缩：一句话——她写作时心里那个不允许她偏离的东西是什么。

蒸馏纪律（铁律，违反会翻车）#

1. 核心信念必须由用户确认#

AI 不允许单方面下断语。流程：

• AI 在第 3 遍读后，列 2–3 个候选信念，每个配一个文本依据 + 一个反例
• 用户选 / 驳 / 修正
• 没有用户校验前不得进入第 6 步

2. 必须有黑名单#

只列正面会写废。 最锋利的理解都在反例里。黑名单应该至少有 6 条。

3. 必须区分邻近作家#

如果作家 A 与作家 B 共享某种表面风格（例如"九十年代知识分子的反讽"），必须明确指出 A 跟 B 在哪一刀上分开——通常分在信念层而非句法层。

4. 禁止抄句式作"致敬"#

借结构可以（例如"用世代框架开头"），借具体句子是查重问题。蒸馏出的 perspective 应明确写："不要复用她的具体句子；只复用她写句子的方法。"

5. 测试驱动#

蒸馏完成前，跑一次质量门：

• 找一段 AI 默认体的文字（论文、商务稿都行）
• 让蒸馏出的 voice 重写
• 用户读完判断："这是她吗？" 不像就回到第 3 遍读重做。

6. 至少两次用户校验#

• 第 1 次：表面阅读后，提信念候选
• 第 2 次：黑名单完成后，让用户加上他认为最锋利的一条反例

少于两次校验的 SKILL.md 不发布。

输出物：SKILL.md 模板#

text

---
name: <author-slug>-perspective
description: |
  1–3 行：作家是谁；核心信念压缩；适用场景。
  如属私人参考，加 "LOCAL ONLY — Do not publish" 并说明隐私边界。
---

# <作家姓名> · <一个动词性命名>

> 一句话核心信念（用引号或 quote 块）

## 核心信念
100–200 字。说清三件事：她相信什么；她不相信什么；这两件如何同时成立。

## 风格内核
4–6 条信念性短语（不操作，是底色）。

## 句法纪律
6–8 条具体可操作的 do's。例：用词偏好、断句习惯、标点纪律、节奏规则。

## 论证习惯 / 修辞习惯
4–6 条该作家特有的论证或修辞 moves。

## 黑色幽默 / 修辞规则（如果有）
2–4 条。

## 反例 / 避免清单
6–10 条 don't's。**最重要的部分**。

## 应用流程
改写一段文字时的 3–6 步操作。

## 一句话压缩
最后一句，从全篇渗出来的总判断。

常见陷阱#

• Surface read 误当 deep read：第一次读出的声音通常错。配人为校正才能纠偏。
• 把作家 cosplay 成相似作家：表面词汇相同，骨子里的信念可能完全相反。必须用黑名单分开。
• 抄句式作致敬 = 查重风险：借结构不借句子。蒸馏产物里要明写。
• 把政治标签当写作底色：作家的政治位置 ≠ 她写作时的姿态。两者经常错位。
• 没读非虚构就下定论：只看虚构会把叙事者当作家，必错。
• 没让用户校正就发布：AI 单方面写出的"信念"基本都是表面读。
• 正面列表没配反例：只说"她会做什么"立不住，必须说"她绝不会做什么"。

应用流程（用户调用此 skill 后）#

1. 用户提供输入物（按 §输入物 表）
2. AI 跑前 3 遍读，列信念候选 → 第 1 次校验
3. 用户选 / 修正候选
4. AI 跑后 3 遍读，列黑名单 → 第 2 次校验
5. 用户加锋
6. AI 写出 SKILL.md → 跑测试驱动质量门
7. 测试通过 → 输出 <author-slug>-perspective/SKILL.md
8. 测试不通过 → 回到第 2 步重做

一句话压缩#

把一位作家的写作底色，蒸馏成一份 会被人为校验两次 的 perspective skill——以确保 voice 立在她的信念上，而不是 AI 对她表面声音的猜测上。