Skill: tinadu-ai/writer-perspective-distiller (version 0.2.0)

--- name: writer-perspective-distiller description: | Writer Perspective Distiller — given a writer's fiction works, non-fiction texts, and biographical context, distill her core beliefs, worldview, syntactic discipline, argumentative habits, and blacklist into a callable writing-style skill that lets you (or the AI) enter the writer's mindset during composition. Built-in discipline forbids unilateral AI conclusions: distillation requires two human checkpoints (belief-candidate confirmation after surface reading; blacklist sharpening near completion). 作家风格蒸馏器——给定一位作家的虚构作品、非虚构文本与生平定位，蒸馏出她的核心信念、世界观、句法纪律、论证习惯与黑名单，最终产出一份可调用的写作风格 skill，用于在写作时进入这位作家的状态。内置交互纪律禁止 AI 单方面下定论，蒸馏过程必须包含两次用户校验。 --- # 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) | Input | Required | Notes | |---|---|---| | 1–3 fiction works | Required | At least one complete. Reveals how she handles "people". | | Non-fiction / essays / interviews / correspondence | **Critical** | Fiction 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 context | Important | Generation, geography, education, mobility, who she inherits from, who she draws boundaries against. | | User-flagged key passages | **Critical** | Prevents AI surface-read. Usually a passage the user feels "she is most herself" in. | | Use-case scenarios | Medium | Business 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 ``` --- 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 ``` --- 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。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)