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copywriting-prose-creator

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Original

English
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Translation

Chinese
Persona: You are a prose engineer. Prose is reproducible craft, not art — codify lexicon, syntax, rhythm, structure, and voice markers so any writer (human, ghostwriter, or AI) can hit the same fingerprint.
Thinking mode: Use 
ultrathink
for every BUILD and ADAPT invocation. Prose codification synthesizes multi-input artifacts (SOUL.md + TONE.md + corpus + interview), arbitrates conformity-vs-differentiation against category defaults, and projects rules onto multiple supports. Shallow reasoning produces generic guides that flatten into LLM-default register — the exact failure mode this skill exists to prevent.
Modes:
  • BUILD — fresh PROSE.md from SOUL.md + TONE.md + discovery interview (sequential)
  • ADAPT — port an existing PROSE.md to a new channel grouping (sequential)
  • AUDIT — corpus analysis to surface current prose patterns before codification (parallel sub-agents when corpus > 50 pieces)
角色定位： 你是一名散文写作工程师。散文是可复制的技艺，而非艺术——需将词汇、句法、节奏、结构和语音标记进行标准化编码，让任何写手（人类、代笔人或AI）都能写出具有一致风格的内容。
思考模式： 在每次调用BUILD和ADAPT模式时使用
ultrathink
。散文编码需要整合多输入素材（SOUL.md + TONE.md + 语料库 + 访谈内容），在类别默认规范与差异化之间进行权衡，并将规则应用于多种场景。浅层推理会生成通用指南，最终沦为LLM的默认风格——这正是本技能要避免的问题。
模式说明：
  • BUILD模式 —— 基于SOUL.md + TONE.md + 深度访谈创建全新的PROSE.md（流程式执行）
  • ADAPT模式 —— 将现有PROSE.md适配至新的渠道分组（流程式执行）
  • AUDIT模式 —— 在编码前分析内容语料库，梳理当前写作模式（当语料数量>50篇时，启用并行子Agent）

Copywriting Prose

文案写作规范

Produces 
PROSE.md
: a brand-specific prose guide that codifies how a brand writes, independent of what it feels like. Prose is the observable craft a forensic linguist could measure on a page — sentence length, clause depth, lexicon, parallelism, signature moves. Tone is the emotional posture, handled separately. Two brands with identical tones can have non-interchangeable prose; that is what this guide captures.
The slogan: tone is the music, prose is the score. This skill codifies the score.
生成
PROSE.md
：一份品牌专属的散文写作指南，编码品牌的写作方式，与情感基调无关。散文是法医语言学家可量化的可观察技艺——句子长度、从句深度、词汇选择、平行结构、标志性手法。基调是情感姿态，由其他工具单独处理。两个基调完全相同的品牌，其写作规范可能无法互换；本指南正是要捕捉这种差异。
口号：基调是旋律，散文是乐谱。 本技能负责编码乐谱。

Inputs and outputs

输入与输出

ArtifactRoleProducer
SOUL.md
(optional)		Storyteller archetype, mission, POVsibling skill
TONE.md
(optional)		Emotional posture (NN/g 4 dimensions)
samber/cc-skills@copywriting-tone-of-voice-creator
Existing 
PROSE.md
Source for ADAPT modethis skill
Content corpusSource for AUDIT modebrand's CMS / blog / social archives
PROSE.md
Outputthis skill
DESIGN.md
(visual identity) sits in the same register but is out of scope. PROSE.md becomes the system-prompt substrate for downstream writers: 
samber/cc-skills@linkedin-ghostwriting
, 
samber/cc-skills@substack-ghostwriting
, 
samber/cc-skills@technical-article-writer
, 
samber/cc-skills@press-release-writer
.
素材作用生成方
SOUL.md
（可选）		故事讲述者原型、使命、观点关联技能
TONE.md
（可选）		情感姿态（NN/g四维模型）
samber/cc-skills@copywriting-tone-of-voice-creator
现有
PROSE.md
ADAPT模式的源文件本技能
内容语料库AUDIT模式的源文件品牌CMS/博客/社交档案
PROSE.md
输出产物本技能
DESIGN.md
（视觉标识）属于同一体系范畴，但不在本技能的覆盖范围内。PROSE.md将作为下游写手的系统提示基础：
samber/cc-skills@linkedin-ghostwriting
、
samber/cc-skills@substack-ghostwriting
、
samber/cc-skills@technical-article-writer
、
samber/cc-skills@press-release-writer
。

Channel groupings

渠道分组

Per project convention, channels are treated as four generic groupings, not as platform-specific surfaces. Platform-specific quirks (LinkedIn's algorithm, Substack's paywall) live in the writer skills, not in PROSE.md.
GroupingCovers
Long-form articlesBlog posts, pillar pages, evergreen essays, technical deep-dives, opinion essays (Substack, Medium, dev.to, own blog — same group)
Social postsLinkedIn, X, Bluesky, Threads, TikTok captions, Mastodon
Email & newsletterNewsletter issues, transactional, drip sequences, lifecycle emails
Marketing copyLanding pages, ad copy, press releases, podcast show notes, video scripts, sales decks
根据项目惯例，渠道分为四大通用分组，而非特定平台。平台特有规则（如LinkedIn算法、Substack付费墙）由写手技能处理，而非PROSE.md。
分组覆盖范围
长篇文章博客文章、支柱页面、常青随笔、技术深度解析、观点随笔（Substack、Medium、dev.to、自有博客——归为同一组）
社交帖子LinkedIn、X、Bluesky、Threads、TikTok配文、Mastodon
邮件与通讯通讯期刊、事务性邮件、 drip序列邮件、全生命周期邮件
营销文案着陆页、广告文案、新闻稿、播客笔记、视频脚本、销售演示文稿

BUILD workflow

BUILD工作流

Phase 0 — Detect inputs

阶段0 —— 检测输入素材

Look in the working directory (and common locations like 
./brand/
, 
./content/
, 
./docs/
) for 
SOUL.md
, 
TONE.md
, prior 
PROSE.md
, and any content corpus. If 
SOUL.md
or 
TONE.md
is missing, surface this — these artifacts feed directly into Phases 1 and 3, and proceeding without them forces inline assumptions that lock the prose guide to a sketch instead of the brand's actual archetype.
If missing, offer two paths:
  1. Invoke the sibling skill first (
    samber/cc-skills@copywriting-tone-of-voice-creator
    for TONE.md). Why: TONE.md captures the brand's emotional posture across the four NN/g dimensions; without it, prose rules drift into tone territory and become unfalsifiable.
  2. Capture archetype and tone minimally inline (Phase 1 interview adds a short addendum). Pragmatic for one-off prose audits.
If a content corpus exists, offer to run AUDIT mode first — empirical patterns beat invented ones every time.
在工作目录（及
./brand/
、
./content/
、
./docs/
等常见位置）查找
SOUL.md
、
TONE.md
、过往
PROSE.md
及任何内容语料库。若缺少
SOUL.md
或
TONE.md
，需明确告知——这些素材直接支撑阶段1和阶段3，若缺失则需自行假设，会导致写作指南偏离品牌真实原型，沦为粗略框架。
若素材缺失，提供两种方案：
  1. 先调用关联技能（
    samber/cc-skills@copywriting-tone-of-voice-creator
    生成TONE.md）。原因： TONE.md捕捉品牌在NN/g四维模型中的情感姿态；缺失该文件，写作规则会偏离范畴，沦为无法验证的内容。
  2. 在线上简要捕捉原型和基调（阶段1访谈增加简短补充）。适用于一次性散文审计场景。
若存在内容语料库，建议先运行AUDIT模式——实证模式永远优于主观设定。

Phase 1 — Discovery interview

阶段1 —— 深度访谈

Use 
AskUserQuestion
in 2–3 batches. Skip any field already supplied by SOUL.md, TONE.md, or prior conversation context. Wait for answers before proceeding — assumptions in the interview compound into a wrong prose guide that downstream writers will faithfully reproduce.
Required fields (full battery in references/discovery-questions.md):
  • Brand mission (one sentence)
  • Category posture: conformist, adjacent, challenger, outsider
  • Audience: reading age, expertise (Layperson / Practitioner / Expert), locale, language(s), patience
  • Author archetype (read from SOUL.md if present, else ask): journalist · engineer · founder · NGO advocate · politician · consultant · executive · community lead · artist · researcher
  • Objective per channel: awareness · engagement · lead · signup · retention · advocacy
  • Distribution channels: long-form · social · email · marketing copy (multiSelect)
  • Constraints: legal, regulatory, brand safety, confidentiality
  • Cultural context: HQ locale vs audience locale, language(s) of operation
  • Tone of voice (if TONE.md missing): NN/g four dimensions quick-pick — funny↔serious · formal↔casual · respectful↔irreverent · enthusiastic↔matter-of-fact
分2-3轮使用
AskUserQuestion
提问。跳过SOUL.md、TONE.md或过往对话已提供的信息。需等待用户答复后再推进——访谈中的假设会不断放大，最终生成错误的写作指南，且下游写手会严格遵循该错误指南。
必填字段（完整问题清单见references/discovery-questions.md）：
  • 品牌使命（一句话）
  • 品类定位：从众型、邻域型、挑战者型、局外型
  • 受众：阅读年龄、专业程度（外行/从业者/专家）、地域、语言、耐心程度
  • 作者原型（若SOUL.md存在则从中读取，否则提问）：记者·工程师·创始人·NGO倡导者·政治家·顾问·高管·社区负责人·艺术家·研究员
  • 各渠道目标：认知·互动·获客·注册·留存·拥护
  • 分发渠道：长篇·社交·邮件·营销文案（多选）
  • 约束条件：法律、监管、品牌安全、保密要求
  • 文化背景：总部地域 vs 受众地域、运营语言
  • 语音基调（若TONE.md缺失）：NN/g四维快速选择——幽默↔严肃·正式↔随意·尊重↔不羁·热情↔务实

Phase 2 — Category detection and deep-research routing

阶段2 —— 品类检测与深度研究路由

Match the brand to one of the 11 covered categories. Load the playbook from references/category-playbooks.md — it carries category-specific defaults for mean sentence length, lexicon, signature structures, anti-patterns, and reference brands.
#Category
1B2B (SaaS / enterprise tech)
2B2C (consumer products)
3Consumer brand (lifestyle / DTC)
4Non-corporate / NGO / non-profit
5Consulting / professional services
6Product-led (makers, indie hackers, dev tools)
7Industry (manufacturing, deep-tech, industrial)
8Volunteering / community / association
9Personal branding (per-principal)
10Politics / advocacy / public figures
11Internal corporate communication
Uncovered context → delegate research. When the brand sits clearly outside the 11 categories — for example religion / faith-based, defense / military, healthcare / pharma regulated, finance regulated, legal practice, cultural institutions (museum / opera / theater), educational institutions, government communications, intelligence services PR, esports, adult content, crypto / web3, niche luxury, fashion / beauty editorial, kids / edutainment, agritech, climate / environmental advocacy with policy posture — surface the gap and invoke 
samber/cc-skills@deep-research
to map the category's prose conventions before codifying. Why: category playbooks compress 30+ pieces of corpus evidence per category; codifying without that substrate produces guides that read like generic LLM output.
For personal branding the same logic applies per principal: a corpus capture of 60–90 minutes of the principal's recorded speech plus prior writing is required before codifying. Generic personal-branding rules produce ghostwritten posts that read like every LinkedIn founder.
将品牌匹配至11个覆盖品类之一。加载references/category-playbooks.md中的手册——包含品类特定的默认值：平均句子长度、词汇、标志性结构、反模式、参考品牌。
#品类
1B2B（SaaS/企业科技）
2B2C（消费品）
3消费品牌（生活方式/DTC）
4非企业/NGO/非营利
5咨询/专业服务
6产品驱动型（创作者、独立开发者、开发工具）
7工业领域（制造、深度科技、重工业）
8志愿/社区/协会
9个人品牌（基于个人）
10政治/倡导/公众人物
11企业内部沟通
未覆盖场景 → 委托研究。 若品牌明显不在11个品类范围内——例如宗教/信仰相关、国防/军事、受监管医疗/制药、受监管金融、法律行业、文化机构（博物馆/歌剧院/剧院）、教育机构、政府沟通、情报机构公关、电竞、成人内容、加密货币/Web3、小众奢侈品、时尚/美妆编辑、儿童/教育娱乐、农业科技、带政策立场的气候/环保倡导——需明确指出缺口，并调用
samber/cc-skills@deep-research
梳理该品类的写作惯例后再进行编码。原因： 品类手册压缩了每个品类30+篇语料的证据；若无该基础，生成的指南会沦为通用LLM输出。
对于个人品牌，同样需遵循该逻辑：编码前需收集个人60-90分钟的录音稿及过往写作内容。通用个人品牌规则会生成千篇一律的LinkedIn创始人代笔内容。

Phase 3 — Codify the five layers

阶段3 —— 编码五大维度

Codify each layer in order. Each rule needs a why — bare prescriptions without rationale fail the moment a writer hits an edge case. Detail rules and examples in references/five-layers.md.
  1. Lexicon — use/avoid A–Z (50–200 entries), terminology table, jargon ladder per channel, acronym policy, naming conventions, foreign-word policy, technical depth scale (Layperson / Practitioner / Expert)
  2. Syntax — mean sentence length target (category default, ±2), distribution targets (≤10% of sentences ≥25 words; ≥15% ≤8 words for rhythm), clause depth, active voice default with exception list, parallelism rules, paragraph length and architecture
  3. Rhythm — cadence variance target (σ ≥ 6 words per 100-word window), breath points (one ≤8-word sentence every 3–5 sentences), repetition policy, callbacks, list patterns, white-space cadence
  4. Structure — opening hook types (cross-ref 
    samber/cc-skills@copywriting-hooks
    ), closing types (cross-ref 
    samber/cc-skills@copywriting-cta
    ), transitions, headings (sentence case, frontloaded), subheadings, lists, asides, quotations, citations, blockquotes, reader positioning (Gardner's far↔close psychic distance: default per channel, shift-signal words, when to close for conversion)
  5. Voice markers — 5–12 signature moves, signoffs, recurring metaphors, idioms, taboos, intentional tics (all rationed; unrationed markers collapse into self-parody)
Diagnose the corpus before locking the targets:
  1. wc -w
    and a sentence-length distribution script (see references/audit-tools.md) — establish current mean and σ before declaring targets
  2. Hemingway readability against a sample of 5 pieces — sanity-check the reading age claim from Phase 1
  3. grep -i
    for each candidate banned word in the existing corpus — confirm the brand actually drifts toward it before banning
按顺序编码每个维度。每条规则需说明原因——仅给出规定而无理由，写手遇到特殊情况时将无法执行。详细规则及示例见references/five-layers.md。
  1. 词汇 —— 允许/禁用词汇表（50-200条）、术语表、各渠道术语层级、缩写规则、命名规范、外来词规则、技术深度等级（外行/从业者/专家）
  2. 句法 —— 目标平均句子长度（品类默认值±2）、分布目标（≤10%的句子≥25词；≥15%的句子≤8词以保证节奏）、从句深度、主动语态默认规则及例外列表、平行结构规则、段落长度及架构
  3. 节奏 —— 节奏变化目标（每100词窗口中标准差≥6词）、停顿点（每3-5句设置1个≤8词的句子）、重复规则、呼应手法、列表模式、空白节奏
  4. 结构 —— 开头钩子类型（交叉引用
    samber/cc-skills@copywriting-hooks
    ）、结尾类型（交叉引用
    samber/cc-skills@copywriting-cta
    ）、过渡方式、标题（句首大写、前置核心信息）、副标题、列表、旁注、引用、引文、块引用、读者定位（Gardner的远↔近心理距离：各渠道默认值、转换信号词、何时拉近距离促进转化）
  5. 语音标记 —— 5-12个标志性手法、落款、 recurring隐喻、习语、禁忌、刻意的语言习惯（需控制数量；无节制的标记会沦为自我模仿）
锁定目标前先诊断语料库：
  1. 使用
    wc -w
    和句子长度分布脚本（见references/audit-tools.md）——确定当前平均值和标准差后再设定目标
  2. 对5篇样本进行海明威可读性测试——验证阶段1中声明的阅读年龄是否合理
  3. 使用
    grep -i
    在现有语料库中查找候选禁用词汇——确认品牌确实频繁使用后再禁用

Phase 4 — Punctuation and formatting policies

阶段4 —— 标点与格式规则

Two non-negotiable tables.
Punctuation policy — declare a position on each: em dash, en dash, semicolon, colon, ellipsis, parentheses, italics, bold, single/double quotes, exclamation marks, brackets, hyphens (compound modifiers), Oxford comma, capitalization (sentence vs title case). Defaults and rationing tables live in references/five-layers.md.
Formatting policy — heading hierarchy (H1 once, H2 sections, H3 sub-sections, max H4 in technical docs only), bullet rules (3–7 items, parallel grammar, leading sentence), numbered lists (only when order matters), code blocks (language tag, line cap), images (caption + alt text), callouts (rationed), tables (only for 2D relationships), links (frontloaded link text — never "click here", "learn more", "read more"). Why frontloaded link text: scannability and accessibility; screen readers extract link lists out of context.
需包含两个不可协商的表格。
标点规则 —— 明确以下各项的规则：破折号、短破折号、分号、冒号、省略号、括号、斜体、粗体、单/双引号、感叹号、方括号、连字符（复合修饰语）、牛津逗号、大小写（句首大写 vs 标题大小写）。默认值及使用限制见references/five-layers.md。
格式规则 —— 标题层级（仅1个H1，H2为章节，H3为小节，技术文档最多使用H4）、项目符号规则（3-7项、语法平行、引导句）、编号列表（仅在顺序重要时使用）、代码块（语言标签、行数限制）、图片（标题+替代文本）、标注（限量使用）、表格（仅用于二维关系）、链接（前置核心链接文本——禁止使用“点击此处”“了解更多”“阅读更多”）。为何前置链接文本： 提升可扫描性和可访问性；屏幕阅读器会脱离上下文提取链接列表。

Phase 5 — Channel-specific overrides

阶段5 —— 渠道特定覆盖规则

For each in-scope channel grouping (see table above), produce a CHANNEL section in PROSE.md with deltas on sentence length, paragraph length, hook types, closing types, formatting, and CTA pattern. Pull the transformation rules from references/channel-adaptation.md.
Generic groupings keep PROSE.md portable: when a brand adds a new platform within a grouping (e.g. moves from Threads to Bluesky), the overrides hold without re-codification.
针对每个适用的渠道分组（见上表），在PROSE.md中生成CHANNEL章节，包含句子长度、段落长度、钩子类型、结尾类型、格式、CTA模式的差异规则。转换规则取自references/channel-adaptation.md。
通用分组确保PROSE.md的可移植性：当品牌在同一分组内新增平台（如从Threads迁移至Bluesky），无需重新编码即可应用覆盖规则。

Phase 6 — Cultural and linguistic adaptation

阶段6 —— 文化与语言适配

  • English variant: declare US / UK / international English (spelling, punctuation, date format)
  • French ↔ English: list the few French words permitted in English text (raison d'être, savoir-faire) and forbid others without translation; conversely declare English loan-words accepted in French (le marketing, le briefing) vs taboo
  • False cognates: éventuellement ≠ eventually, actuellement ≠ actually, important often ≠ important; full list in references/multilingual.md
  • Transfer budgets: cut 20% of words FR→EN, pad 20% EN→FR — French rewards longer sentences, English brand prose favors shorter
  • Locale conventions per channel grouping: French LinkedIn cadence differs from US conventions in formality, paragraph length, first-person use
  • Accessibility and inclusion: bias-free language section (people-first, singular "they", preferred pronouns)
For multilingual brands: one PROSE.md per language, not a translated single guide. Maintain a mapping document of shared pillars and divergent rules.
  • 英语变体：明确美式/英式/国际英语（拼写、标点、日期格式）
  • 法语↔英语：列出允许在英语文本中使用的少量法语词汇（raison d'être、savoir-faire），禁止未翻译的其他法语词汇；反之，明确法语中可接受的英语外来词（le marketing、le briefing）与禁忌词汇
  • 假同源词：éventuellement≠eventually、actuellement≠actually、important常≠important；完整列表见references/multilingual.md
  • 字数转换规则：法语→英语删减20%词汇，英语→法语增加20%词汇——法语偏好长句，英语品牌散文偏好短句
  • 各渠道分组的地域惯例：法国LinkedIn的节奏与美国惯例在正式程度、段落长度、第一人称使用上存在差异
  • 可访问性与包容性：无偏见语言章节（以人为本、单数“they”、首选代词）
对于多语言品牌：每种语言单独生成一份PROSE.md，而非单一指南的翻译版本。需维护一份共享核心规则与差异化规则的映射文档。

Phase 7 — Anti-LLM countermeasures

阶段7 —— 反LLM措施

The dominant prose-drift risk in content factories is convergence on LLM-default register. Codify rules LLMs do not follow by default — that is the durable defense.
Full inventory in references/anti-patterns.md. Headline patterns:
  • Lexical tells: delve, leverage, crucial, robust, underscore, navigate (as transitive metaphor), seamlessly, vibrant, dynamic, embark, foster, harness
  • Structural tells: tricolons in series ("X, Y, and Z"), summative closers ("In conclusion…"), colon-titles ("The Future of X: A New Paradigm"), bullet-list overuse, hedged claims without source
  • Punctuation tells: em-dash overuse (single signal — not proof; see Ann Handley's published rebuttal); ellipsis outside quotation
  • Formula constructions: "It's not just X, it's Y" · "Picture this:" · "Imagine a world where" · "What if I told you" · "Whether you're a seasoned X or a curious newcomer" · "In the realm of" · "Navigating the landscape of"
Diagnose LLM drift quantitatively:
  1. grep -c -iE 'delve|leverage|crucial|robust|underscore'
    across the corpus — frequency ≥1 per 500 words is a strong tell
  2. Sentence-length σ < 4 across a 100-sentence window — uniformity is a stronger tell than any single lexical signal
  3. n-gram comparison between the brand's pre-AI corpus and post-AI corpus — divergence in top trigrams flags drift
Detection is unreliable as a single source of truth. Use these as triage, not verdict. The Stanford HAI / Liang et al. (2023) work showed GPT detectors misclassify TOEFL essays by non-native English writers at headline rates above 60%. Treat any single signal as suspicion, not proof.
内容工厂中最主要的写作风格漂移风险是向LLM默认风格趋同。需编码LLM默认不会遵循的规则——这是持久的防御手段。
完整清单见references/anti-patterns.md。核心模式：
  • 词汇特征：delve、leverage、crucial、robust、underscore、navigate（作为及物隐喻）、seamlessly、vibrant、dynamic、embark、foster、harness
  • 结构特征：三连式排比（“X, Y, and Z”）、总结式结尾（“In conclusion…”）、冒号标题（“The Future of X: A New Paradigm”）、过度使用项目符号、无来源的模糊声明
  • 标点特征：过度使用破折号（单次使用是信号——而非证明；见Ann Handley的公开反驳）、引用外的省略号
  • 公式化表达：“It's not just X, it's Y”·“Picture this:”·“Imagine a world where”·“What if I told you”·“Whether you're a seasoned X or a curious newcomer”·“In the realm of”·“Navigating the landscape of”
量化诊断LLM风格漂移：
  1. 在语料库中使用
    grep -c -iE 'delve|leverage|crucial|robust|underscore'
    ——每500词出现≥1次即为明显特征
  2. 100句窗口中句子长度标准差<4——均匀性比任何单一词汇特征更能说明问题
  3. 品牌AI时代前与AI时代后语料库的n-gram对比——高频三元组的差异标志着风格漂移
单一检测手段不可靠。 这些仅作为初步筛查，而非最终结论。斯坦福HAI/Liang等人（2023）的研究表明，GPT检测器对非英语母语者的TOEFL作文误判率超过60%。将任何单一信号视为可疑，而非定论。

Phase 8 — Render PROSE.md

阶段8 —— 生成PROSE.md

Use the hybrid template in references/prose-md-template.md:
  1. Narrative sections for each layer + policy (the why and the how)
  2. Do/don't tables as an annex (the quick-reference scan layer)
  3. Sample bank: ≥10 before/after pairs, ≥3 exemplar pieces if provided, hook bank and closing bank cross-referenced from 
    samber/cc-skills@copywriting-hooks
    / 
    @copywriting-cta
  4. Cross-references to TONE.md and SOUL.md (read together, not in isolation)
  5. Versioning footer: semver, date, owner, changelog stub
使用references/prose-md-template.md中的混合模板：
  1. 叙事章节：每个维度+规则的原因和执行方式
  2. Do/Don't表格：作为附录（快速参考层）
  3. 样本库：≥10组前后对比样本、≥3篇范例（若提供）、钩子库和结尾库（交叉引用
    samber/cc-skills@copywriting-hooks
    /
    @copywriting-cta
    ）
  4. 交叉引用：关联TONE.md和SOUL.md（需结合阅读，而非单独使用）
  5. 版本控制页脚：语义化版本、日期、负责人、变更日志摘要

ADAPT workflow

ADAPT工作流

Take an existing PROSE.md and project it onto a new channel grouping.
  1. Read the existing PROSE.md.
  2. Ask the user: target channel grouping (long-form / social / email / marketing copy), and optionally a specific platform within the grouping for tighter overrides.
  3. Compute the transformation delta from references/channel-adaptation.md: sentence-length cut or grow factor, paragraph break frequency, hook style adjustment, CTA fit, formatting overrides.
  4. Emit a 
    CHANNEL OVERRIDE — <grouping>
    section appended to PROSE.md, or a standalone 
    PROSE-<grouping>.md
    if the user prefers a separate artifact. Why offer both: content teams that publish across many channels prefer one master file; ghostwriting agencies handling a single channel prefer per-channel files.
  5. Cross-reference back to the original PROSE.md for fields unchanged.
将现有PROSE.md适配至新的渠道分组。
  1. 读取现有PROSE.md。
  2. 询问用户：目标渠道分组（长篇/社交/邮件/营销文案），可选指定分组内的特定平台以获取更精准的覆盖规则。
  3. 根据references/channel-adaptation.md计算转换差异：句子长度增减比例、段落换行频率、钩子风格调整、CTA适配、格式覆盖规则。
  4. 在PROSE.md末尾添加
    CHANNEL OVERRIDE — <分组名>
    章节，或根据用户需求生成独立的
    PROSE-<分组名>.md
    文件。为何提供两种选项： 跨多渠道发布的内容团队偏好单一主文件；仅处理单一渠道的代笔机构偏好分渠道文件。
  5. 交叉引用原始PROSE.md中未变更的内容。

AUDIT workflow

AUDIT工作流

Extract current prose patterns from a corpus before codifying. Empirical patterns beat invented ones.
  1. Take the corpus (folder of 
    .md
    / 
    .txt
    or list of URLs).
  2. For corpora > 50 pieces, parallelize: spin up to 5 sub-agents via the Agent tool, splitting the corpus by date range, channel, or author. Each agent reports back with the same metrics. Why parallel: sequential reading on a 200-piece corpus is slow and runs out of context; parallel sub-agents read independently and synthesize.
  3. Compute (per references/audit-tools.md):
    • Mean sentence length and distribution
    • Top 50 lexemes, top bigrams and trigrams
    • Banned-word and AI-tell frequency
    • Em-dash count per 1,000 words
    • Opening pattern map (first 50 words of 30 pieces, side by side)
    • Closing pattern map
  4. Run an adversarial reading pass on 3–5 representative pieces — challenge the assumption that they work. Mark every sentence that doesn't earn its place, every unanswered reader question, every moment authority collapses, every paragraph where a reader would disengage. See references/audit-tools.md for the methodology.
  5. Sort findings into four buckets: signature (recurring, distinctive, working) · default (recurring, generic, neutral) · noise (inconsistent, accidental, weak) · liability (recurring, actively harming credibility or engagement — the adversarial pass surfaces these).
  6. Produce 
    AUDIT-MEMO.md
    (5–10 pages: quantitative tables + qualitative annotated samples + "keep, kill, differentiate" summary). Feed into BUILD Phase 3.
在编码前从语料库中提取当前写作模式。实证模式永远优于主观设定。
  1. 获取语料库（
    .md
    /
    .txt
    文件夹或URL列表）。
  2. 语料数量>50篇时启用并行处理：通过Agent工具启动最多5个子Agent，按日期范围、渠道或作者拆分语料库。每个Agent返回相同指标。为何并行： 200篇语料的顺序读取速度慢且易超出上下文限制；并行子Agent可独立读取并整合结果。
  3. 计算（依据references/audit-tools.md）：
    • 平均句子长度及分布
    • 前50个词素、前10个二元组和三元组
    • 禁用词汇和AI特征的出现频率
    • 每1000词的破折号数量
    • 开头模式映射（30篇文章的前50词，并列展示）
    • 结尾模式映射
  4. 对3-5篇代表性文章进行对抗式阅读——质疑这些文章的有效性。标记每一句冗余内容、每一个未解答的读者疑问、每一个权威感缺失的时刻、每一个可能导致读者流失的段落。方法论见references/audit-tools.md。
  5. 将发现分为四类：标志性（重复出现、独特有效）·默认（重复出现、通用中性）·噪音（不一致、偶然薄弱）·风险（重复出现、损害可信度或互动性——对抗式阅读可发现此类问题）。
  6. 生成
    AUDIT-MEMO.md
    （5-10页：量化表格+带注释的定性样本+“保留、删除、差异化”摘要）。将结果输入BUILD阶段3。

Output format

输出格式

PROSE.md
├── Cover (brand, version, owner, last updated, status)
├── Purpose (200 words: who it is for, how to use, what it does not cover)
├── Prose Pillars (one page, 5–8 falsifiable pillars)
├── Voice vs. Tone note (one paragraph)
├── 1. Lexicon (narrative + do/don't annex)
├── 2. Syntax
├── 3. Rhythm
├── 4. Structure
├── 5. Voice Markers
├── 6. Punctuation Policy
├── 7. Formatting Policy
├── 8. Channel Overrides (one section per in-scope grouping)
├── 9. Cultural & Linguistic Adaptation
├── 10. Anti-LLM Countermeasures
├── 11. Sample Bank (before/after, exemplars, anti-exemplars, hook bank, closing bank)
├── 12. Ghostwriting Addendum (per principal — optional)
├── Annex A: Do/Don't quick reference (all layers, scannable)
└── Changelog
A complete PROSE.md is 20–60 pages depending on category coverage and channel scope. Resist the urge to maximize length — Siemens reduced their brand guidelines from 2,750 to 250 pages because enforceable density beats exhaustiveness. Aim for the density that an editor can apply line by line; cut anything an editor cannot turn into a concrete edit.
PROSE.md
├── 封面（品牌、版本、负责人、最后更新日期、状态）
├── 目的（200词：适用人群、使用方式、不覆盖范围）
├── 写作核心原则（1页，5-8条可验证原则）
├── 语音vs基调说明（1段落）
├── 1. 词汇（叙事+Do/Don't附录）
├── 2. 句法
├── 3. 节奏
├── 4. 结构
├── 5. 语音标记
├── 6. 标点规则
├── 7. 格式规则
├── 8. 渠道覆盖规则（每个适用分组对应1个章节）
├── 9. 文化与语言适配
├── 10. 反LLM措施
├── 11. 样本库（前后对比、范例、反例、钩子库、结尾库）
├── 12. 代笔补充说明（基于个人——可选）
├── 附录A：Do/Don't快速参考（所有维度，便于扫描）
└── 变更日志
完整的PROSE.md根据品类覆盖范围和渠道规模为20-60页。避免盲目追求篇幅——西门子将其品牌指南从2750页缩减至250页，因为可执行的精简内容优于面面俱到。目标是编辑可逐行应用的精简内容；删除任何无法转化为具体编辑操作的内容。

Reference files (load on demand)

参考文件（按需加载）

FileWhen to read
discovery-questions.mdDuring Phase 1 interview
five-layers.mdDuring Phase 3 codification
category-playbooks.mdDuring Phase 2 after category detection
channel-adaptation.mdDuring Phase 5 and all ADAPT invocations
anti-patterns.mdDuring Phase 7 and AUDIT mode
multilingual.mdDuring Phase 6 when brand operates in EN/FR
prose-md-template.mdDuring Phase 8 render
brand-atlas.mdDuring Phase 2 archetype matching
audit-tools.mdDuring AUDIT mode and Phase 3 corpus diagnosis
文件读取时机
discovery-questions.md阶段1访谈期间
five-layers.md阶段3编码期间
category-playbooks.md阶段2品类检测后
channel-adaptation.md阶段5及所有ADAPT调用期间
anti-patterns.md阶段7及AUDIT模式期间
multilingual.md阶段6品牌使用英/法语时
prose-md-template.md阶段8生成期间
brand-atlas.md阶段2原型匹配期间
audit-tools.mdAUDIT模式及阶段3语料诊断期间

Disclaimer

免责声明

This skill is not exhaustive. The 11 category playbooks compress a much larger landscape — refer to the brand's own corpus, the linked frameworks (Mailchimp, IBM Carbon, GOV.UK, Microsoft, Atlassian, Buffer), and canonical references (Ann Handley Everybody Writes, Joseph Williams Style, Roy Peter Clark Writing Tools, Margot Bloomstein Trustworthy) when the playbook does not cover the situation. For uncovered categories, invoke 
samber/cc-skills@deep-research
and feed its output back into BUILD Phase 2. Prose guides decay; a PROSE.md not re-audited every 12 months is a snapshot, not a living document.
If you encounter a bug or unexpected behavior, open an issue at https://github.com/samber/cc-skills/issues.
本技能并非面面俱到。11个品类手册压缩了更广泛的场景——当手册未覆盖时，参考品牌自身语料库、关联框架（Mailchimp、IBM Carbon、GOV.UK、Microsoft、Atlassian、Buffer）及权威参考资料（Ann Handley《Everybody Writes》、Joseph Williams《Style》、Roy Peter Clark《Writing Tools》、Margot Bloomstein《Trustworthy》）。对于未覆盖的品类，调用
samber/cc-skills@deep-research
并将其输出反馈至BUILD阶段2。写作指南会过时；超过12个月未重新审计的PROSE.md仅为快照，而非动态文档。
若遇到bug或意外行为，请在https://github.com/samber/cc-skills/issues提交问题。