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fuck-slop

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Chinese

F*ck Slop

文本去AI化

Strip every mark of AI writing from a text and make it good in its genre. Not "make it pass a detector" — make it read like a specific person with a specific point wrote it for a specific audience.
清除文本中所有AI写作的痕迹，使其符合对应语体的优质标准。不是“让它通过AI检测工具”——而是让它读起来像是某个有明确观点的人，为特定受众撰写的内容。

Why this is a loop, not a style guide

为何这是循环流程而非风格指南

The worst tells — above all the "not X but Y" family — are not vocabulary mistakes. They are emergent properties of how LLMs generate text: preference tuning rewards balanced, contrastive, comprehensive-sounding framing, so the contrast move is baked into the model's priors. Two consequences drive this skill's architecture:
  1. You cannot reliably see your own slop. The same priors that produce the pattern make it invisible on re-read. Detection must be mechanical — regex against a fixed catalog — never "does this look AI to me?"
  2. Rewriting reintroduces slop. Ask a model to remove "it's not just X, it's Y" and it produces "this is less about X than Y" — the same move in a wig. So every rewrite gets re-scanned, and the loop runs until the scan is clean.
Workflow: Scan → Diagnose → Rewrite by meaning → Re-scan → (repeat) → Register check.
最明显的AI写作特征——尤其是**"不是X而是Y"**这类表述——并非词汇错误，而是LLM生成文本时的涌现特性：偏好调优会奖励平衡、对比、听起来全面的框架，因此这种对比结构已内置于模型的先验知识中。由此产生的两个结果决定了这项技能的架构：
  1. 你无法可靠地发现自己文本中的AI痕迹。生成这些模式的先验知识会让你在重读时对其视而不见。检测必须是机械性的——基于固定目录的正则匹配——绝不能凭“这看起来像AI写的吗？”来判断。
  2. 改写会重新引入AI痕迹。让模型移除“这不只是X，更是Y”的表述，它会生成“这与其说是X，不如说是Y”——换汤不换药的相同结构。因此每次改写后都要重新扫描，循环执行直至扫描结果干净。
工作流程：扫描 → 诊断 → 基于语义改写 → 重新扫描 →（重复）→ 语体校验。

Phase 0: Fix the target

阶段0：明确目标

Before touching the text, establish:
  • Genre and venue — academic article, tweet, reddit post, LinkedIn, email, blog, docs, marketing. If not stated and not obvious from the text, ask. Genre decides which tells are fatal and what "good" means; see references/voices.md.
  • Audience and stance — who reads it, and what the author actually claims. Slop is what fills the space where a claim should be; you cannot remove it without knowing the claim.
  • Constraints — length limits, required citations, house style.
在修改文本前，需明确：
  • 语体与发布渠道——学术论文、推文、Reddit帖子、LinkedIn内容、邮件、博客、文档、营销文案。若用户未说明且从文本中无法判断，需询问用户。语体决定了哪些AI特征是致命的，以及“优质”的标准；详见references/voices.md。
  • 受众与立场——读者是谁，作者实际要表达的主张是什么。AI痕迹就是那些本该是明确主张的位置上的冗余内容；不了解作者的主张就无法清除这些痕迹。
  • 约束条件——字数限制、必填引用、内部格式规范。

Phase 1: Mechanical scan

阶段1：机械扫描

Run the detection patterns from references/tells.md against the text. If the text is in a file (or you can write it to a temp file), run the grep commands in that reference literally — the catalog is written as runnable 
grep -Ein
patterns. Otherwise apply each pattern by hand, line by line.
Produce a finding list: line/sentence, matched pattern, tell category. Also run the two structural checks that regex can't fully catch:
  • Cadence: flag any run of 3+ consecutive sentences within ±4 words of the same length, and any paragraph where every sentence has the same shape (subject–verb–elaboration).
  • Formatting: bold scattered through prose, emoji-decorated headers or bullets, "Term: definition" bullet lists, headers on a text too short to need them, a tidy intro–three-points–conclusion skeleton.
Report the findings to the user as a short table before rewriting (category, count, worst example). This is the diagnosis; the user should see what was wrong.
使用references/tells.md中的检测模式扫描文本。若文本在文件中（或可写入临时文件），直接运行该文档中的grep命令——目录中的模式都是可执行的
grep -Ein
格式。否则需逐行手动应用每个模式。
生成检测结果列表：行/句子、匹配的模式、AI特征类别。同时还要进行两项正则无法完全覆盖的结构检查：
  • 节奏: 标记连续3句及以上长度相差不超过±4词的句子，以及每个句子结构都相同（主语-谓语-补充说明）的段落。
  • 格式: 正文中零散的粗体、带表情符号的标题或项目符号、"**术语：**定义"式的项目符号列表、篇幅过短却添加标题的文本、规整的‘引言-三点论述-结论’框架。
改写前需以简短表格形式向用户汇报检测结果（类别、数量、最典型示例）。这是诊断环节，用户需要清楚问题所在。

Phase 2: Rewrite by meaning, not by frame

阶段2：基于语义改写，而非框架

Go finding by finding. The cardinal rule: never fix a pattern by paraphrasing the pattern. Fix it by deciding what the sentence actually asserts, then asserting that.
逐个处理检测到的问题。核心原则：绝不能通过转述模式来修正模式。要先判断句子实际要表达的主张，然后直接表述该主张。

The "not X but Y" family — three-way triage

"不是X而是Y"类表述——三类处理方式

Every negative parallelism gets exactly one of these treatments:
  1. The negation is a strawman (nobody believes X). Delete the X half entirely and assert Y directly, with whatever evidence the text has.
    • "It's not just a tool, it's a fundamental shift in how teams work" → "Teams that adopted it stopped holding standups within a month."
  2. The contrast is real (people genuinely hold X). Then earn it: name who holds X, say concretely why Y beats it. A real contrast survives being made specific; slop doesn't.
  3. The sentence asserts nothing (the contrast is decoration on an empty claim). Delete the whole sentence. Most cases are this one.
Banned escape hatches — these are the same move and count as new findings: "less about X than Y", "X matters, but Y matters more", "the real X is Y", "the question isn't X, it's Y", "X? Y." (rhetorical-question variant), and the em-dash variant "— not X, but Y".
所有反向平行结构都需采用以下三种处理方式之一：
  1. 否定部分是稻草人谬误（没人会相信X）。直接删除X部分，用文本中已有的证据直接表述Y。
    • "这不只是一个工具，更是团队工作方式的根本性转变" → "采用该工具的团队在一个月内就停止了每日站会。"
  2. 对比是真实存在的（确实有人持有X观点）。那就具体化：说明谁持有X观点，具体阐述Y为何更优。真实的对比经得住具体化，而AI痕迹则不行。
  3. 句子无实质主张（对比只是空洞主张的装饰）。删除整个句子。大多数情况都属于此类。
禁止使用的规避手段——这些属于同类结构，会被视为新的AI痕迹："与其说是X不如说是Y"、"X很重要，但Y更重要"、"真正的X是Y"、"问题不在于X，而在于Y"、"X？Y。"（反问变体），以及破折号变体"——不是X，而是Y"。

Everything else

其他AI特征处理

  • Puffery and inflated vocabulary (pivotal, seismic, testament, tapestry, landscape, delve…): replace with the plain word, or with the concrete fact the puffery was hiding. "Plays a vital role in" → "does".
  • Rule-of-three lists: keep the strongest item, cut the rest — unless all three carry distinct information, in which case keep them and break the rhythm (different lengths, different syntax).
  • False ranges ("from X to Y"): if you can't name a meaningful midpoint between X and Y, it's not a range — name the two things or cut one.
  • Hedged both-sidesing ("it's worth noting", auto-counterpoints, "while X, it's also true that Y"): commit. One opinion, stated, owned. A counterpoint stays only if the author genuinely concedes it.
  • Uniform cadence: vary deliberately. Follow a long sentence with a short one. Fragments are legal. Don't apply a formula (alternating long/short is its own tell) — read the paragraph aloud and break wherever the rhythm is metronomic.
  • Low specificity: replace "many companies" / "studies show" / "recent research" with the actual names, numbers, and dates — only from the source text, the conversation, or verifiable research you actually do. Never invent specifics. If the author needs to supply one, leave a marked placeholder: 
    [ADD: which study?]
    .
  • Stock skeleton: kill throat-clearing openers ("In today's fast-paced world…"), summary conclusions ("In conclusion… Ultimately…"), and engagement-bait endings ("What do you think?"). Start where the point starts; stop when it's made.
  • 浮夸词汇（pivotal、seismic、testament、tapestry、landscape、delve等）：替换为平实词汇，或替换为浮夸词汇所掩盖的具体事实。"在……中发挥至关重要的作用"→"用于"。
  • 三段式列表：保留最有力的一项，删除其余内容——除非三项都承载不同信息，这种情况下可保留，但要打破节奏（不同长度、不同句式）。
  • 虚假范围词（"从X到Y"）：若无法说出X和Y之间有意义的中间项，那就不是范围——直接列出这两个事物或删除其中一个。
  • 模棱两可的折中表述（"值得注意的是"、自动反驳、"虽然X，但Y也成立"）：明确立场。只表述一个观点，并为其负责。只有当作者真正认可反驳观点时，才保留该反驳。
  • 统一节奏：刻意变换节奏。长句后接短句。允许使用碎片句。不要套用固定公式（长短句交替本身也是一种AI特征）——大声朗读段落，在节奏呆板的地方断开。
  • 低特异性：将"许多公司"/"研究表明"/"近期研究"替换为实际名称、数字和日期——仅可使用源文本、对话内容或你实际查证过的可验证信息。绝不能编造细节。若需要作者提供相关信息，留下标记占位符：
    [补充：哪项研究？]
    。
  • 固定框架：删除套话开头（"在当今快节奏的世界中……"）、总结式结尾（"综上所述……最终……"）和引流式结尾（"你怎么看？"）。从观点开始处写起，观点表述完毕即停止。

What not to do — overcorrection is also slop

切勿过度修正——过度修正也是AI痕迹

  • No fake typos, forced slang, or manufactured "voice". Humanizer-tool output is its own genre of slop.
  • Em dashes are not banned. Humans use them. The tell is density and the double-dash "— not X, but —" move. Budget: at most one em dash per ~150 words, never two in a sentence.
  • Don't trade precision for personality in academic or technical text. There, de-slopping means cutting puffery and committing to claims — not adding attitude.
  • Preserve the author's meaning, claims, and facts exactly. This is a style pass, not a content edit. Flag, don't silently fix, anything that looks factually wrong.
  • 不要添加虚假拼写错误、强行使用俚语或刻意制造"语气"。拟人化工具的输出本身就是一种AI痕迹。
  • 破折号并未被禁用。人类也会使用破折号。AI特征在于破折号的密度以及"——不是X，而是——"这类用法。使用上限：每约150字最多使用1个破折号，同一句子中绝不能使用2个。
  • 在学术或技术文本中，不要为了个性而牺牲准确性。此类文本的去AI化意味着删除浮夸表述并明确主张——而非添加态度。
  • 严格保留作者的原意、主张和事实。这是风格修正，而非内容编辑。若发现疑似事实错误，应标记出来，而非默默修改。

Phase 3: Verify loop

阶段3：验证循环

Re-run the full Phase 1 scan on your rewritten text. This step is not optional and not a formality — expect your own rewrite to contain new tells, because the model writing it has the same priors that created them. Fix and re-scan until a pass produces zero pattern hits and the cadence check passes. Cap at 4 passes; if a pattern survives 4 passes, rewrite that sentence from scratch starting from its bare claim ("what fact or opinion is this sentence for?").
对你改写后的文本重新执行阶段1的完整扫描。这一步是必选项，绝非形式主义——你的改写内容很可能会引入新的AI痕迹，因为生成改写内容的模型和生成原AI文本的模型有着相同的先验知识。修正后重新扫描，直至扫描结果无匹配模式且节奏检查通过。最多执行4次循环；若某一模式经过4次循环仍存在，则从句子的核心主张（"这句话要表达什么事实或观点？"）出发，彻底重写该句子。

Phase 4: Register check

阶段4：语体校验

Check the clean text against its genre profile in references/voices.md: right length, right formality, right person, genre-specific tells gone (e.g. on reddit: no bold, no bullet essay; in academic prose: no first-person hot takes added). Then the final test — read it aloud. Anywhere you wouldn't say it to the actual audience, rewrite that sentence.
Deliver: the rewritten text, plus a brief change log (categories fixed, counts, and number of verify passes it took).
对照references/voices.md中的语体规范检查清洗后的文本：长度合适、正式程度恰当、人称正确、语体特有的AI特征已清除（例如在Reddit上：无粗体、无项目符号式文章；在学术文本中：未添加第一人称主观观点）。然后进行最终测试——大声朗读文本。任何你不会对实际受众说的内容，都要重写。
交付内容：改写后的文本，加上简短的修改日志（修正的类别、数量、验证循环的次数）。",