Back to Details

deslopify

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

Deslopify

Deslopify

Strip the AI fingerprints out of a text and make it good in its genre. The target is prose that reads like one person wrote it for one audience about something they actually know. Detector scores are beside the point; text can score human and still be unreadable.
去除文本中的AI写作痕迹，使其符合对应体裁的优质标准。目标是让文稿读起来像是一个了解相关内容的人，为特定受众撰写的。检测分数并非关键；有些文本可能被判定为人类撰写，但仍然难以阅读。

Why this loops

为何需要循环处理

The "not X but Y" family and its relatives are generative habits. Preference tuning rewards balanced, contrastive, comprehensive-sounding framing, so the contrast move sits deep in the model's priors and surfaces about once a paragraph. Two consequences shape this skill:
  1. You cannot see your own slop. The priors that produce the pattern also make it invisible on re-read. So detection runs mechanically, as regex against a fixed catalog. "Does this look AI to me?" is not a detection method.
  2. Rewriting reintroduces slop. Ask a model to remove "it's not just X, it's Y" and out comes "this is less about X than Y", the same move in a wig. Every rewrite therefore gets re-scanned, and the loop runs until a scan comes back clean.
Workflow: Scan → Diagnose → Rewrite by meaning → Re-scan → (repeat) → Register check.
"不是X而是Y"这类表述及其变体是生成式AI的固有写作习惯。偏好调优会奖励平衡、对比、听起来全面的框架，因此这种对比手法深深根植于模型的先验知识中，大约每段都会出现一次。由此产生的两个特点决定了这项技能的处理方式：
  1. 你无法自行察觉AI痕迹：生成这类模式的先验知识也会让你在重读时无法发现它们。因此检测需通过机械方式进行，即使用正则表达式匹配固定的特征库。"这段文本看起来像AI写的吗？"并不是有效的检测方法。
  2. 改写会重新引入AI痕迹：让模型去除"不只是X，更是Y"的表述，结果可能会变成"与其说这是X，不如说是Y"——换汤不换药的相同手法。因此每次改写后都需要重新扫描，循环执行直到扫描结果显示文本干净为止。
工作流程：扫描 → 诊断 → 基于语义改写 → 重新扫描 →（重复）→ 风格校验

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. Ask when it isn't stated and isn't obvious from the text. 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 fills the space where a claim should be, so you cannot remove it without knowing the claim.
  • Constraints. Length limits, required citations, house style.
在修改文本前，先确定以下内容：
  • 体裁与发布渠道：学术论文、推文、Reddit帖子、LinkedIn内容、邮件、博客、文档、营销文案。当用户未明确说明且无法从文本中推断时，主动询问。体裁决定了哪些AI痕迹是致命的，以及"优质"的定义；详见references/voices.md。
  • 受众与立场：读者是谁，作者实际想要表达的主张是什么。AI痕迹会填补本应放置明确主张的空白，因此若不了解核心主张，就无法有效去除这些痕迹。
  • 约束条件：字数限制、必要引用、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 or sentence, matched pattern, tell category. Then run the two structural checks regex can't 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. The user should see the diagnosis.
使用references/tells.md中的检测规则扫描文本。如果文本在文件中，或可写入临时文件，直接运行该文档中的grep命令：特征库以可执行的
grep -Ein
模式编写。否则手动逐行应用每个规则。
生成检测结果列表：行号或句子、匹配的规则、AI痕迹类别。然后执行两项正则表达式无法捕捉的结构检查：
  • 节奏检查：标记任何连续3句及以上句子长度相差不超过4词的段落，以及所有句子结构相同（主语+谓语+修饰）的段落。
  • 格式检查：标记散文中零散的粗体、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、篇幅过短却使用标题的文本，以及规整的"引言-三点论述-结论"框架。
在改写前向用户提交简短的检测报告表格：类别、数量、最典型示例。用户需要了解诊断结果。

Phase 2: Rewrite by meaning

阶段2：基于语义改写

Go finding by finding. The cardinal rule: never fix a pattern by paraphrasing the pattern. Decide what the sentence asserts, then assert that.
逐一处理检测结果。核心原则：永远不要通过改写模式来修复模式。先明确句子要表达的内容，然后直接表述该内容。

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

"不是X而是Y"类表述：三种处理方式

Every negative parallelism gets exactly one of these treatments:
  1. Strawman negation (nobody believes X). Delete the X half 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. Real contrast (people genuinely hold X). Earn it: name who holds X, say concretely why Y beats it. A real contrast survives being made specific; slop doesn't.
  3. Empty claim (the contrast decorates a sentence that asserts nothing). Delete the sentence. Most cases are this one.
Banned escape hatches, all the same move, all counted 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." (the rhetorical-question variant), and the em-dash form "— 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. 稻草人式否定（无人认同X）：删除X部分，直接表述Y，并附上文本中提供的证据。
    • "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. 真实对比（有人确实认同X）：合理表述对比：指出认同X的人群，具体说明Y优于X的原因。真实的对比经得起具体化的检验；而AI痕迹则不行。
  3. 空洞主张（对比只是装饰，句子未表达任何实质内容）：删除该句子。多数情况属于此类。
禁止使用以下逃避手段，这些本质上都是相同的手法，会被视为新的AI痕迹："与其说X不如说Y"、"X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），以及破折号形式的"——不是X，而是Y"。

Everything else

其他AI痕迹处理

  • Puffery and inflated vocabulary (pivotal, seismic, testament, tapestry, landscape, delve…). Replace with the plain word, or with the concrete fact the puffery hides. "Plays a vital role in" → "does".
  • Rule-of-three lists. Keep the strongest item, cut the rest. Where all three carry distinct information, keep them and break the rhythm with different lengths and different syntax.
  • False ranges ("from X to Y"). If you can't name a meaningful midpoint between X and Y, name the two things plainly 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 where the author genuinely concedes it.
  • Uniform cadence. Vary deliberately. Follow a long sentence with a short one. Fragments are legal. Avoid formulas, since alternating long and short is its own tell; read the paragraph aloud and break wherever the rhythm goes metronomic.
  • Low specificity. Replace "many companies" / "studies show" / "recent research" with actual names, numbers, and dates, drawn only from the source text, the conversation, or research you actually do. Never invent specifics. Where the author has 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（关键的、颠覆性的、证明、织锦、格局、深入探究……）：替换为平实词汇，或替换为浮夸词汇所掩盖的具体事实。"发挥至关重要的作用" → "负责"。
  • 三段式列表：保留最有力的一项，删除其余内容。若三项均包含不同信息，则全部保留，并通过改变句子长度和语法结构打破节奏。
  • 虚假范围表述（"从X到Y"）：若无法说出X和Y之间有意义的中间项，则直接列出两者或删除其一。
  • 模棱两可的两面性表述（"值得注意的是"、自动提出反方观点、"虽然X，但Y也成立"）：明确立场。只表述一个观点，清晰且明确。只有当作者真正想要让步时，才保留反方观点。
  • 统一节奏：刻意变换节奏。长句后接短句。允许使用碎片句。避免固定模式，因为交替长短句本身也可能成为AI痕迹；大声朗读段落，在节奏变得机械的地方断句。
  • 低特异性表述：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，且只能来自源文本、对话内容或你实际完成的研究。切勿编造细节。若需要作者补充信息，留下标记占位符：
    [ADD: 哪项研究？]
    。
  • 固定框架：删除冗余的开场白（"在当今快节奏的世界中……"）、总结性结尾（"综上所述……最终……"）和互动式结尾（"你怎么看？"）。从核心观点开始；观点表述完毕即停止。

Overcorrection is also slop

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 stay legal. Humans use them. The tell is density, plus the contrast form "— not X, but Y". Budget: at most one em dash per ~150 words, never two in a sentence.
  • Keep precision in academic and technical text. There, de-slopping means cutting puffery and committing to claims. Adding attitude makes it worse.
  • Preserve the author's meaning, claims, and facts exactly. This is a style pass. Flag anything that looks factually wrong rather than silently fixing it.
  • 不要添加虚假拼写错误、生硬俚语或刻意制造的"语气"。人性化工具的输出本身就是一种AI痕迹。
  • 破折号是合法的。人类也会使用破折号。AI痕迹在于破折号的密度，以及"——不是X，而是Y"的对比形式。使用限制：每约150词最多使用1个破折号，同一句子中不得使用2个。
  • 保留学术和技术文本的精确性。在这类文本中，去AI化意味着删除浮夸词汇并明确主张。添加主观态度会适得其反。
  • 严格保留作者的原意、主张和事实。这只是风格优化。若发现事实错误，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. Expect the rewrite to carry fresh tells, because the model producing it has the same priors that produced the originals. Skipping this step is how slop survives the pass. Fix and re-scan until one pass returns 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?
对改写后的文本重新执行完整的阶段1扫描。预计改写后的文本会带有新的AI痕迹，因为生成改写内容的模型与生成原文的模型拥有相同的先验知识。跳过此步骤会导致AI痕迹残留。修改后重新扫描，直到某次扫描无匹配规则且节奏检查通过。最多循环4次。若某一模式在4次循环后仍存在，则从句子的核心主张（该句子要表达的事实或观点是什么？）出发，彻底重写该句子。

Phase 4: Register check

阶段4：风格校验

Check the clean text against its genre profile in references/voices.md: right length, right formality, right person, genre-specific tells gone. On reddit that means no bold and no bullet essay. In academic prose it means no first-person hot takes added. Then read it aloud. Anywhere you wouldn't say it to the actual audience, rewrite that sentence.
Deliver the rewritten text and a short change log: categories fixed, counts, and how many verify passes it took.
对照references/voices.md中的体裁特征检查优化后的文本：字数合适、正式程度恰当、人称正确、体裁特有的AI痕迹已去除。例如，Reddit内容不应包含粗体和项目符号式的散文；学术文本不应添加第一人称的主观观点。然后大声朗读文本。任何你不会对实际受众说的内容，都要改写该句子。
交付改写后的文本和简短的变更日志：修复的类别、数量，以及验证循环的次数。",