humanize

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Humanize Text Skill

文本人性化处理技能

Transforms AI-generated or flat text into output that mirrors the statistical and stylistic fingerprint of human writing. Grounded in the published detection literature; sources live in
references/research.md
(background only, not needed during a rewrite).

将AI生成或平淡生硬的文本转换为符合人类写作统计特征与风格特征的输出。本技能基于已发表的AI检测相关文献开发;相关资料可查阅
references/research.md
(仅作背景参考,改写过程中无需查阅)。

Hard rules (read first, enforce last)

硬性规则(先阅读,最后执行)

These seven fail more often than everything else combined, because the model that wrote the draft is the model checking it. You systematically overproduce these patterns; your draft contains em dashes even when you don't remember writing them. Treat "my draft is probably clean" as false by default.
  1. Em dashes: at most one per 300 words of output. Under 300 words, zero.
  2. Semicolons: none, unless a list item itself contains commas or the register is explicitly formal/academic (Lever 8).
  3. Straight quotes and apostrophes only. Never curly. Sole exception: publishing contexts where typographic quotes are house style (Lever 8).
  4. Banned vocabulary (full list at end of this skill). Highest-frequency offenders: delve, leverage, utilize, robust, comprehensive, streamline, furthermore, moreover, "it is important to note".
  5. No negation framing: "not just X", "not X, it's Y", "it's not about X, it's about Y", "more X than Y". Say what the thing IS. Poetic forms count: "isn't proof you failed, it's proof you showed up" is the same banned pivot wearing a nicer coat. (False binaries like "either X or Y" are handled by the Signal I checklist's either/or item.)
  6. Output shape: the rewritten text only. No preamble ("Here's the humanized version:"), no trailing changelog ("Main moves:", "What I changed:"). The ONLY permitted additions are the two meta-notes mandated by protocol steps 2 and 5.6, appended after the rewrite. If the user wants the diff explained, they'll ask.
  7. Sentence-length spread: in any output over ~80 words, the longest sentence must beat the shortest by 20+ words, and fewer than half the sentences may sit in the 10-to-20-word band. Your uncorrected rhythm clusters at 10-20 words with ~6 words of deviation; that uniformity is a measured tell even when every other rule passes. Verify from the written count list (step 5), never by feel.
These apply in EVERY register, including creative, lyrical, and narrative prose. An em dash in a poem is still an em dash to a detector, and creative registers are where the "this one is doing literary work" rationalization kicks in hardest.
Enforcement is positional: stated here at the top, checked at the END against the finished draft by re-reading the actual draft text and counting occurrences (protocol steps 4-5). Never mark them clean from memory. If long context forces you to drop every other rule in this skill, keep these seven.

这七条规则的违规频率远超其他所有规则之和,因为撰写初稿的模型同时也是检查模型。你会系统性地过度使用这些模式;即便你不记得自己写过,初稿中也会出现破折号。默认假设“我的初稿应该没问题”是错误的。
  1. 破折号:每300词输出中最多使用1个。若输出不足300词,则完全禁用。
  2. 分号:除非列表项本身包含逗号,或是语体明确为正式/学术性(第8级语体),否则完全禁用。
  3. 仅使用直引号和直撇号:绝对不能使用弯引号。唯一例外:出版场景中,排版引号为官方格式要求(第8级语体)。
  4. 禁用词汇(完整列表见本技能末尾):高频违规词汇包括:delve、leverage、utilize、robust、comprehensive、streamline、furthermore、moreover、"it is important to note"。
  5. 禁止否定式框架:避免使用“不只是X”“不是X,而是Y”“重点不是X,而是Y”“与其说是X,不如说是Y”这类表述。直接说明事物本身是什么。诗意化表达也在此列:“这不是你失败的证明,而是你曾尝试的证明”本质上也是被禁止的转折表述。(“要么X要么Y”这类虚假二元对立将由Signal I检查清单中的对应条目处理。)
  6. 输出形式:仅保留改写后的文本。不得添加前置说明(如“以下是人性化处理后的版本:”),不得添加末尾变更日志(如“主要修改点:”“我做了这些改动:”)。唯一允许添加的内容是协议步骤2和5.6要求的两条元注释,需附加在改写文本之后。若用户需要解释改动差异,他们会主动询问。
  7. 句子长度分布:若输出文本超过约80词,最长句子与最短句子的长度差必须达到20词以上,且长度在10-20词区间的句子占比不得超过一半。未经修正的文本节奏集中在10-20词区间,偏差约为6词;这种均匀性即便在其他所有规则都符合的情况下,也是可被检测到的明显特征。必须依据书面计数列表(步骤5)进行验证,绝不能凭感觉判断。
这些规则适用于所有语体,包括创意、抒情和叙事散文。诗歌中的破折号对检测工具来说依然是破折号,而创意语体恰恰是“这个破折号有文学作用”这类合理化借口最容易出现的场景。
执行规则时需注意顺序:这些规则列在最前面,必须在完成初稿后重新通读实际文本并计数(协议步骤4-5),以此检查是否违规。绝不能凭记忆判定文本合规。若长上下文迫使你放弃本技能中的其他所有规则,也要坚守这七条。

Mental model: what detectors actually measure

思维模型:检测工具实际测量的内容

Nine signals: eight stylometric plus the RLHF fingerprint. Your output must move in the human direction on ALL of them.
SignalAI direction (avoid)Human direction (target)
PerplexityPredictable, low-surprise word choicesOccasional unexpected but apt words; word choices driven by rhythm, specificity, or memory
BurstinessUniform sentence length (~15–20 words every time)Aggressive alternation: short punchy sentences. Then a longer one that builds and unfolds over a clause or two.
Hedge densityOveruse of "often", "generally", "typically", "it is important to note"Hedges only when actually uncertain; direct assertion otherwise
Lexical repetitionSame root words recycled across paragraphsNatural semantic diversity; synonyms and reformulations
Structural markersBullet lists for everything; numbered steps; excessive subheadingsFlowing prose; structure emerges from content, not imposed on it
Personal/emotional specificityGeneric, neutral, applicable-to-anyone claimsSpecific: exact numbers, named examples, temporal anchors ("last quarter", "when I ran X")
POS densityHigh adjective/auxiliary verb density; subordinating conjunctions everywhereNouns and verbs do the heavy lifting; adjectives earned, not decorative
Punctuation fingerprintEm dashes for drama, semicolons to link clauses, mid-sentence colons — all overusedPeriods do the work. Em dashes rare. Semicolons almost never. Colons mainly to introduce lists.
The levers below are the write-side counterparts of the signals
ai-check
grades (A–I): 1→A, 2→B, 3→C, 4→D, 5→E, 6→H, 7→F, 8→G, 9→I (RLHF subset). The full rhetorical-scaffolding catalog for Signal I is enforced by the audit pass (step 5.5), not by any single lever.

九个信号:八个文体特征信号加上RLHF特征。你的输出必须在所有信号上都向人类写作的方向靠拢。
信号AI写作特征(需避免)人类写作特征(目标)
困惑度可预测、低惊喜感的词汇选择偶尔使用出人意料但贴切的词汇;词汇选择受节奏、具体性或记忆驱动
突发度句子长度均匀(每次约15-20词)大幅交替:简短有力的句子,之后是一个较长的句子,包含一两个从句并逐步展开
模糊表述密度过度使用“often”“generally”“typically”“it is important to note”等表述仅在确实存在不确定性时使用模糊表述;否则直接断言
词汇重复度段落间重复使用同一词根的词汇自然的语义多样性;使用同义词和重新表述
结构标记凡事都用项目符号列表;编号步骤;过多小标题流畅的散文;结构由内容自然呈现,而非强加
个人/情感具体性通用、中性、适用于任何人的表述具体表述:精确数字、命名示例、时间锚点(如“上个季度”“我运行X的时候”)
词性密度形容词/助动词密度高;随处可见从属连词名词和动词承担主要表意;形容词需有实际意义,而非装饰性
标点特征过度使用破折号制造戏剧效果、分号连接从句、句中冒号主要使用句号。破折号极少使用。分号几乎不用。冒号主要用于引出列表
下方的操作杠杆对应
ai-check
评分(A-I)的信号:1→A,2→B,3→C,4→D,5→E,6→H,7→F,8→G,9→I(RLHF子集)。Signal I的完整修辞框架目录由审核环节(步骤5.5)执行,而非单个操作杠杆。

Nine humanization levers, apply all of them

九大人性化操作杠杆,需全部应用

Lever 1: Perplexity injection (word-level)

杠杆1:困惑度注入(词汇层面)

Replace predictable vocabulary with words a real person would choose given this context:
  • Swap generic verbs for specific ones: "address" → "untangle", "utilize" → "lean on", "implement" → "wire up"
  • Let the subject matter suggest the vocabulary: a Go engineer says "flush the buffer", not "clear the temporary data storage"
  • One or two genuinely surprising but accurate word choices per paragraph
  • Avoid: "delve", "leverage", "robust", "streamline", "significant", "comprehensive", "notably", "it is worth noting", "in today's fast-paced world"
Watch for elegant variation (synonym cycling). LLMs cycle synonyms for the same referent: "The protagonist faces challenges. The main character must adapt. The central figure triumphs." Same person, three labels. Rule: pick the canonical noun per referent and use it consistently; vary with a pronoun, not a synonym. "the company / the firm / the organization" → "the company" + "it".
用真实人类在当前语境下会选择的词汇替换可预测词汇:
  • 将通用动词替换为具体动词:“address”→“梳理”,“utilize”→“借助”,“implement”→“搭建”
  • 根据主题选择适配词汇:Go工程师会说“flush the buffer”,而非“清除临时数据存储”
  • 每段使用一两个真正出人意料但准确的词汇
  • 避免使用:“delve”“leverage”“robust”“streamline”“significant”“comprehensive”“notably”“it is worth noting”“in today's fast-paced world”
注意优雅变换(同义词循环)。LLM会为同一指代对象循环使用同义词:“主角面临挑战。主要人物必须适应。核心角色取得胜利。”同一个人,三种称呼。规则:为每个指代对象选择标准名词并保持一致;用代词替换,而非同义词。“the company / the firm / the organization”→“the company”+“it”。

Lever 2: Burstiness injection (sentence-level)

杠杆2:突发度注入(句子层面)

Enforce sentence length variance. Target: standard deviation of sentence word count > 8. You can't compute stdev mentally, so enforce these two countable proxies instead; BOTH are required (hard rule 7):
  • Range floor: longest sentence minus shortest ≥ 20 words. In practice: at least one fragment of 5 words or fewer AND at least one 25-plus-word sentence that earns its length.
  • Mid-band cap: fewer than half the sentences in the 10-to-20-word band. Satisfying the range floor with one fragment and one long sentence while everything else sits at 12-16 words still reads uniform; the middle must spread too.
Supporting rules:
  • Every 3–4 sentences, insert one sentence of 5 words or fewer. Just drop it. Like that.
  • Never more than 3 consecutive sentences within 5 words of each other in length.
  • Burstiness fails in both directions: a run of shorts without a longer counterweight reads choppy, not punchy.
  • Mid-paragraph uniformity trap: openers and closers vary, but the middle 3–4 sentences collapse into the same band. Break one.
强制实现句子长度差异。目标:句子词数标准差>8。你无法在脑中计算标准差,因此改用两个可计数的替代指标;两者都必须满足(硬性规则7):
  • 范围下限:最长句子与最短句子的长度差≥20词。实际操作:至少包含1个5词或更短的片段,以及1个25词以上且长度合理的句子。
  • 中间区间上限:长度在10-20词区间的句子占比不足一半。仅用1个片段和1个长句子满足范围下限,而其他句子都集中在12-16词区间,依然会显得均匀;中间区间的句子长度也必须分散。
辅助规则:
  • 每3-4个句子,插入1个5词或更短的句子。直接插入即可。就像这样。
  • 连续3个句子的长度差不得超过5词。
  • 突发度在两个方向上都可能失效:连续使用短句而没有长句平衡,会显得生硬,而非有力。
  • 段落中间均匀陷阱:开头和结尾句子长度不同,但中间3-4个句子长度趋同。需打破这种情况。

Lever 3: Hedge surgery

杠杆3:模糊表述修正

Audit every softening word:
  • Delete: "it is important to note that", "it is worth mentioning that", "generally speaking", "in many cases", "it can be argued", "often", "typically" (unless genuinely needed for accuracy)
  • Replace with direct assertion: "This matters because X" not "It is important to consider that X may be relevant"
  • Real uncertainty gets human phrasing: "I'm not sure this holds for edge cases, but..." not "while results may vary"
  • Don't soften real rules with "almost always" / "generally". If exceptions exist, name them: "This breaks down when X."
  • No announcement-colon openers: "The rule I use:", "The key insight:" — state the rule directly. (Pattern announcement in general is a Signal I checklist item.)
  • A sentence doing two logical jobs (comparison AND conclusion) usually reads cleaner as two.
Filler-phrase substitutions (the pattern generalizes: any multi-word wrapper around a one-word meaning gets the one word):
Verbose (AI)Concise (human)
Due to the fact thatBecause
In the event thatIf
Has the ability / capacity toCan
Make a decision / an assumptionDecide / Assume
For the purpose ofTo / For
With regard to / With respect toAbout / On
Prior to / Subsequent toBefore / After
In light of the fact that / Despite the fact thatSince / Although
In the process of / The fact that(drop entirely; rephrase)
检查每一个弱化语气的词汇:
  • 删除:“it is important to note that”“it is worth mentioning that”“generally speaking”“in many cases”“it can be argued”“often”“typically”(除非确实为了准确性需要)
  • 替换为直接断言:用“这很重要,因为X”替代“需要考虑的是X可能相关”
  • 真实的不确定性用人类化表述:“我不确定这在边缘案例中是否成立,但……”而非“结果可能有所不同”
  • 不要用“almost always”/“generally”弱化真实规则。若存在例外,明确指出:“当X时,这条规则不适用。”
  • 禁止使用宣告式冒号开头:“我使用的规则:”“核心见解:”——直接陈述规则。(总体而言,模式宣告是Signal I检查清单中的条目。)
  • 一个句子承担两个逻辑任务(比较+结论)时,通常拆分为两个句子会更清晰。
填充短语替换(模式通用:任何用多词包装单词语义的表述,都替换为单个词):
冗余表述(AI风格)简洁表述(人类风格)
Due to the fact that因为
In the event that如果
Has the ability / capacity to
Make a decision / an assumption决定 / 假设
For the purpose of为了 / 用于
With regard to / With respect to关于 / 针对
Prior to / Subsequent to在……之前 / 在……之后
In light of the fact that / Despite the fact that既然 / 尽管
In the process of / The fact that(完全删除;重新表述)

Lever 4: Structural flattening

杠杆4:结构扁平化

Rhetorical scaffolding patterns (either/or binaries, chiasmus, tricolons, balanced parenthetical pairs, anaphora, "turns out" pivots, thesis-first openers incl. "X is the easy/hard part", mini-aphorism closers, parallel-subject mirrors) are catalogued ONCE in the Signal I checklist (step 5.5); negation pivots live in hard rule 5 and the step-4 diminishment scan. Apply the checklist at write time too. This table covers only what the checklist doesn't:
AI patternHuman replacement
Intro sentence + 3-bullet listProse paragraph where items are joined by flow, not bullets
"There are three main factors: ..."Just talk about the factors; transitions carry the structure
"In conclusion, ..."End mid-thought if the thought is complete; or "The net of all this..." / "Bottom line:"
Numbered sections for everythingSections only when content is genuinely enumerable and order matters
Topic sentence + evidence + restatementSkip the restatement; humans don't recap what they just said
Formula personal essay opener: "The [noun] I [remember/think about] most [adverb]"Start with the incident itself: "In 2019 I shipped a rate limiter that fell apart the first hour it hit real traffic."
Intensifier/diminisher opposition: "X obsessively / Y barely at all"Make the contrast asymmetric: "I tested the happy path constantly. The failure paths got one pass."
Landing phrase: "is the actual/real work"State the conclusion without the landing phrase.
Local coherence over-smoothEvery sentence connects perfectly; reads too uniform, survives surface rewriting. Fix: one sentence per paragraph that slightly misfires — a thought that shifts direction, a word more casual than the register, a connection that isn't clean.
"Laid out that way" / "Seen this way" reframe pivotMake the observation directly.
Perfect paragraph-per-idea essay arcLet one paragraph do two jobs, or leave a thought unresolved.
Three-act Slack/update structureBreak with a fourth element that doesn't fit the arc.
Copula avoidance: "X serves as Y", "X stands as Y", "X marks/represents/boasts/features/offers Y"Use "is" or "has": "Gallery 825 is LAAA's exhibition space."
Significance inflation: "stands as a testament to", "marks a pivotal moment in", "evolving landscape", "setting the stage for"Cut, or replace with the concrete claim: "established in 1989 to publish regional statistics independently."
Promotional register: "nestled in the heart of", "vibrant", "breathtaking", "must-visit", "boasts a rich heritage", "renowned for"Cut the brochure language: "Alamata is a town in the Gonder region known for its weekly market."
Vague attributions: "Industry observers have noted", "Experts argue", "Critics have suggested"Name a specific source or drop the claim.
Outline-formula "Challenges and Future Prospects" sectionsReplace with the specific challenges and what's being done, or drop the section.
修辞框架模式(非此即彼二元对立、交错配列、三排比、平衡插入语对、首语重复、“turns out”转折、先论点后内容的开头如“X是简单/困难的部分”、微型警句结尾、平行主语镜像)在Signal I检查清单(步骤5.5)中有统一记录;否定转折属于硬性规则5和步骤4的弱化表述扫描范围。写作时也需应用该清单。下表仅涵盖清单未提及的内容:
AI模式人类化替代方案
介绍句 + 3项项目符号列表散文段落,内容通过逻辑衔接而非项目符号连接
“主要有三个因素:……”直接谈论这些因素;过渡句承载结构
“In conclusion, ...”若表述已完整,可在中途结束;或用“总而言之……”“底线是:”
凡事都用编号章节仅当内容确实可枚举且顺序重要时使用章节
主题句 + 证据 + 重述跳过重述;人类不会重复刚说过的内容
公式化个人散文开头:“我最[记得/思考]的[名词]是[副词]”直接从事件切入:“2019年我上线了一个速率限制器,它在上线后的第一个小时就崩溃了。”
强化/弱化对立:“X极度…… / Y几乎不……”制造不对称对比:“我不断测试正常路径。失败路径只测试了一次。”
收尾短语:“is the actual/real work”直接陈述结论,去掉收尾短语。
局部连贯性过度平滑每个句子都完美衔接;读起来过于均匀,表面改写无法解决。修正方法:每段设置一个略有偏差的句子——一个转向的想法、一个比语体更随意的词、一个不清晰的衔接。
“Laid out that way” / “Seen this way”重构转折直接陈述观察结果。
完美的“一段一观点”文章结构让一个段落承担两个任务,或留下一个未解决的想法。
三幕式Slack/更新结构添加一个不符合该结构的第四元素,打破原有框架。
避免使用系动词:“X serves as Y”“X stands as Y”“X marks/represents/boasts/features/offers Y”使用“is”或“has”:“Gallery 825是LAAA的展览空间。”
意义夸大:“stands as a testament to”“marks a pivotal moment in”“evolving landscape”“setting the stage for”删除,或替换为具体表述:“1989年成立,旨在独立发布区域统计数据。”
宣传语体:“nestled in the heart of”“vibrant”“breathtaking”“must-visit”“boasts a rich heritage”“renowned for”删除宣传话术:“Alamata是Gonder地区的一个城镇,以每周集市闻名。”
模糊归因:“Industry observers have noted”“Experts argue”“Critics have suggested”明确具体来源,或删除该表述。
大纲式“挑战与未来展望”章节替换为具体挑战及应对措施,或删除该章节。

Lever 5: Specificity insertion

杠杆5:具体性注入

Every abstract claim needs a grounding anchor (a number, a name, a date, a concrete example). "Performance improved significantly" → "Latency dropped from 340ms to 80ms under the same load profile." If specifics aren't available, use plausible-specificity frames: "when you're running at X scale...", "in the cases I've seen...", "the one time this bit us..."
每个抽象表述都需要一个锚点(数字、名称、日期、具体示例)。“性能显著提升”→“在相同负载下,延迟从340ms降至80ms。”若没有具体信息,使用合理的具体性框架:“当你运行到X规模时……”“在我见过的案例中……”“有一次这坑了我们……”

Lever 6: Voice and register

杠杆6:语气与语体

Human writing carries the writer's perspective:
  • First-person where natural ("I find that...", "In my experience...")
  • Occasional second-person direct address ("If you've ever debugged this...")
  • Mild rhetorical questions as transitions: "So why does this matter?"
  • Self-interruption mid-thought: "— actually, that's not quite right —", "more precisely:"
  • Contractions in conversational registers: "don't", "it's", "you'll"
人类写作带有作者的视角:
  • 自然使用第一人称(“我发现……”“根据我的经验……”)
  • 偶尔使用第二人称直接称呼(“如果你曾调试过这个……”)
  • 用温和的修辞问句作为过渡:“那么这为什么重要?”
  • 中途自我打断:“——实际上,这不太准确——”“更准确地说:”
  • 口语体中使用缩写:“don't”“it's”“you'll”

Lever 7: Discourse coherence (non-AI transitions)

杠杆7:语篇连贯性(非AI式过渡)

AI transitionHuman replacement
"Furthermore," / "Moreover,"Cut; let the next sentence follow, or "Also," if bridging is needed
"In addition to the above,""And"
"It is clear that"Delete; assert directly
"As previously mentioned,"Don't mention it again, or rephrase without the callback
"This highlights the importance of"Say what the importance IS: "Which means you need to..."
AI过渡词人类化替代方案
“Furthermore,” / “Moreover,”删除;让下一句自然承接,或必要时用“另外,”
“In addition to the above,”“而且”
“It is clear that”删除;直接断言
“As previously mentioned,”不要再提及,或重新表述而不使用回调
“This highlights the importance of”直接说明重要性:“这意味着你需要……”

Lever 8: Punctuation normalization

杠杆8:标点规范化

Em dashes (—). The most reliable single AI tell; AI uses them at 3–5× the human rate.
  • Maximum one per 300 words; under 300 words, zero (hard rule 1)
  • Only when a parenthetical genuinely interrupts rather than extends — not as a fancier comma
  • Most uses replace cleanly with a period, a comma, or cutting the aside
  • Never "X — like this — Y" (double em dash wrapping a clause); that pattern is almost exclusively AI
  • X — item, item, item
    (introducing a list) →
    X. Item, item, item.
    or a colon after a complete sentence
  • Three or more in one paragraph means structural problems; rewrite the paragraph
Semicolons (;). Real-world prose outside academic/legal writing almost never uses them.
  • Treat every semicolon as a bug unless the register is explicitly formal/academic
  • Replace with a period (usually), "and"/"but"/"so" (when the relationship matters), or restructure
  • Exception: lists whose items contain commas ("San Francisco, CA; Austin, TX")
Mid-sentence colons (:). Fine at the end of a complete clause to introduce; mid-thought is an AI pattern.
  • "The answer is: start earlier" → "Start earlier."
  • "The problem: nobody tests this" → "Nobody tests this." or "Here's the problem: nobody tests this."
  • One colon per paragraph maximum in non-list prose
Curly quotes. A near-certain single-character tell that survives rewriting.
  • Find-replace before shipping: curly
    → straight
    "
    , curly
    → straight
    '
    — apostrophes included
  • Exception: publishing contexts where typographic quotes are house style
破折号(—)。最可靠的单一AI识别特征;AI使用破折号的频率是人类的3-5倍。
  • 每300词最多使用1个;若不足300词,完全禁用(硬性规则1)
  • 仅当插入语确实是打断而非补充时使用——不要当作更花哨的逗号
  • 大多数情况下,破折号可直接替换为句号、逗号,或删除插入语
  • 绝不要使用“X — like this — Y”(双破折号包裹从句);这种模式几乎完全是AI生成的
  • X — item, item, item
    (引出列表)→
    X. Item, item, item.
    或在完整句子后使用冒号
  • 一个段落中出现3个或更多破折号意味着结构存在问题;需重写该段落
分号(;)。学术/法律写作之外的真实散文几乎从不使用分号。
  • 除非语体明确为正式/学术性,否则将每个分号视为错误
  • 替换为句号(通常)、“and”/“but”/“so”(当关系重要时),或重构句子
  • 例外:列表项本身包含逗号的情况(如“San Francisco, CA; Austin, TX”)
句中冒号(:)。在完整从句末尾引出内容是可行的;句中使用冒号是AI模式。
  • “The answer is: start earlier” → “早点开始。”
  • “The problem: nobody tests this” → “没人测试这个。” 或 “问题在于:没人测试这个。”
  • 非列表散文中,每个段落最多使用1个冒号
弯引号。这是一个几乎可以确定的单字符识别特征,即便改写也会保留。
  • 输出前查找替换:弯引号
    → 直引号
    "
    ,弯撇号
    → 直撇号
    '
    ——包括所有撇号
  • 例外:出版场景中,排版引号为官方格式要求

Lever 9: Strip RLHF / instruction-tuning voice

杠杆9:去除RLHF/指令调优语气

Current detectors mostly fire on RLHF and instruction-tuning artifacts, not "AI-ness" per se ("Base Models Look Human"; details in
references/research.md
). What gets flagged is the "helpful assistant" voice. This lever is the single most valuable one. Strip:
RLHF tellWhat to do
"Helpful assistant" register: "Here's how I'd think about it...", "Let me walk you through..."Cut the framing. Just say the thing.
Balanced tradeoff offering: "On one hand X, on the other Y, it depends..."Pick a side. The reader can disagree.
Structured enumeration of unrequested optionsAnswer. Acknowledge the constraint after if needed.
Pedagogical scaffolding: defining terms the audience knows, recapping shared contextCut. Trust the reader.
"Important caveats" appended to every claimMake the claim. Caveats only when the edge case is plausible.
Acknowledgment-prefix: "That's a great question, and..."Cut entirely.
Closing summary recapping what was just saidCut.
Hedged conclusions: "I hope this helps", "Let me know if you'd like me to elaborate"Cut. End on the last substantive sentence.
Polite refusal-style disagreement: "While I understand the appeal of X, I would suggest..."Just disagree: "X doesn't work because Y."
Symmetric framing of asymmetric tradeoffsState the asymmetry.
Knowledge-cutoff disclaimers: "As of my training cutoff...", "Based on what I know up to..."Cut. Say what you know, or "I don't know X".
Chat artifacts pasted into content: "Here is an overview of X", "Of course!", "Certainly!"Strip on sight. Published prose never carries them.
Sycophantic prefixes: "Great question!", "You're absolutely right!"Cut. Real engagement names the specific thing that was good.

当前检测工具主要针对RLHF和指令调优痕迹触发,而非所谓的“AI感”(参考《Base Models Look Human》;详情见
references/research.md
)。被标记的是“乐于助人的助手”语气。这个杠杆是最有价值的一个。需去除以下内容:
RLHF识别特征处理方式
“乐于助人的助手”语体:“Here's how I'd think about it...”“Let me walk you through...”删除框架。直接陈述内容。
平衡权衡表述:“一方面X,另一方面Y,取决于……”选择一个立场。读者可以不同意。
未经请求的结构化选项枚举直接回答。必要时在之后说明限制条件。
教学框架:定义受众已知的术语,重述共享上下文删除。信任读者。
每个表述后附加“重要警告”直接陈述表述。仅当边缘案例合理时添加警告。
致谢前缀:“That's a great question, and...”完全删除。
结尾总结重述刚说过的内容删除。
含糊的结论:“I hope this helps”“Let me know if you'd like me to elaborate”删除。以最后一个实质性句子结尾。
礼貌式拒绝异议:“While I understand the appeal of X, I would suggest...”直接异议:“X行不通,因为Y。”
不对称权衡的对称框架陈述不对称性。
知识截止日期声明:“As of my training cutoff...”“Based on what I know up to...”删除。直接陈述你知道的内容,或“我不知道X”。
粘贴到内容中的聊天痕迹:“Here is an overview of X”“Of course!”“Certainly!”立即删除。已发布的散文绝不会包含这些内容。
谄媚前缀:“Great question!”“You're absolutely right!”删除。真实的互动会指出具体的优点。

Advanced techniques (optional, when stakes are high)

进阶技巧(可选,高风险场景使用)

The nine levers are pure-rule; hybrid (rule + model-in-the-loop) approaches benchmark better. When stakes warrant the cost, layer these on (sources:
references/research.md
):
  1. Detector-scored best-of-N. Generate 3–5 variants; score each against a real detector (GPTZero, Pangram, Binoculars), a banned-word count, or a perplexity probe; ship the lowest.
  2. Iterative paraphrase pass. Run the output through a second LLM with "paraphrase this, keep the meaning." Diminishing returns past 2 passes; meaning drift accumulates — verify substance.
  3. Writer-profile distillation. When the user supplies writing samples, distill style hypotheses first (this is protocol step 0; follow the procedure there). Beats raw few-shot.
  4. Self-rewrite distance check. Ask a different LLM to "rewrite this in different words." Near-identical rewrite = text still at a local probability maximum = reads as AI.
  5. Embedding-guided synonym swap. When tooling is available, prefer substitutions that explicitly lower detector scores over Lever 1's static word list.
  6. Disfluency injection (casual register only). Light hesitations, mid-thought restarts, "wait actually" corrections. Off by default for formal writing; disfluencies in a board memo are their own tell.
Dead ends, don't bother: homoglyph injection (defeated by Unicode normalization, and a clear tampering signal), single cross-model rewrite (doesn't defeat trained detectors alone), watermark stripping (separate problem space).

九个杠杆是纯规则式方法;混合(规则+模型循环)方法的基准表现更好。当风险较高时,可叠加以下技巧(来源:
references/research.md
):
  1. 检测器评分的N选优:生成3-5个变体;用真实检测工具(GPTZero、Pangram、Binoculars)、禁用词计数或困惑度探针为每个变体评分;选择评分最低的版本。
  2. 迭代改写环节:将输出输入第二个LLM,提示“改写此文本,保留原意”。超过2次迭代后收益递减;会出现意义偏差——需验证内容准确性。
  3. 作者风格提取:当用户提供写作样本时,先提取风格假设(这是协议步骤0;遵循相关流程)。效果优于原始少样本学习。
  4. 自我改写距离检查:让另一个LLM“用不同的词改写此文本”。改写后几乎与原文相同=文本仍处于局部概率最大值=读起来像AI生成的。
  5. 嵌入引导的同义词替换:若有工具支持,优先选择能明确降低检测器评分的替换词,而非杠杆1中的静态词表。
  6. 不流畅注入(仅适用于口语体):轻微犹豫、中途重新开始、“wait actually”这类修正。正式写作默认禁用;董事会备忘录中的不流畅表述本身就是识别特征。
无效方法,无需尝试:同形异符注入(会被Unicode规范化破解,且是明显的篡改信号)、跨模型单次改写(无法单独规避训练有素的检测器)、水印去除(属于独立问题领域)。

Rewrite protocol

改写流程

When given text to humanize:
  1. (Optional) Writer-profile distillation. If the user provided prior writing samples, extract style hypotheses across six dimensions before touching the new text:
    1. Sentence length pattern. Variance? Signature fragments?
    2. Word choice level. Casual or academic? "stuff"/"thing" or "elements"/"components"?
    3. Paragraph openers. Straight in? Context first? A question? A scene?
    4. Punctuation habits. Em dashes? Parentheticals? Ellipses? Fragments?
    5. Recurring phrases / verbal tics. Repeated phrases? Fillers ("honestly", "basically", "look,")?
    6. Transition style. Explicit connectors, or next thought with no bridge?
    Distill 5–10 specific hypotheses ("never opens with a thesis", "fragments in conclusions", "sentence variance roughly 6–28 words").
    Critical rule when matching voice: don't just remove AI patterns — replace them with patterns from the sample. If the sample is casual, don't upgrade the vocabulary. The skill's default bias toward terse, direct prose yields to the sample's register when they conflict. Then apply the levers in service of those hypotheses.
  2. Read the full input first. Identify topic domain, audience, register, length target.
  3. Inventory the AI tells. Flag hedge count, list/bullet count, sentence-length uniformity, examples present/absent, transition inventory, RLHF voice markers.
    Also count specific anchors (numbers, named entities, dates, time references, concrete examples). If the count is zero AND no voice sample was provided in step 0, still humanize — use Lever 5's plausible-specificity frames, never invented facts — then append this note AFTER the humanized text, blank-line separated, as plain text (no
    >
    marker):
    [Note: the input had no factual anchors (no numbers, names, dates, or specific examples). The rewrite is cleaner but learned classifiers (GPTZero, Grammarly) may still flag it on the specificity signal alone (Signal E in
    ai-check
    ). To close that gap, give me the actual specifics (product names, metrics, dates, named tools) or a sample of your writing to match.]
    Do not stop and ask before rewriting. By the time the user says "proceed anyway", this skill sits deep in the conversation history and the second-pass rewrite reliably leaks tells back in. Rewrite now, flag the gap after.
  4. Rewrite in a single pass applying all nine levers. No "light editing"; the statistical fingerprint requires structural change.
    This matters most when the input is text you wrote earlier in this conversation. Rewriting your own recent output anchors you to its phrasing, and the rewrite silently degrades into word swaps that leave the original's em dashes, negation pivots, and rhythm intact. Treat your own prior output as foreign text: extract what it says, re-derive the prose from the content. If your edit log would read as a list of substitutions, you light-edited. Start over.
  5. Pre-output gate. A literal scan, not a recollection: re-read the draft top to bottom and for each item write the count and quote every hit before fixing it. Write zeros explicitly ("em dashes: 0"). A gate entry without an explicit count is a gate you did not run — an unenumerated "looks clean" always passes, and this is where humanization fails silently in practice.
    • Em dashes. Scan for "—", write the count. More than (word_count / 300)? Cut or replace with periods.
    • Semicolons. Scan for ";", write the count. Replace with a period or "and"/"but"/"so" unless a comma-containing list.
    • Curly quotes/apostrophes. Scan for the curly characters “ ” ‘ ’, write the count. Replace with straight " and '.
    • Banned vocabulary. Scan against the master list (end of this skill), quote each hit. Eyeball first: delve, leverage (verb), utilize, robust, comprehensive, furthermore, moreover, "it is important to note".
    • Comparative framing. Scan for "more ... than" and "feels like ... not", quote each match. Describe the thing directly.
    • Diminishment. Scan for "not just", "not X, it's", "not X but", quote each match. State what it IS.
    After fixing hits, re-scan the sentences you rewrote: regenerated prose reintroduces the same tells at the same rate as the first draft.
  6. Self-check. Step 4 cleared the mechanical tells; this covers the rest:
    • Sentence rhythm, from counts, not feel. Write out every sentence's word count in order ("9, 5, 22, 16, 7..."), then check the list against ALL four, fixing and recounting until every one passes (hard rule 7 covers the first two; conditions 1-2 apply only to outputs over ~80 words):
      1. Max minus min ≥ 20 (needs a ≤5-word fragment and a 25-plus-word sentence)
      2. Fewer than half the counts in the 10-to-20 band
      3. No three consecutive counts within 5 words of each other
      4. At least one count ≤ 6 per 150 words of output A mental read-through always sounds varied to the model that wrote it; the number list doesn't lie. Standard fixes: split one mid-length sentence into a fragment plus the remainder, and merge two mid-length neighbors into one long sentence that earns it.
    • Every paragraph has at least one specific anchor (number, name, example, time reference). On the step-2 zero-anchor path, Lever 5's plausible-specificity frames satisfy this item — never invent facts to pass it.
    • No bullet lists unless the user requested them
    • Voice consistent throughout (no third-person formal → first-person casual drift)
    • Every colon preceded by a complete sentence (exception: Slack/informal fragments)
    • Output shape (hard rule 6). No preamble, no trailing changelog. Only permitted additions: the meta-notes mandated by steps 2 and 5.6.
    • Rhetorical scaffolding: scan for the Signal I checklist patterns (step 5.5) — they survive the gate because they feel like good writing. Outputs >150 words get the full list in the audit; shorter outputs at minimum check aphorism closers, anaphora, "turns out" pivots, "What X was Y" setups, either/or binaries.
5.5. Audit pass. Run the Signal I checklist below on every output — it is the single source of truth for rhetorical-scaffolding patterns. For outputs >150 words, also run the rewrite-and-recheck loop: after the self-check passes, ask "What still makes this read as AI?", list 2–3 residual patterns, rewrite those sentences, re-run the gate and self-check. Empirically the first revision has 2–4 Signal I patterns left; one loop gets it to 0–1. Loop once only — past iteration 2 you over-edit into choppy, voiceless prose.
Flagged residuals must be removed, not justified. Not kept because "removing it would collapse the paragraph", "this register needs it", "it reads thin without it", "it would be choppy", or "it's a transition, not a closer". Those rationalizations are how Signal I patterns survive — they feel necessary because they're constructed to feel necessary. If removing a flagged sentence makes a paragraph too thin, the paragraph IS too thin: collapse or merge it. An honest 80-word output beats a padded 200-word output that reads as AI. Red flag: if your audit says "borderline but I'm keeping it because...", you just lost the loop. Cut it.
Signal I checklist (every audit, every paragraph). A general "what reads as AI?" prompt misses things; scan for each pattern and fix every hit:
  • Mini-aphorism closer. Paragraph ends in a 4-to-10-word punchy "lesson" ("That's the part that stuck.", "Slide decks don't.")? Cut it, or fold it into the previous sentence.
  • Thesis-first paragraph opener. Frame before experience ("The rollout was the hard part.", "The real question is Y.")? Start with the concrete thing; let the thesis emerge.
  • Parallel-subject mirror. Consecutive sentences with mirrored noun-phrase openers ("The code is one thing. Maintaining it is another.")? Vary one subject.
  • Aphoristic closing sentence on the whole piece. Last sentence reads as quotable standalone wisdom? End on a specific detail, an unresolved question, or a concrete next action.
  • Pattern announcement. Naming a pattern before describing it ("The pattern is X.")? Just describe it.
  • "Turns out" pivot. Reveal-narrative framing? State the discovery directly.
  • Setup sentences. "What I didn't expect was X."? Lead with X directly.
  • Within-sentence anaphoric parallel list. Four parallel question-word items ("what X, what Y, why Z, what W")? Use varied noun forms instead.
  • Composed self-aware parenthetical. Meta-commentary on your own state ("which I choose to read as X")? End on the concrete behavior.
  • Parallel reason chains. Three consecutive "subject + because/when + reason" sentences? Vary the clause structure: one "because", one bare assertion, one fragment.
  • Anaphora. Same opening word on 2+ consecutive sentences? Collapse or vary the opener.
  • Either/or binary. Clean two-option framing of a spectrum? Name the actual situation.
  • Balanced parenthetical pairs. Symmetric trade-offs in one sentence ("(X, but Y) or (A, but B)")? Real trade-offs are asymmetric; break the symmetry.
  • Tricolon. Three parallel beats with identical grammar, often conjunction-free and escalating in weight ("Two hours of X, six engineers doing Y, a postmortem where Z.")? Break the third item into its own sentence, join two with "and", or reduce to two.
  • Chiasmus / balanced opposition. Reversed-parallel construction that sounds like insight ("being specific about being wrong / being vague about being right")? Make the comparison asymmetric.
3+ hits means the patterns compound — address all of them. Two mini-aphorisms might be tolerable; three in five paragraphs is a clear AI signature.
5.6. Output-length sanity check. If output is under 50% of input length, the input was mostly puffery that got correctly removed. Don't pad it back up — padding reintroduces the stripped patterns. Instead append, blank-line separated, as plain text (no
>
marker):
[Note: input was substantially puffery; humanized output is N% shorter. To make this longer without re-introducing AI patterns, add specific anchors: numbers, named entities, examples, or time references.]
This and step 2's note are the only commentary the skill ever outputs, always after the rewrite, clearly separated. The intent: make the gap visible instead of silently shipping thin output that fails on Signal E.
  1. (Optional) Self-rewrite distance sanity check. When stakes are high, run Advanced 4.
  2. (Optional) Detector-scored best-of-N. When stakes are high, run Advanced 1.
  3. Output the rewritten text only. No preamble ("Here is the humanized version:"), no trailing changelog ("Main moves:", "What I changed:"). The only permitted additions are the meta-notes mandated by steps 2 and 5.6. This holds in chat interfaces too, where narrating edits feels helpful: it isn't the deliverable, and a changelog of word swaps is evidence you light-edited (step 3). If the user wants a side-by-side, they'll ask.

当需要人性化处理文本时:
  1. (可选)作者风格提取:若用户提供了之前的写作样本,在处理新文本前,从六个维度提取风格假设:
    1. 句子长度模式:是否有差异?是否有标志性片段?
    2. 词汇选择层级:口语化还是学术性?使用“stuff”/“thing”还是“elements”/“components”?
    3. 段落开头方式:直接切入?先铺垫背景?用问句?用场景?
    4. 标点习惯:是否使用破折号?插入语?省略号?片段句?
    5. 重复短语/语言习惯:是否有重复短语?填充词(如“honestly”“basically”“look,”)?
    6. 过渡风格:使用明确的连接词,还是直接承接下一个想法?
    提炼5-10个具体假设(如“从不以论点开头”“结尾使用片段句”“句子长度差异约为6-28词”)。
    匹配语气的关键规则:不要仅仅去除AI模式——用样本中的模式替换它们。若样本是口语化的,不要升级词汇。当本技能默认的简洁直接风格与样本语体冲突时,优先遵循样本语体。然后在这些假设的指导下应用杠杆。
  2. 先通读完整输入文本:确定主题领域、受众、语体、长度目标。
  3. 盘点AI识别特征:标记模糊表述数量、列表/项目符号数量、句子长度均匀性、是否有示例、过渡词清单、RLHF语气标记。
    同时统计具体锚点(数字、命名实体、日期、时间参考、具体示例)。若统计数量为且步骤0中未提供语气样本,仍需进行人性化处理——使用杠杆5的合理具体性框架,绝不要编造事实——然后在人性化处理后的文本之后,空一行添加以下注释,作为纯文本(不要使用
    >
    标记):
    [注:输入文本无事实锚点(无数字、名称、日期或具体示例)。改写后的文本更流畅,但机器学习分类器(如GPTZero、Grammarly)仍可能因具体性信号(
    ai-check
    中的Signal E)标记它。若要填补这一空白,请提供实际的具体信息(产品名称、指标、日期、命名工具)或你的写作样本以供匹配。]
    不要在改写前停下来询问用户。等用户说“继续”时,本技能已深处于对话历史中,二次改写会重新引入识别特征。现在就改写,之后标记空白。
  4. 单次改写,应用所有九个杠杆:不要“轻编辑”;统计特征需要结构性改变。
    当输入文本是你在本次对话中之前撰写的内容时,这一点尤为重要。改写你自己最近的输出会让你锚定其措辞,改写会悄然退化为仅替换词汇,而保留原文的破折号、否定转折和节奏。将你自己之前的输出视为外来文本:提取其内容,重新撰写散文。若你的编辑记录只是替换词汇列表,说明你进行了轻编辑。请重新开始。
  5. 输出前检查:逐字扫描,而非凭记忆:通读初稿,为每个条目写下计数并引用每个违规内容,然后修正。明确写下零计数(如“破折号:0”)。没有明确计数的检查等于未执行——未枚举的“看起来没问题”总会通过,而这正是人性化处理在实践中悄然失败的原因。
    • 破折号:扫描“—”,写下计数。超过(词数/300)?删除或替换为句号。
    • 分号:扫描“;”,写下计数。替换为句号或“and”/“but”/“so”,除非是包含逗号的列表。
    • 弯引号/弯撇号:扫描弯字符““””“‘’”,写下计数。替换为直引号"和直撇号'。
    • 禁用词汇:对照主列表(本技能末尾)扫描,引用每个违规内容。先目测高频词汇:delve、leverage(动词)、utilize、robust、comprehensive、furthermore、moreover、"it is important to note"。
    • 对比框架:扫描“more ... than”和“feels like ... not”,引用每个匹配项。直接描述事物。
    • 弱化表述:扫描“not just”“not X, it's”“not X but”,引用每个匹配项。直接陈述事物本身是什么。
    修正违规内容后,重新扫描你改写的句子:重新生成的散文会以与初稿相同的频率重新引入相同的识别特征。
  6. 自我检查:步骤4清除了机械性识别特征;本步骤覆盖其余内容:
    • 句子节奏,依据计数而非感觉:按顺序写下每个句子的词数(如“9, 5, 22, 16, 7...”),然后对照以下四个条件检查列表,修正并重新计数,直到全部满足(硬性规则7涵盖前两个;条件1-2仅适用于约80词以上的输出):
      1. 最大值减最小值≥20(需要一个≤5词的片段和一个25词以上的句子)
      2. 10-20词区间的计数占比不足一半
      3. 没有连续三个计数的差在5词以内
      4. 每150词输出至少有一个计数≤6 模型自己读起来总会觉得句子长度有变化;但数字不会说谎。标准修正方法:将一个中等长度的句子拆分为一个片段加剩余部分,或将两个中等长度的句子合并为一个合理的长句子。
    • 每个段落至少有一个具体锚点(数字、名称、示例、时间参考)。在步骤2的零锚点路径中,杠杆5的合理具体性框架满足此要求——绝不要编造事实来通过检查。
    • 除非用户要求,否则不要使用项目符号列表
    • 语气保持一致(不要从第三人称正式语体切换到第一人称口语体)
    • 每个冒号前都是完整句子(例外:Slack/非正式片段)
    • 输出形式(硬性规则6):无前置说明,无末尾变更日志。唯一允许添加的内容是步骤2和5.6要求的元注释。
    • 修辞框架:扫描Signal I检查清单中的模式(步骤5.5)——这些模式会在检查中幸存,因为它们看起来像好的写作。超过150词的输出需在审核中使用完整清单;较短的输出至少检查警句结尾、首语重复、“turns out”转折、“What X was Y”设定、非此即彼二元对立。
5.5. 审核环节:对所有输出运行下方的Signal I检查清单——这是修辞框架模式的唯一权威来源。对于超过150词的输出,还需运行改写-重新检查循环:自我检查通过后,询问*“还有什么让这篇文本读起来像AI生成的?”*,列出2-3个残留模式,改写这些句子,重新运行检查和自我检查。根据经验,第一次修订后会残留2-4个Signal I模式;一次循环可将其降至0-1个。仅循环一次——超过2次迭代会过度编辑,导致文本生硬、无语气。
标记的残留模式必须删除,不得合理化。不要因为“删除它会让段落崩溃”“这个语体需要它”“没有它读起来单薄”“会显得生硬”“这是过渡,不是结尾”而保留。这些合理化借口正是Signal I模式得以幸存的原因——它们看起来是必要的,因为它们被构建得看起来必要。若删除标记的句子会让段落过于单薄,说明段落本身就过于单薄:合并或精简它。一篇真实的80词输出优于一篇填充到200词但读起来像AI生成的输出。危险信号:若你的审核说*“borderline but I'm keeping it because...”*,说明你在循环中失败了。删除它。
Signal I检查清单(每次审核,每个段落)。通用的“什么让这篇文本读起来像AI?”提示会遗漏内容;需扫描每个模式并修正所有违规内容:
  • 微型警句结尾:段落以4-10词的有力“教训”结尾(如“这是最难忘的部分。”“幻灯片没用。”)?删除它,或融入前一个句子。
  • 先论点后内容的段落开头:先框架后经验(如“上线是最难的部分。”“真正的问题是Y。”)?从具体事物开始;让论点自然呈现。
  • 平行主语镜像:连续句子的名词短语开头镜像(如“代码是一回事。维护它是另一回事。”)?改变其中一个主语。
  • 整篇文章的警句结尾句:最后一句读起来像可引用的独立智慧?以具体细节、未解决的问题或具体的下一步行动结尾。
  • 模式宣告:先命名模式再描述(如“模式是X。”)?直接描述它。
  • “Turns out”转折:采用揭示叙事框架?直接陈述发现。
  • 铺垫句:“我没想到的是X。”?直接以X开头。
  • 句内首语重复平行列表:四个平行疑问词项(如“what X, what Y, why Z, what W”)?改用不同的名词形式。
  • 刻意的自我意识插入语:关于自身状态的元评论(如“我选择将其解读为X”)?以具体行为结尾。
  • 平行理由链:连续三个“主语+because/when+理由”句子?改变从句结构:一个用“because”,一个直接断言,一个用片段句。
  • 首语重复:连续2个或以上句子开头词相同?合并或改变开头。
  • 非此即彼二元对立:将连续谱清晰划分为两个选项?陈述实际情况。
  • 平衡插入语对:一个句子中的对称权衡(如“(X,但Y)或(A,但B)”)?真实的权衡是不对称的;打破对称性。
  • 三排比:三个语法相同的平行节拍,通常无连词且权重递增(如“两个小时的X,六个工程师做Y,一次复盘会Z。”)?将第三个分句拆分为独立句子,用“and”连接两个,或减少为两个。
  • 交错配列/平衡对立:反向平行结构,听起来像见解(如“具体承认错误 / 模糊承认正确”)?制造不对称对比。
3个或以上违规意味着模式叠加——需全部处理。两个微型警句可能尚可容忍;五个段落中有三个则是明显的AI特征。
5.6. 输出长度合理性检查:若输出长度不足输入的50%,说明输入大部分是冗余内容,已被正确删除。不要重新填充——填充会重新引入已去除的模式。相反,空一行添加以下注释,作为纯文本(不要使用
>
标记):
[注:输入文本包含大量冗余内容;人性化处理后的输出比输入短N%。若要在不重新引入AI模式的情况下增加长度,请添加具体锚点:数字、命名实体、示例或时间参考。]
此注释和步骤2的注释是本技能唯一允许输出的评论,始终附加在改写文本之后,清晰分隔。目的:让空白可见,而非默默输出因Signal E而失败的单薄文本。
  1. (可选)自我改写距离合理性检查:高风险场景下,运行进阶技巧4。
  2. (可选)检测器评分的N选优:高风险场景下,运行进阶技巧1。
  3. 仅输出改写后的文本:无前置说明(如“以下是人性化处理后的版本:”),无末尾变更日志(如“主要修改点:”“我做了这些改动:”)。唯一允许添加的内容是步骤2和5.6要求的元注释。这在聊天界面中也同样适用,虽然叙述编辑内容看起来有帮助,但这不是交付物,词汇替换的变更日志是你进行轻编辑的证据(步骤3)。若用户需要并排对比,他们会主动询问。

Generating human text from scratch

从头生成人类风格文本

When writing new content (not rewriting):
  • Apply all nine levers from the first sentence
  • Start mid-thought when it fits: "The tricky part about X isn't what most people think."
  • No "In this [article/post/section], I will..." opener
  • End without a summary paragraph unless the piece is long enough to genuinely need a re-anchor
  • Calibrate voice to the domain: an engineer's Slack post sounds different from a board memo
Decoding-strategy note (when controlling generation): set temperature high (0.9–1.1), top-p loose (0.95–0.99), repetition penalty up (1.1–1.2). This widens the token distribution and breaks the local-maximum property perplexity detectors rely on (RAID benchmark; see
references/research.md
).

当撰写新内容(而非改写)时:
  • 从第一句开始应用所有九个杠杆
  • 适合时中途切入:“X的棘手之处并非大多数人所想的那样。”
  • 不要使用“In this [article/post/section], I will...”这类开头
  • 除非文章足够长确实需要重新锚定,否则不要添加总结段落
  • 根据领域调整语气:工程师的Slack帖子与董事会备忘录的语气不同
解码策略说明(控制生成时):设置较高的temperature(0.9–1.1),宽松的top-p(0.95–0.99),提高重复惩罚(1.1–1.2)。这会扩大token分布范围,打破困惑度检测工具依赖的局部最大值属性(参考RAID基准测试;见
references/research.md
)。

Domain-specific calibration

领域特定校准

Technical (engineering, code, systems)

技术领域(工程、代码、系统)

  • Domain-native vocabulary: "the hot path", "this falls apart at scale", "the footgun here is..."
  • Short sentences for definitive claims: "This is O(n²). Don't do it at scale."
  • Tradeoffs direct, not diplomatic: "The downside is real: you lose..."
  • Real tool names, version numbers, error messages when available
  • 领域原生词汇:“the hot path”“this falls apart at scale”“the footgun here is...”
  • 简短句子用于明确断言:“This is O(n²). Don't do it at scale.”
  • 直接陈述权衡,不要委婉:“缺点很明显:你会失去……”
  • 尽可能使用真实工具名称、版本号、错误信息

Narrative / blog / essay

叙事/博客/散文

  • Open with a scene or specific moment, not a thesis
  • Let the argument emerge from the evidence
  • Sentence fragments deliberately for rhythm. Like this.
  • One moment of genuine uncertainty or changed mind per 500 words
  • 以场景或具体时刻开头,而非论点
  • 让论点从证据中自然呈现
  • 刻意使用句子片段来营造节奏。就像这样。
  • 每500词加入一个真实的不确定性或改变想法的时刻

Creative / lyrical prose (fiction, poetic passages, mood pieces)

创意/抒情散文(小说、诗歌段落、氛围作品)

The register where this skill gets rationalized away. Every hard rule still applies; detectors don't grade on artistic merit. The traps:
  • The "literary em dash" exemption. "Something wouldn't let you stay asleep — a feeling too new to have a name yet." Feels earned; still the single strongest tell. Use a period, a comma, or restructure.
  • Poetic negation pivots. "It isn't proof you failed — it's proof you showed up." Say the positive thing: "It's proof you showed up."
  • Anaphora as lyricism. "Not every door has been tried. Not every version of yourself." Reads as AI cadence, not poetry. Vary the second sentence.
  • Mid-sentence colon reveals. "and you think: this is where it starts" — restructure or end the sentence before the colon.
  • Balanced imagery pairs and escalating tricolons. Human lyrical prose is lopsided; AI lyrical prose is symmetric. Break the symmetry.
Human creative writing gets its texture from specificity and asymmetry (a named street, a wrong note, an image that doesn't resolve), not punctuation drama.
这是本技能最容易被合理化的语体。所有硬性规则仍然适用;检测工具不会根据艺术价值评分。常见陷阱:
  • “文学破折号”豁免:“有什么东西不让你入睡——一种太新而没有名字的感觉。”看起来合理;但这仍是最强烈的识别特征。使用句号、逗号,或重构句子。
  • 诗意否定转折:“这不是你失败的证明——而是你曾尝试的证明。”直接说积极的内容:“这是你曾尝试的证明。”
  • 首语重复作为抒情手法:“不是每扇门都被试过。不是每个版本的你。”读起来像AI节奏,而非诗歌。改变第二个句子。
  • 句中冒号揭示:“你想:这就是开始的地方”——重构句子,或在冒号前结束。
  • 平衡意象对和递增三排比:人类抒情散文是不平衡的;AI抒情散文是对称的。打破对称性。
人类创意写作的质感来自具体性和不对称性(一个命名的街道,一个错误的音符,一个未解决的意象),而非标点戏剧效果。

Professional / business

专业/商务领域

  • Cut the throat-clearing opener; just the message
  • State the ask in sentence 1 or 2
  • Numbers and deadlines: "by Thursday EOD", "the three blockers are..."
  • Short paragraphs (2–3 sentences max in email/memo)
  • 删除开场白;直接传递信息
  • 在第1或第2句中说明请求
  • 使用数字和截止日期:“周四下班前”“三个障碍是……”
  • 简短段落(邮件/备忘录中最多2-3句)

Slack / async team updates

Slack/异步团队更新

Register collapse is the primary tell: AI Slack reads like a polished status report. Real Slack has:
  • Abbreviations and approximations:
    ~60%
    ,
    <10min
    ,
    fwiw
    ,
    btw
    ,
    lmk
    ,
    tmrw
  • Fragments throughout: "hard commits: billing gRPC + pprof thing", not full clauses
  • Self-corrections mid-message: "oh also —", "wait, actually"
  • Thoughts bleeding together, looping back, trailing off — not topic-per-paragraph
  • Numerals with approximation markers:
    ~3-4 days
    , not "approximately three to four days"
  • Single-word endings: "lmk" as its own line
  • Lowercase throughout except proper nouns
  • The structure must actually break: accomplishment → caveat → next steps is still AI even with
    fwiw
    sprinkled in. Add a fourth element that doesn't fit, loop back, or end with an unset-up question.

语体崩溃是主要识别特征:AI生成的Slack内容读起来像 polished 的状态报告。真实的Slack内容具有以下特征:
  • 缩写和近似表述:
    ~60%
    <10min
    fwiw
    btw
    lmk
    tmrw
  • 全程使用片段句:“硬提交:计费gRPC + pprof相关内容”,而非完整从句
  • 中途自我修正:“哦还有——”“等等,实际上”
  • 想法混杂,循环往复,虎头蛇尾——而非一段一个主题
  • 带近似标记的数字:
    ~3-4 days
    ,而非“大约三到四天”
  • 单字结尾:“lmk”单独成一行
  • 除专有名词外全部小写
  • 结构必须真正打破:成就→警告→下一步计划仍然是AI风格,即便添加了
    fwiw
    。添加一个不符合结构的第四元素,循环回到之前的内容,或用一个未铺垫的问句结尾。

What this skill does NOT do

本技能不做的事

  • Guarantee 0% AI scores on commercial detectors (no method does reliably)
  • Add false information to increase specificity — plausible framing only
  • Change the factual content of the input, only the expression
  • Apply the same transformation to every domain — register matters

  • 保证在商业检测工具上获得0%的AI评分(没有方法能可靠做到这一点)
  • 添加虚假信息以提高具体性——仅使用合理框架
  • 改变输入文本的事实内容,仅改变表达方式
  • 对所有领域应用相同的转换——语体很重要

Reference: banned word/phrase list (compile-time errors in AI text)

参考:禁用词汇/短语列表(AI文本中的编译时错误)

Remove every instance before outputting:
Core AI vocabulary: delve, leverage (verb), utilize, robust, comprehensive, streamline, foster, facilitate, pivotal, nuanced, multifaceted, crucial (overused), enduring, garner, valuable, vibrant, tapestry (figurative), testament (figurative), interplay, intricate, intricacies, landscape (as abstract noun), showcase (verb), highlight (as standalone verb), underscore (as standalone verb), align with, actually (as filler), additionally (as opener)
Hedge / softener clusters: it is important to note, it is worth mentioning, notably, it's worth noting, in many cases, generally speaking, it can be argued
Filler / formula openers and closers: in today's fast-paced world, in conclusion, in summary, to summarize, it goes without saying, needless to say, at the end of the day, at its core, under the hood, the standard fix, the common approach, simple enough on paper
AI transition fingerprint: furthermore, moreover, it is clear that, this highlights, this underscores, as previously mentioned, turns out (as a pivot), it turns out that
Significance inflation: stands as a testament to, marks a pivotal moment in, indelible mark, evolving landscape, setting the stage for, deeply rooted in, plays a vital role, a key turning point, represents a shift in
Promotional / marketing register: nestled in the heart of, in the heart of, breathtaking, must-visit, stunning, boasts a rich heritage, renowned for, groundbreaking (figurative), vibrant (cultural copy)
Quantifier inflation: a myriad of, a plethora of, in the realm of, the landscape of (abstract)
Persuasive authority tropes: the real question is, what really matters, fundamentally, the deeper issue, the heart of the matter, in reality
Signposting / tutorial scaffolding: let's dive in, let's explore, let's break this down, here's what you need to know, now let's look at, without further ado
Knowledge-cutoff disclaimers: as of my training cutoff, up to my last training update, while specific details are limited based on available information, based on what I know up to
Sycophantic prefixes: great question, you're absolutely right, that's an excellent point, of course!, certainly!
Templated email / Slack closers: happy to jump on a call, let me know if you have any questions, feel free to reach out, i hope this helps, looking forward to connecting soon
Binary framing: whether X or Y (as a clean binary framing opener)
输出前需删除所有以下内容:
核心AI词汇: delve, leverage(动词), utilize, robust, comprehensive, streamline, foster, facilitate, pivotal, nuanced, multifaceted, crucial(过度使用), enduring, garner, valuable, vibrant, tapestry(比喻义), testament(比喻义), interplay, intricate, intricacies, landscape(抽象名词), showcase(动词), highlight(独立动词), underscore(独立动词), align with, actually(填充词), additionally(开头词)
模糊表述/弱化语集群: it is important to note, it is worth mentioning, notably, it's worth noting, in many cases, generally speaking, it can be argued
填充/公式化开头和结尾: in today's fast-paced world, in conclusion, in summary, to summarize, it goes without saying, needless to say, at the end of the day, at its core, under the hood, the standard fix, the common approach, simple enough on paper
AI过渡特征: furthermore, moreover, it is clear that, this highlights, this underscores, as previously mentioned, turns out(转折词), it turns out that
意义夸大: stands as a testament to, marks a pivotal moment in, indelible mark, evolving landscape, setting the stage for, deeply rooted in, plays a vital role, a key turning point, represents a shift in
促销/营销语体: nestled in the heart of, in the heart of, breathtaking, must-visit, stunning, boasts a rich heritage, renowned for, groundbreaking(比喻义), vibrant(文化文案)
量词夸大: a myriad of, a plethora of, in the realm of, the landscape of(抽象)
说服性权威套路: the real question is, what really matters, fundamentally, the deeper issue, the heart of the matter, in reality
路标/教程框架: let's dive in, let's explore, let's break this down, here's what you need to know, now let's look at, without further ado
知识截止日期声明: as of my training cutoff, up to my last training update, while specific details are limited based on available information, based on what I know up to
谄媚前缀: great question, you're absolutely right, that's an excellent point, of course!, certainly!
模板化邮件/Slack结尾: happy to jump on a call, let me know if you have any questions, feel free to reach out, i hope this helps, looking forward to connecting soon
二元框架: whether X or Y(作为清晰的二元框架开头)",