| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1194 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 66.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1194 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "familiar" | | 1 | "vibrated" | | 2 | "constructed" | | 3 | "standard" | | 4 | "echoed" | | 5 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 120 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 5 | | totalWords | 1183 | | ratio | 0.004 | | matches | | 0 | "Entry by Bone Token Only." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1099 | | uniqueNames | 10 | | maxNameDensity | 1.55 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Quinn | 17 | | Morris | 1 | | Soho | 1 | | Camden | 1 | | Tube | 2 | | Veil | 1 | | Market | 1 | | Bone | 1 | | Token | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Market" |
| | places | | | globalScore | 0.727 | | windowScore | 0.667 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 2 | | matches | | 0 | "as if conspiring to hide the track" | | 1 | "sounded like gravel grinding together" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.845 | | wordCount | 1183 | | matches | | 0 | "not of her, but of the place behind him" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 26.29 | | std | 19.18 | | cv | 0.73 | | sampleLengths | | 0 | 39 | | 1 | 64 | | 2 | 79 | | 3 | 3 | | 4 | 7 | | 5 | 65 | | 6 | 5 | | 7 | 50 | | 8 | 32 | | 9 | 30 | | 10 | 17 | | 11 | 45 | | 12 | 11 | | 13 | 48 | | 14 | 16 | | 15 | 34 | | 16 | 46 | | 17 | 25 | | 18 | 41 | | 19 | 11 | | 20 | 7 | | 21 | 36 | | 22 | 11 | | 23 | 17 | | 24 | 17 | | 25 | 47 | | 26 | 23 | | 27 | 22 | | 28 | 24 | | 29 | 13 | | 30 | 22 | | 31 | 54 | | 32 | 8 | | 33 | 6 | | 34 | 12 | | 35 | 51 | | 36 | 32 | | 37 | 3 | | 38 | 17 | | 39 | 7 | | 40 | 5 | | 41 | 25 | | 42 | 45 | | 43 | 7 | | 44 | 4 |
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| 96.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 120 | | matches | | 0 | "were gone" | | 1 | "was hooded" | | 2 | "was surrounded" |
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| 98.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 197 | | matches | | 0 | "were moving" | | 1 | "was leading" | | 2 | "was dissolving" |
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| 29.48% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 5 | | totalSentences | 126 | | ratio | 0.04 | | matches | | 0 | "Ten yards ahead, the runner—a lanky man in a hoodie that looked two sizes too big—skidded on the slick cobblestones." | | 1 | "The street-level noise—the distant sirens, the hum of traffic—died instantly." | | 2 | "The low hum never stopped, but the chatter of the few patrons—some humanoid, some shapes that hurt Quinn’s eyes to look at—faded away." | | 3 | "They didn't walk; they flowed." | | 4 | "The military precision she usually relied on—clear the room, control the threat, neutralize the target—was dissolving in the face of this impossible geometry." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1110 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.014414414414414415 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0045045045045045045 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 9.39 | | std | 6.02 | | cv | 0.641 | | sampleLengths | | 0 | 22 | | 1 | 7 | | 2 | 10 | | 3 | 15 | | 4 | 10 | | 5 | 20 | | 6 | 19 | | 7 | 4 | | 8 | 13 | | 9 | 14 | | 10 | 31 | | 11 | 5 | | 12 | 12 | | 13 | 3 | | 14 | 4 | | 15 | 3 | | 16 | 12 | | 17 | 16 | | 18 | 17 | | 19 | 20 | | 20 | 5 | | 21 | 15 | | 22 | 16 | | 23 | 4 | | 24 | 15 | | 25 | 14 | | 26 | 5 | | 27 | 13 | | 28 | 15 | | 29 | 9 | | 30 | 6 | | 31 | 3 | | 32 | 2 | | 33 | 2 | | 34 | 10 | | 35 | 8 | | 36 | 3 | | 37 | 19 | | 38 | 15 | | 39 | 11 | | 40 | 14 | | 41 | 7 | | 42 | 11 | | 43 | 8 | | 44 | 8 | | 45 | 3 | | 46 | 13 | | 47 | 12 | | 48 | 22 | | 49 | 9 |
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| 39.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.30158730158730157 | | totalSentences | 126 | | uniqueOpeners | 38 | |
| 29.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 114 | | matches | | 0 | "Then the merchant tilted his" |
| | ratio | 0.009 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 114 | | matches | | 0 | "Her blood was up, hot" | | 1 | "She lunged over a discarded" | | 2 | "He corrected his balance with" | | 3 | "She didn't need to look" | | 4 | "Her lungs burned, a familiar" | | 5 | "She pushed the thought down." | | 6 | "She didn't need the ghost" | | 7 | "They never did." | | 8 | "They were moving away from" | | 9 | "He was leading her somewhere." | | 10 | "She descended, keeping her tread" | | 11 | "It was wrong." | | 12 | "Her heart hammered against her" | | 13 | "She looked at the runner." | | 14 | "He reached into his pocket" | | 15 | "He slapped it onto the" | | 16 | "He pointed a long finger" | | 17 | "He looked at her, then" | | 18 | "He pulled his hood back." | | 19 | "His face was gaunt, eyes" |
| | ratio | 0.333 | |
| 8.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 103 | | totalSentences | 114 | | matches | | 0 | "Harlow Quinn didn't feel the" | | 1 | "Her blood was up, hot" | | 2 | "She lunged over a discarded" | | 3 | "Water sheeted up her trousers," | | 4 | "He corrected his balance with" | | 5 | "Quinn checked her watch." | | 6 | "The leather strap was slick" | | 7 | "She didn't need to look" | | 8 | "Her lungs burned, a familiar" | | 9 | "She pushed the thought down." | | 10 | "She didn't need the ghost" | | 11 | "The words meant nothing." | | 12 | "They never did." | | 13 | "The runner vaulted a rusted" | | 14 | "Quinn followed, her shoulder catching" | | 15 | "They were moving away from" | | 16 | "The city noise faded here," | | 17 | "He was leading her somewhere." | | 18 | "The streets grew narrower, the" | | 19 | "The usual urban grime of" |
| | ratio | 0.904 | |
| 43.86% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 114 | | matches | | 0 | "If she stayed, she was" |
| | ratio | 0.009 | |
| 68.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 5 | | matches | | 0 | "Ten yards ahead, the runner—a lanky man in a hoodie that looked two sizes too big—skidded on the slick cobblestones." | | 1 | "Quinn followed, her shoulder catching the metal with a dull clang that vibrated through her bones." | | 2 | "The streets grew narrower, the buildings leaning in as if conspiring to hide the track." | | 3 | "The atmosphere pressed in on her, vibrating with a low hum that set her teeth on edge." | | 4 | "He looked at the detective standing in the center of the forbidden market, rain dripping from her salt-and-pepper hair, her stance rigid, her jaw set in a sharp…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "the runner whispered (whisper)" | | 1 | "the merchant hissed (hiss)" |
| | dialogueSentences | 14 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.333 | | effectiveRatio | 0.286 | |