| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.396 | | leniency | 0.792 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.91% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1486 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "completely" | | 1 | "very" | | 2 | "slowly" |
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| 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) | |
| 79.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1486 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "efficient" | | 1 | "footsteps" | | 2 | "echoing" | | 3 | "stomach" | | 4 | "methodical" | | 5 | "calculating" |
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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 | 0 | | narrationSentences | 110 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 7 | | totalWords | 1483 | | ratio | 0.005 | | matches | | 0 | "Found your compass. We can discuss terms." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1061 | | uniqueNames | 14 | | maxNameDensity | 1.98 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Chapter | 1 | | Three | 1 | | Wrong | 1 | | Compass | 1 | | Quinn | 21 | | Morris | 13 | | Tube | 2 | | Camden | 2 | | Holbrook | 3 | | Underground | 1 | | Blitz | 1 | | One | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Camden" | | 3 | "Holbrook" | | 4 | "Blitz" | | 5 | "One" | | 6 | "Market" |
| | places | | | globalScore | 0.51 | | windowScore | 0.5 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 4 | | matches | | 0 | "smelled like rust and wet concrete, with a" | | 1 | "as though reaching for something" | | 2 | "looked like an old encyclopedia" | | 3 | "as though waiting" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1483 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 138 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 26.96 | | std | 21.59 | | cv | 0.801 | | sampleLengths | | 0 | 6 | | 1 | 39 | | 2 | 46 | | 3 | 22 | | 4 | 27 | | 5 | 72 | | 6 | 8 | | 7 | 68 | | 8 | 6 | | 9 | 10 | | 10 | 39 | | 11 | 8 | | 12 | 30 | | 13 | 77 | | 14 | 57 | | 15 | 6 | | 16 | 22 | | 17 | 6 | | 18 | 42 | | 19 | 7 | | 20 | 5 | | 21 | 11 | | 22 | 79 | | 23 | 8 | | 24 | 7 | | 25 | 8 | | 26 | 37 | | 27 | 58 | | 28 | 9 | | 29 | 13 | | 30 | 46 | | 31 | 24 | | 32 | 45 | | 33 | 11 | | 34 | 6 | | 35 | 54 | | 36 | 3 | | 37 | 39 | | 38 | 5 | | 39 | 66 | | 40 | 7 | | 41 | 66 | | 42 | 26 | | 43 | 16 | | 44 | 4 | | 45 | 14 | | 46 | 32 | | 47 | 24 | | 48 | 29 | | 49 | 19 |
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| 98.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 110 | | matches | | 0 | "been walked" | | 1 | "been blocked" |
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| 53.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 182 | | matches | | 0 | "was thinking" | | 1 | "was thinking" | | 2 | "was thinking" | | 3 | "wasn't coming" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 138 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1062 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.03672316384180791 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.013182674199623353 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 10.75 | | std | 7.25 | | cv | 0.675 | | sampleLengths | | 0 | 15 | | 1 | 25 | | 2 | 5 | | 3 | 22 | | 4 | 19 | | 5 | 1 | | 6 | 4 | | 7 | 22 | | 8 | 19 | | 9 | 8 | | 10 | 18 | | 11 | 17 | | 12 | 15 | | 13 | 7 | | 14 | 15 | | 15 | 8 | | 16 | 13 | | 17 | 12 | | 18 | 13 | | 19 | 9 | | 20 | 21 | | 21 | 6 | | 22 | 2 | | 23 | 8 | | 24 | 13 | | 25 | 12 | | 26 | 14 | | 27 | 5 | | 28 | 3 | | 29 | 23 | | 30 | 7 | | 31 | 20 | | 32 | 11 | | 33 | 11 | | 34 | 16 | | 35 | 19 | | 36 | 6 | | 37 | 14 | | 38 | 8 | | 39 | 8 | | 40 | 21 | | 41 | 3 | | 42 | 3 | | 43 | 22 | | 44 | 3 | | 45 | 3 | | 46 | 16 | | 47 | 11 | | 48 | 8 | | 49 | 7 |
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| 74.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4927536231884058 | | totalSentences | 138 | | uniqueOpeners | 68 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 97 | | matches | | 0 | "She didn't bother with pleasantries." | | 1 | "He looked up as Quinn" | | 2 | "She'd never been able to" | | 3 | "She studied Holbrook's remains without" | | 4 | "His right arm extended upward," | | 5 | "She gestured to the wall" | | 6 | "They passed through the platform" | | 7 | "Her stomach tightened." | | 8 | "She'd seen symbols like that" | | 9 | "She'd found them in Morris's" | | 10 | "She'd buried them so deep" | | 11 | "She'd handle those later, properly." | | 12 | "She continued through the station," | | 13 | "She stood in the exact" | | 14 | "She didn't look up" | | 15 | "She turned to face Morris" | | 16 | "She was thinking about Morris," | | 17 | "She was thinking about symbols" | | 18 | "She was thinking about the" | | 19 | "She emerged from the station" |
| | ratio | 0.258 | |
| 47.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 97 | | matches | | 0 | "# Chapter Three: The Wrong" | | 1 | "Detective Quinn noticed it the" | | 2 | "She didn't bother with pleasantries." | | 3 | "Constable Morris, no relation to" | | 4 | "He looked up as Quinn" | | 5 | "Fear made officers careful." | | 6 | "Quinn knelt beside the outline," | | 7 | "Morris cleared his throat" | | 8 | "The abandoned Tube station beneath" | | 9 | "Something Quinn had learned to" | | 10 | "That electrical charge in the" | | 11 | "She'd never been able to" | | 12 | "She studied Holbrook's remains without" | | 13 | "The body lay diagonal across" | | 14 | "His right arm extended upward," | | 15 | "The outline suggested he'd been" | | 16 | "Quinn stood, brushing dust from" | | 17 | "She gestured to the wall" | | 18 | "The young officer's jaw tightened." | | 19 | "Quinn pulled on latex gloves" |
| | ratio | 0.825 | |
| 51.55% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 97 | | matches | | 0 | "Whoever had carved that symbol" |
| | ratio | 0.01 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "That electrical charge in the air that meant the world had bent itself somewhere nearby." | | 1 | "She was thinking about symbols that shouldn't exist and markets that moved every full moon, about compasses that pointed toward things that shouldn't be pointab…" | | 2 | "The driver's eyes met hers in the rear-view mirror, and for just a moment, they reflected light that wasn't coming from any streetlamp." |
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| 98.68% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn stood, her sharp jaw catching the overhead light" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 48 | | tagDensity | 0.063 | | leniency | 0.125 | | rawRatio | 0.333 | | effectiveRatio | 0.042 | |