| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva said softly [softly]" | | 1 | "Quinn crouched again [again]" |
| | dialogueSentences | 58 | | tagDensity | 0.379 | | leniency | 0.759 | | rawRatio | 0.091 | | effectiveRatio | 0.069 | |
| 86.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1808 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | adverb | "reluctantly" | | count | 1 |
| | 2 | | | 3 | | | 4 | |
| | highlights | | 0 | "softly" | | 1 | "reluctantly" | | 2 | "carefully" | | 3 | "gently" | | 4 | "really" |
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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) | |
| 77.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1808 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "velvet" | | 1 | "etched" | | 2 | "silence" | | 3 | "pulse" | | 4 | "familiar" | | 5 | "trembled" |
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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 | 125 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 125 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 160 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1808 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1280 | | uniqueNames | 15 | | maxNameDensity | 1.95 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Quinn | 25 | | Camden | 2 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Eva | 20 | | Vale | 5 | | Metropolitan | 1 | | Police | 1 | | Morris | 2 | | Rigor | 1 | | Three | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Kowalski" | | 3 | "Museum" | | 4 | "Eva" | | 5 | "Vale" | | 6 | "Police" | | 7 | "Morris" | | 8 | "Rigor" |
| | places | | | globalScore | 0.523 | | windowScore | 0.5 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 2 | | matches | | 0 | "as if listening to a sound Quinn could not hear" | | 1 | "smelled like Eva’s satchel, trying to find" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.553 | | wordCount | 1808 | | matches | | 0 | "not at the chalk circle or the wall behind the desk, but at a rusted ventilation grille set low in the paneling to Qu" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 160 | | matches | | 0 | "seen that crescent" | | 1 | "suspecting that something" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 26.99 | | std | 24.19 | | cv | 0.896 | | sampleLengths | | 0 | 67 | | 1 | 81 | | 2 | 76 | | 3 | 62 | | 4 | 4 | | 5 | 21 | | 6 | 16 | | 7 | 65 | | 8 | 3 | | 9 | 52 | | 10 | 3 | | 11 | 51 | | 12 | 5 | | 13 | 5 | | 14 | 40 | | 15 | 58 | | 16 | 31 | | 17 | 5 | | 18 | 39 | | 19 | 3 | | 20 | 6 | | 21 | 65 | | 22 | 23 | | 23 | 40 | | 24 | 55 | | 25 | 4 | | 26 | 5 | | 27 | 4 | | 28 | 5 | | 29 | 9 | | 30 | 3 | | 31 | 42 | | 32 | 14 | | 33 | 56 | | 34 | 11 | | 35 | 1 | | 36 | 31 | | 37 | 4 | | 38 | 8 | | 39 | 7 | | 40 | 33 | | 41 | 46 | | 42 | 16 | | 43 | 19 | | 44 | 6 | | 45 | 49 | | 46 | 3 | | 47 | 98 | | 48 | 3 | | 49 | 13 |
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| 68.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 13 | | totalSentences | 125 | | matches | | 0 | "was tipped" | | 1 | "was curled" | | 2 | "been etched" | | 3 | "been chipped" | | 4 | "been disturbed" | | 5 | "been wiped" | | 6 | "been redrawn" | | 7 | "been carved" | | 8 | "been scratched" | | 9 | "been etched" | | 10 | "was mistaken" | | 11 | "were curled" | | 12 | "was smeared" | | 13 | "was given" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 209 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 160 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1286 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.03265940902021773 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006220839813374806 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 160 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 160 | | mean | 11.3 | | std | 8.64 | | cv | 0.765 | | sampleLengths | | 0 | 23 | | 1 | 22 | | 2 | 22 | | 3 | 21 | | 4 | 12 | | 5 | 4 | | 6 | 28 | | 7 | 16 | | 8 | 23 | | 9 | 12 | | 10 | 9 | | 11 | 6 | | 12 | 26 | | 13 | 19 | | 14 | 12 | | 15 | 26 | | 16 | 5 | | 17 | 4 | | 18 | 11 | | 19 | 10 | | 20 | 7 | | 21 | 9 | | 22 | 4 | | 23 | 17 | | 24 | 13 | | 25 | 15 | | 26 | 7 | | 27 | 9 | | 28 | 3 | | 29 | 39 | | 30 | 13 | | 31 | 3 | | 32 | 38 | | 33 | 13 | | 34 | 5 | | 35 | 5 | | 36 | 13 | | 37 | 3 | | 38 | 24 | | 39 | 5 | | 40 | 16 | | 41 | 20 | | 42 | 17 | | 43 | 5 | | 44 | 3 | | 45 | 18 | | 46 | 5 | | 47 | 5 | | 48 | 16 | | 49 | 23 |
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| 55.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.375 | | totalSentences | 160 | | uniqueOpeners | 60 | |
| 29.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 112 | | matches | | | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 112 | | matches | | 0 | "She touched the bone token" | | 1 | "She moved through the crowd" | | 2 | "Her sharp jaw was set," | | 3 | "Her worn leather watch ticked" | | 4 | "She tucked a strand behind" | | 5 | "Her freckled face held the" | | 6 | "Her satchel bulged with books." | | 7 | "His head was tipped back" | | 8 | "His lips were bluish, but" | | 9 | "Her knees protested." | | 10 | "She leaned closer to Vale’s" | | 11 | "Its screws were old, painted" | | 12 | "She stood and crossed to" | | 13 | "It gave with a reluctant" | | 14 | "She leaned closer." | | 15 | "Her pulse changed pace." | | 16 | "She tilted her head." | | 17 | "She studied the circle." | | 18 | "Her breath caught." | | 19 | "She had seen that crescent" |
| | ratio | 0.25 | |
| 58.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 112 | | matches | | 0 | "Harlow Quinn had learned to" | | 1 | "The feeling waited for her" | | 2 | "She touched the bone token" | | 3 | "The Veil Market had folded" | | 4 | "Lanterns without flames burned in" | | 5 | "The tiled walls sweated." | | 6 | "Stalls crowded the platform beneath" | | 7 | "Quinn caught the scent of" | | 8 | "She moved through the crowd" | | 9 | "Her sharp jaw was set," | | 10 | "Her worn leather watch ticked" | | 11 | "That was why she had" | | 12 | "Eva Kowalski stood near the" | | 13 | "She tucked a strand behind" | | 14 | "Her freckled face held the" | | 15 | "Her satchel bulged with books." | | 16 | "Quinn kept her voice flat" | | 17 | "Eva flinched but did not" | | 18 | "Quinn looked past her." | | 19 | "A man, middle-aged, wearing a" |
| | ratio | 0.804 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 112 | | matches | (empty) | | ratio | 0 | |
| 51.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 6 | | matches | | 0 | "Harlow Quinn had learned to trust the wrongness that gathered at the base of her skull before her eyes had evidence for it." | | 1 | "Her freckled face held the pallor of someone who spent too many nights in the British Museum’s restricted archives and too little time under honest sun." | | 2 | "Dust lay thick on the floor beneath it, a fine black grit that gathered in the station’s corners like soot." | | 3 | "But the dust had been disturbed in a broad smear, as if something heavy had brushed across it and then been wiped back." | | 4 | "Three years filing reports that no one read and visiting archives that smelled like Eva’s satchel, trying to find a word for the thing that had taken her partne…" | | 5 | "Of the official report that had used words like misadventure and unexplained because no one had wanted to look closer." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, not unkindly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 58 | | tagDensity | 0.19 | | leniency | 0.379 | | rawRatio | 0 | | effectiveRatio | 0 | |