| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 100 | | tagDensity | 0.11 | | leniency | 0.22 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1810 | | 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) | |
| 91.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1810 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "weight" | | 1 | "velvet" | | 2 | "trembled" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 150 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 150 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 239 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1810 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 82 | | wordCount | 1233 | | uniqueNames | 15 | | maxNameDensity | 3 | | worstName | "Quinn" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 37 | | Camden | 1 | | Town | 1 | | Underground | 1 | | Bell | 7 | | Kowalski | 1 | | Bovril | 1 | | Eva | 20 | | Malcolm | 1 | | Vane | 3 | | Veil | 1 | | Compass | 1 | | Detective | 2 | | Blood | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Bell" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Malcolm" | | 6 | "Vane" | | 7 | "Blood" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Underground" | | 3 | "Detective" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1810 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 239 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 157 | | mean | 11.53 | | std | 12.67 | | cv | 1.099 | | sampleLengths | | 0 | 37 | | 1 | 53 | | 2 | 5 | | 3 | 24 | | 4 | 4 | | 5 | 12 | | 6 | 15 | | 7 | 9 | | 8 | 2 | | 9 | 11 | | 10 | 2 | | 11 | 5 | | 12 | 45 | | 13 | 1 | | 14 | 4 | | 15 | 5 | | 16 | 1 | | 17 | 5 | | 18 | 1 | | 19 | 16 | | 20 | 39 | | 21 | 6 | | 22 | 23 | | 23 | 5 | | 24 | 3 | | 25 | 5 | | 26 | 42 | | 27 | 2 | | 28 | 3 | | 29 | 13 | | 30 | 4 | | 31 | 4 | | 32 | 14 | | 33 | 10 | | 34 | 11 | | 35 | 54 | | 36 | 13 | | 37 | 5 | | 38 | 4 | | 39 | 29 | | 40 | 3 | | 41 | 6 | | 42 | 6 | | 43 | 34 | | 44 | 17 | | 45 | 6 | | 46 | 33 | | 47 | 5 | | 48 | 46 | | 49 | 1 |
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| 91.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 150 | | matches | | 0 | "been sealed" | | 1 | "being dismissed" | | 2 | "been removed" | | 3 | "were shuttered" | | 4 | "been painted" | | 5 | "been smeared" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 215 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 239 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1236 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.013754045307443365 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0024271844660194173 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 239 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 239 | | mean | 7.57 | | std | 5.74 | | cv | 0.759 | | sampleLengths | | 0 | 24 | | 1 | 13 | | 2 | 10 | | 3 | 23 | | 4 | 20 | | 5 | 4 | | 6 | 1 | | 7 | 15 | | 8 | 9 | | 9 | 4 | | 10 | 12 | | 11 | 9 | | 12 | 6 | | 13 | 9 | | 14 | 2 | | 15 | 3 | | 16 | 8 | | 17 | 2 | | 18 | 5 | | 19 | 22 | | 20 | 23 | | 21 | 1 | | 22 | 4 | | 23 | 5 | | 24 | 1 | | 25 | 5 | | 26 | 1 | | 27 | 16 | | 28 | 7 | | 29 | 32 | | 30 | 6 | | 31 | 23 | | 32 | 5 | | 33 | 3 | | 34 | 5 | | 35 | 14 | | 36 | 8 | | 37 | 20 | | 38 | 2 | | 39 | 3 | | 40 | 13 | | 41 | 4 | | 42 | 4 | | 43 | 9 | | 44 | 5 | | 45 | 7 | | 46 | 3 | | 47 | 11 | | 48 | 12 | | 49 | 4 |
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| 44.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.2911392405063291 | | totalSentences | 237 | | uniqueOpeners | 69 | |
| 25.64% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 130 | | matches | | 0 | "Then she noticed the man’s" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 130 | | matches | | 0 | "It struck the platform edge," | | 1 | "His hand hovered near the" | | 2 | "He glanced down the track" | | 3 | "He pointed towards the street" | | 4 | "He held out a white" | | 5 | "Her curly red hair had" | | 6 | "His coat had been expensive" | | 7 | "His other hand remained open," | | 8 | "Her freckled face held the" | | 9 | "It stopped in a smooth" | | 10 | "She stepped around the salt" | | 11 | "Their signs had been painted" | | 12 | "Her skin had the waxy" | | 13 | "He moved towards the tunnel" | | 14 | "It pointed at Malcolm Vane’s" | | 15 | "It was silver, thick, engraved" | | 16 | "She pinched his knuckles and" | | 17 | "She pulled a pair of" | | 18 | "Her leather watchband pressed against" | | 19 | "It pointed straight at Detective" |
| | ratio | 0.154 | |
| 44.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 130 | | matches | | 0 | "Detective Harlow Quinn stood beneath" | | 1 | "It struck the platform edge," | | 2 | "The station had been sealed" | | 3 | "Someone had dragged a modern" | | 4 | "Quinn checked her watch." | | 5 | "A uniformed constable waited beside" | | 6 | "His hand hovered near the" | | 7 | "He glanced down the track" | | 8 | "Quinn lifted the police tape" | | 9 | "The constable swallowed." | | 10 | "The young man’s cheeks coloured." | | 11 | "He pointed towards the street" | | 12 | "Bell took a breath through" | | 13 | "He held out a white" | | 14 | "Something small rested in its" | | 15 | "Quinn did not touch it." | | 16 | "Bell pointed along the platform." | | 17 | "Eva Kowalski crouched near the" | | 18 | "Books crowded the cracked concrete" | | 19 | "Her curly red hair had" |
| | ratio | 0.831 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 130 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 3 | | matches | | 0 | "Something small rested in its centre: a finger bone, polished smooth and carved with a black mark that resembled an eye stitched shut." | | 1 | "Her freckled face held the pinched, stubborn expression of somebody who had spent years being dismissed by people with badges." | | 2 | "A clean horizontal line, as if the head had been removed with a single stroke from something impossibly sharp." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 100 | | tagDensity | 0.08 | | leniency | 0.16 | | rawRatio | 0.125 | | effectiveRatio | 0.02 | |