| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 2 | | adverbTags | | 0 | "The correction came quickly [quickly]" | | 1 | "Quinn said quietly [quietly]" |
| | dialogueSentences | 48 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.111 | | effectiveRatio | 0.083 | |
| 78.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1868 | | totalAiIsmAdverbs | 8 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | adverb | "barely above a whisper" | | count | 1 |
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| | highlights | | 0 | "slightly" | | 1 | "quickly" | | 2 | "lazily" | | 3 | "slowly" | | 4 | "barely above a whisper" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 73.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1868 | | totalAiIsms | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "practiced ease" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | |
| | highlights | | 0 | "weight" | | 1 | "echoing" | | 2 | "methodical" | | 3 | "traced" | | 4 | "measured" | | 5 | "practiced ease" | | 6 | "crystal" | | 7 | "shattered" | | 8 | "whisper" | | 9 | "pulsed" |
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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 | 107 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1893 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1163 | | uniqueNames | 11 | | maxNameDensity | 2.15 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 25 | | Kowalski | 15 | | Chen | 4 | | London | 1 | | Underground | 1 | | City | 1 | | Oxfords | 1 | | Patek | 1 | | Philippe | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Chen" | | 4 | "Underground" | | 5 | "Patek" | | 6 | "Philippe" |
| | places | | | globalScore | 0.425 | | windowScore | 0.167 | |
| 48.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 3 | | matches | | 0 | "shadows that seemed to press back, resisting the intrusion" | | 1 | "looked like a recent attempt to tame them" | | 2 | "looked like a strong wind could carry her" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.528 | | wordCount | 1893 | | matches | | 0 | "not from impact, but from within" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 38.63 | | std | 25.79 | | cv | 0.668 | | sampleLengths | | 0 | 25 | | 1 | 76 | | 2 | 61 | | 3 | 4 | | 4 | 81 | | 5 | 28 | | 6 | 26 | | 7 | 24 | | 8 | 38 | | 9 | 81 | | 10 | 56 | | 11 | 92 | | 12 | 39 | | 13 | 7 | | 14 | 48 | | 15 | 25 | | 16 | 35 | | 17 | 15 | | 18 | 70 | | 19 | 14 | | 20 | 36 | | 21 | 27 | | 22 | 76 | | 23 | 6 | | 24 | 40 | | 25 | 75 | | 26 | 10 | | 27 | 49 | | 28 | 23 | | 29 | 49 | | 30 | 43 | | 31 | 8 | | 32 | 70 | | 33 | 31 | | 34 | 9 | | 35 | 36 | | 36 | 44 | | 37 | 83 | | 38 | 6 | | 39 | 72 | | 40 | 63 | | 41 | 9 | | 42 | 4 | | 43 | 60 | | 44 | 32 | | 45 | 60 | | 46 | 18 | | 47 | 6 | | 48 | 3 |
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| 85.59% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 107 | | matches | | 0 | "been rigged" | | 1 | "been closed" | | 2 | "been forced" | | 3 | "been broken" | | 4 | "been trained" | | 5 | "been pushed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 197 | | matches | | 0 | "were looking" | | 1 | "was only beginning" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 134 | | ratio | 0.052 | | matches | | 0 | "She paused at the top of the escalator — frozen, rusted, its teeth visible through gaps in the metal — and let her eyes adjust to the darkness below." | | 1 | "She tucked a stubborn curl back behind her left ear — a gesture Quinn had already seen her make twice in thirty seconds." | | 2 | "His skin had a faint grey pallor that went beyond mere death, and when she leaned close, she could see — barely — a tracery of dark lines beneath the skin, like ink spreading through water." | | 3 | "Its face wasn't marked with cardinal directions but with strange symbols — sigils, Chen had called them — and its needle, when they'd found it, had been spinning lazily, not settling on north or any other fixed point." | | 4 | "The needle — dark, almost black — swung in a wide arc, then seemed to hesitate." | | 5 | "She'd seen death before — more than most." | | 6 | "The face was cracked — not from impact, but from within." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1032 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.031007751937984496 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.009689922480620155 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 14.13 | | std | 11.06 | | cv | 0.783 | | sampleLengths | | 0 | 11 | | 1 | 14 | | 2 | 24 | | 3 | 29 | | 4 | 23 | | 5 | 6 | | 6 | 7 | | 7 | 19 | | 8 | 29 | | 9 | 4 | | 10 | 9 | | 11 | 22 | | 12 | 27 | | 13 | 23 | | 14 | 8 | | 15 | 20 | | 16 | 5 | | 17 | 14 | | 18 | 7 | | 19 | 8 | | 20 | 16 | | 21 | 33 | | 22 | 5 | | 23 | 14 | | 24 | 15 | | 25 | 13 | | 26 | 6 | | 27 | 18 | | 28 | 7 | | 29 | 8 | | 30 | 12 | | 31 | 16 | | 32 | 19 | | 33 | 3 | | 34 | 2 | | 35 | 4 | | 36 | 14 | | 37 | 16 | | 38 | 4 | | 39 | 9 | | 40 | 36 | | 41 | 13 | | 42 | 11 | | 43 | 28 | | 44 | 5 | | 45 | 2 | | 46 | 13 | | 47 | 35 | | 48 | 18 | | 49 | 7 |
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| 58.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.39552238805970147 | | totalSentences | 134 | | uniqueOpeners | 53 | |
| 35.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 95 | | matches | | 0 | "Then she tucking her hair" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 95 | | matches | | 0 | "She paused at the top" | | 1 | "She didn't blame them." | | 2 | "She tucked a stubborn curl" | | 3 | "She moved past Kowalski toward" | | 4 | "She noted that, filed it" | | 5 | "His eyes were open, fixed" | | 6 | "She tilted her head, studying" | | 7 | "His skin had a faint" | | 8 | "They traced from his fingertips" | | 9 | "She stood, her eyes moving" | | 10 | "She needed a clear picture" | | 11 | "She pointed to the man's" | | 12 | "She'd heard enough of Chen's" | | 13 | "Its face wasn't marked with" | | 14 | "She produced an evidence bag" | | 15 | "She turned it slowly." | | 16 | "It pointed toward the eastbound" | | 17 | "She'd read Chen's preliminary notes." | | 18 | "She'd seen death before —" | | 19 | "She was right." |
| | ratio | 0.242 | |
| 49.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 95 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "Something that had been waiting" | | 2 | "Detective Harlow Quinn ducked beneath" | | 3 | "She paused at the top" | | 4 | "Emergency lights had been rigged" | | 5 | "The uniforms had done their" | | 6 | "A forensic tech in a" | | 7 | "She didn't blame them." | | 8 | "A voice from behind a" | | 9 | "A woman stepped into view," | | 10 | "She tucked a stubborn curl" | | 11 | "the woman said, extending her" | | 12 | "Quinn took the hand briefly." | | 13 | "The correction came quickly, almost" | | 14 | "Quinn's voice was flat, matter-of-fact" | | 15 | "She moved past Kowalski toward" | | 16 | "The station had been closed" | | 17 | "The heavy iron door at" | | 18 | "The lock had been broken" | | 19 | "She noted that, filed it" |
| | ratio | 0.821 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 8 | | matches | | 0 | "Something that had been waiting in the dark long before the trains stopped running." | | 1 | "Emergency lights had been rigged along the platform, their harsh white glare cutting through shadows that seemed to press back, resisting the intrusion." | | 2 | "Two constables stood at the far end, their faces pale, their postures stiff in a way that told her they didn't want to be here any longer than necessary." | | 3 | "His skin had a faint grey pallor that went beyond mere death, and when she leaned close, she could see — barely — a tracery of dark lines beneath the skin, like…" | | 4 | "Kowalski opened her satchel, rummaging through it with the practiced ease of someone who knew exactly where everything was despite the chaos." | | 5 | "The verdigris on the casing was uneven, concentrated around the sigils as if the markings themselves had oxidised from within." | | 6 | "The younger woman's green eyes were wide behind her round glasses, and the nervous energy that had surrounded her since Quinn's arrival had stilled into somethi…" | | 7 | "She was someone who understood what they were looking at in a way that Quinn was only beginning to grasp." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn said, more to herself than to Kowalski" | | 1 | "Kowalski's voice had, measured" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |