| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.64% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1488 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 59.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1488 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "measured" | | 1 | "pumping" | | 2 | "familiar" | | 3 | "weight" | | 4 | "etched" | | 5 | "trembled" | | 6 | "warmth" | | 7 | "firmly" | | 8 | "standard" | | 9 | "electric" |
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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 | 152 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1488 | | ratio | 0 | | matches | (empty) | |
| 25.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 2 | | matches | | 0 | "Broken junk, Thorne said, stepping closer." | | 1 | "This is not a street robbery, Quinn said quietly." |
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| 86.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1488 | | uniqueNames | 13 | | maxNameDensity | 1.28 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 2 | | Quinn | 19 | | Ford | 1 | | Met | 1 | | Victorian | 1 | | Aris | 1 | | Thorne | 10 | | Earth | 1 | | Morris | 1 | | Veil | 4 | | Market | 4 | | You | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Met" | | 3 | "Victorian" | | 4 | "Thorne" | | 5 | "Morris" | | 6 | "Market" | | 7 | "You" |
| | places | (empty) | | globalScore | 0.862 | | windowScore | 1 | |
| 77.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like the flesh had been sealed shu" | | 1 | "Something like that, Thorne supplied, tappin" | | 2 | "as if gripping something that was no longer there" |
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| 65.59% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.344 | | wordCount | 1488 | | matches | | 0 | "neither fraying nor" | | 1 | "not as a guarantee of safety, but as a promise to herself" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 152 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 87.53 | | std | 56.64 | | cv | 0.647 | | sampleLengths | | 0 | 159 | | 1 | 102 | | 2 | 89 | | 3 | 140 | | 4 | 28 | | 5 | 100 | | 6 | 90 | | 7 | 20 | | 8 | 131 | | 9 | 11 | | 10 | 21 | | 11 | 92 | | 12 | 40 | | 13 | 52 | | 14 | 174 | | 15 | 39 | | 16 | 200 |
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| 82.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 10 | | totalSentences | 152 | | matches | | 0 | "been carried" | | 1 | "been shot" | | 2 | "was confined" | | 3 | "been sealed" | | 4 | "was etched" | | 5 | "been found" | | 6 | "was cauterized" | | 7 | "got killed" | | 8 | "been touched" | | 9 | "was paid" |
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| 21.20% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 261 | | matches | | 0 | "was fencing" | | 1 | "was waiting" | | 2 | "wasn’t carrying" | | 3 | "was carrying" | | 4 | "was hunting" | | 5 | "was already moving" | | 6 | "were aligning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 152 | | ratio | 0.007 | | matches | | 0 | "The accelerant traces, the missing bag, the gunshot—" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1492 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.030160857908847184 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.011394101876675604 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 9.79 | | std | 6.07 | | cv | 0.62 | | sampleLengths | | 0 | 14 | | 1 | 20 | | 2 | 22 | | 3 | 14 | | 4 | 19 | | 5 | 10 | | 6 | 10 | | 7 | 20 | | 8 | 5 | | 9 | 25 | | 10 | 19 | | 11 | 15 | | 12 | 17 | | 13 | 1 | | 14 | 13 | | 15 | 14 | | 16 | 5 | | 17 | 5 | | 18 | 3 | | 19 | 4 | | 20 | 2 | | 21 | 4 | | 22 | 7 | | 23 | 11 | | 24 | 16 | | 25 | 15 | | 26 | 18 | | 27 | 10 | | 28 | 7 | | 29 | 5 | | 30 | 3 | | 31 | 3 | | 32 | 11 | | 33 | 4 | | 34 | 7 | | 35 | 16 | | 36 | 12 | | 37 | 22 | | 38 | 13 | | 39 | 14 | | 40 | 9 | | 41 | 8 | | 42 | 3 | | 43 | 2 | | 44 | 13 | | 45 | 11 | | 46 | 5 | | 47 | 2 | | 48 | 3 | | 49 | 7 |
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| 33.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 27 | | diversityRatio | 0.3092105263157895 | | totalSentences | 152 | | uniqueOpeners | 47 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 146 | | matches | | 0 | "Just a rusted service hatch" | | 1 | "Officially, it was municipal neglect." | | 2 | "Unofficially, the air hanging over" | | 3 | "Instead, the staining was confined" | | 4 | "Probably a high-yield incendiary round." | | 5 | "Probably belonged to his knapsack." |
| | ratio | 0.041 | |
| 83.01% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 146 | | matches | | 0 | "She approached the cordon with" | | 1 | "He was a good man," | | 2 | "He straightened as she passed," | | 3 | "You’re just in time to" | | 4 | "She crossed the threshold, her" | | 5 | "She did not reach for" | | 6 | "She looked at the floor." | | 7 | "He had simply stopped." | | 8 | "She noted the lack of" | | 9 | "She leaned closer, ignoring the" | | 10 | "It looked like the flesh" | | 11 | "We found trace accelerant on" | | 12 | "It blistered fabric." | | 13 | "It left a sour, chemical" | | 14 | "She reached out, fingers hovering" | | 15 | "She followed the line of" | | 16 | "She picked it up with" | | 17 | "It was a compass, but" | | 18 | "It trembled, then settled, locking" | | 19 | "It angled toward a collapsed" |
| | ratio | 0.342 | |
| 55.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 118 | | totalSentences | 146 | | matches | | 0 | "The rain had turned the" | | 1 | "Detective Harlow Quinn stepped out" | | 2 | "The worn leather strap of" | | 3 | "She approached the cordon with" | | 4 | "The crime scene was not" | | 5 | "DS Aris Thorne leaned against" | | 6 | "He was a good man," | | 7 | "He straightened as she passed," | | 8 | "You’re just in time to" | | 9 | "The kid in the yellow" | | 10 | "Looks like a botched deal." | | 11 | "Quinn ignored the dismissal in" | | 12 | "She crossed the threshold, her" | | 13 | "The chamber was a relic" | | 14 | "The body lay near a" | | 15 | "A heavy wool coat, three" | | 16 | "Quinn dropped to one knee," | | 17 | "She did not reach for" | | 18 | "She looked at the floor." | | 19 | "The victim had not been" |
| | ratio | 0.808 | |
| 34.25% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 146 | | matches | | | ratio | 0.007 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 4 | | matches | | 0 | "It angled toward a collapsed section of the ceiling, toward a rusted maintenance ladder that descended into absolute darkness." | | 1 | "I’m saving you from going down a rabbit hole that ate your last partner." | | 2 | "The thermal seal on the victim’s chest meant he had been touched by something that operated outside standard thermodynamics." | | 3 | "Thorne opened his mouth to protest, but Quinn was already moving, her boots striking the brick with that same relentless, military cadence." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |