| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1238 | | 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) | |
| 35.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1238 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "treacherous" | | 1 | "maw" | | 2 | "warmth" | | 3 | "familiar" | | 4 | "vibrated" | | 5 | "standard" | | 6 | "sense of" | | 7 | "could feel" | | 8 | "glint" | | 9 | "silence" | | 10 | "pulse" | | 11 | "weight" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "hung in the air" | | count | 2 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 150 | | matches | (empty) | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 2 | | narrationSentences | 150 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 150 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1238 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1238 | | uniqueNames | 16 | | maxNameDensity | 0.48 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | London | 3 | | Camden | 2 | | Harlow | 6 | | Quinn | 2 | | Soho | 1 | | Morris | 4 | | Veil | 4 | | Market | 3 | | Herrera | 1 | | Tube | 1 | | Taser | 1 | | Saint | 1 | | Christopher | 1 | | Spanish-inflected | 1 | | Tomás | 5 | | You | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Veil" | | 4 | "Market" | | 5 | "Herrera" | | 6 | "Taser" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Tomás" | | 10 | "You" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 38.45% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.616 | | wordCount | 1238 | | matches | | 0 | "neither she nor" | | 1 | "Not from the chase, but from something deeper" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 150 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 30.2 | | std | 25.02 | | cv | 0.829 | | sampleLengths | | 0 | 96 | | 1 | 94 | | 2 | 58 | | 3 | 62 | | 4 | 65 | | 5 | 6 | | 6 | 50 | | 7 | 52 | | 8 | 26 | | 9 | 18 | | 10 | 73 | | 11 | 10 | | 12 | 12 | | 13 | 54 | | 14 | 47 | | 15 | 42 | | 16 | 1 | | 17 | 32 | | 18 | 31 | | 19 | 28 | | 20 | 28 | | 21 | 34 | | 22 | 4 | | 23 | 5 | | 24 | 57 | | 25 | 16 | | 26 | 6 | | 27 | 36 | | 28 | 5 | | 29 | 6 | | 30 | 5 | | 31 | 6 | | 32 | 7 | | 33 | 16 | | 34 | 29 | | 35 | 52 | | 36 | 15 | | 37 | 2 | | 38 | 27 | | 39 | 9 | | 40 | 16 |
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| 95.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 150 | | matches | | 0 | "been condemned" | | 1 | "was hidden" | | 2 | "was scarred" | | 3 | "was supposed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 221 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 150 | | ratio | 0.013 | | matches | | 0 | "The rain did not fall on London; it hammered." | | 1 | "The market was not empty; it was hidden behind the senses of the normal world." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1239 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.020177562550443905 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007263922518159807 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 150 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 150 | | mean | 8.25 | | std | 5.77 | | cv | 0.699 | | sampleLengths | | 0 | 9 | | 1 | 20 | | 2 | 23 | | 3 | 26 | | 4 | 18 | | 5 | 6 | | 6 | 18 | | 7 | 4 | | 8 | 16 | | 9 | 15 | | 10 | 10 | | 11 | 15 | | 12 | 10 | | 13 | 5 | | 14 | 25 | | 15 | 2 | | 16 | 14 | | 17 | 12 | | 18 | 4 | | 19 | 25 | | 20 | 17 | | 21 | 7 | | 22 | 6 | | 23 | 3 | | 24 | 6 | | 25 | 3 | | 26 | 15 | | 27 | 7 | | 28 | 21 | | 29 | 5 | | 30 | 4 | | 31 | 2 | | 32 | 2 | | 33 | 3 | | 34 | 3 | | 35 | 14 | | 36 | 5 | | 37 | 3 | | 38 | 9 | | 39 | 6 | | 40 | 8 | | 41 | 5 | | 42 | 5 | | 43 | 21 | | 44 | 13 | | 45 | 13 | | 46 | 7 | | 47 | 19 | | 48 | 3 | | 49 | 4 |
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| 28.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 32 | | diversityRatio | 0.22 | | totalSentences | 150 | | uniqueOpeners | 33 | |
| 23.81% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 140 | | matches | | | ratio | 0.007 | |
| 22.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 69 | | totalSentences | 140 | | matches | | 0 | "It came down in sheets" | | 1 | "Her boots skidded on the" | | 2 | "It was about the shadow" | | 3 | "She glanced at her left" | | 4 | "It was 11:43 PM." | | 5 | "Her breath came in white" | | 6 | "He had whispered about the" | | 7 | "She was here now, chasing" | | 8 | "He was moving too clean" | | 9 | "She turned the corner." | | 10 | "It had been condemned for" | | 11 | "It was ozone, copper, and" | | 12 | "Her ears popped." | | 13 | "He stopped at the stairwell" | | 14 | "He looked back." | | 15 | "He did not smile, but" | | 16 | "He did not have to" | | 17 | "She looked down at it." | | 18 | "He had left it." | | 19 | "It had moved locations every" |
| | ratio | 0.493 | |
| 13.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 125 | | totalSentences | 140 | | matches | | 0 | "The rain did not fall" | | 1 | "It came down in sheets" | | 2 | "Detective Harlow Quinn did not" | | 3 | "Her boots skidded on the" | | 4 | "It was about the shadow" | | 5 | "She glanced at her left" | | 6 | "The worn leather watch on" | | 7 | "It was 11:43 PM." | | 8 | "Her breath came in white" | | 9 | "Morris hadn't bled out so" | | 10 | "He had whispered about the" | | 11 | "She was here now, chasing" | | 12 | "The target was a man." | | 13 | "A former paramedic who lost" | | 14 | "He was moving too clean" | | 15 | "She turned the corner." | | 16 | "The target was at the" | | 17 | "It had been condemned for" | | 18 | "It was ozone, copper, and" | | 19 | "The air pressure dropped around" |
| | ratio | 0.893 | |
| 71.43% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 140 | | matches | | 0 | "If she took it, she" | | 1 | "If she left it, she" |
| | ratio | 0.014 | |
| 82.07% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 4 | | matches | | 0 | "It came down in sheets that masked the city, turning the cobblestones of Camden into black mirrors, slick and treacherous." | | 1 | "Tall, olive skin hidden under a soaked trench coat, moving with a grace that did not belong on a civilian walking away from a scene." | | 2 | "A former paramedic who lost his license treating things that could not be treated." | | 3 | "He wore a Saint Christopher medallion which caught the low light, a small glint against the dark fabric of his coat." |
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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 | |