| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1077 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | | 2 | | adverb | "deliberately" | | count | 1 |
| | 3 | |
| | highlights | | 0 | "slowly" | | 1 | "very" | | 2 | "deliberately" | | 3 | "slightly" |
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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) | |
| 86.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1077 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "warmth" | | 1 | "footsteps" | | 2 | "whisper" |
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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 | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1068 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 812 | | uniqueNames | 8 | | maxNameDensity | 0.74 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 6 | | Crane | 3 | | Inverness | 1 | | Street | 1 | | Camden | 1 | | Kowalski | 3 | | Eva | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Crane" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | | 0 | "Inverness" | | 1 | "Street" | | 2 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 1 | | matches | | 0 | "something like it before" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1068 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 21.8 | | std | 23.67 | | cv | 1.086 | | sampleLengths | | 0 | 12 | | 1 | 75 | | 2 | 38 | | 3 | 1 | | 4 | 3 | | 5 | 2 | | 6 | 88 | | 7 | 92 | | 8 | 8 | | 9 | 5 | | 10 | 31 | | 11 | 20 | | 12 | 4 | | 13 | 3 | | 14 | 46 | | 15 | 3 | | 16 | 7 | | 17 | 55 | | 18 | 32 | | 19 | 1 | | 20 | 12 | | 21 | 3 | | 22 | 10 | | 23 | 20 | | 24 | 1 | | 25 | 32 | | 26 | 15 | | 27 | 18 | | 28 | 6 | | 29 | 33 | | 30 | 31 | | 31 | 8 | | 32 | 16 | | 33 | 86 | | 34 | 7 | | 35 | 16 | | 36 | 8 | | 37 | 6 | | 38 | 4 | | 39 | 23 | | 40 | 6 | | 41 | 63 | | 42 | 18 | | 43 | 12 | | 44 | 17 | | 45 | 15 | | 46 | 2 | | 47 | 36 | | 48 | 18 |
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| 77.92% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 77 | | matches | | 0 | "been folded" | | 1 | "been sedated" | | 2 | "was papered" | | 3 | "was clipped" | | 4 | "been pulled" | | 5 | "been leached" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 116 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 105 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 817 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.03549571603427173 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.011015911872705019 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 10.17 | | std | 8.46 | | cv | 0.831 | | sampleLengths | | 0 | 12 | | 1 | 31 | | 2 | 8 | | 3 | 1 | | 4 | 1 | | 5 | 6 | | 6 | 24 | | 7 | 4 | | 8 | 13 | | 9 | 17 | | 10 | 8 | | 11 | 1 | | 12 | 3 | | 13 | 2 | | 14 | 22 | | 15 | 5 | | 16 | 13 | | 17 | 20 | | 18 | 28 | | 19 | 30 | | 20 | 4 | | 21 | 16 | | 22 | 22 | | 23 | 20 | | 24 | 2 | | 25 | 6 | | 26 | 5 | | 27 | 16 | | 28 | 15 | | 29 | 20 | | 30 | 4 | | 31 | 3 | | 32 | 6 | | 33 | 23 | | 34 | 4 | | 35 | 4 | | 36 | 9 | | 37 | 3 | | 38 | 7 | | 39 | 9 | | 40 | 7 | | 41 | 19 | | 42 | 1 | | 43 | 1 | | 44 | 7 | | 45 | 11 | | 46 | 21 | | 47 | 6 | | 48 | 5 | | 49 | 1 |
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| 86.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5619047619047619 | | totalSentences | 105 | | uniqueOpeners | 59 | |
| 52.08% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 64 | | matches | | 0 | "Too clean for an alley" |
| | ratio | 0.016 | |
| 70.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 64 | | matches | | 0 | "She studied the man inside" | | 1 | "His legs were bent at" | | 2 | "She heard him clicking his" | | 3 | "She pulled a latex glove" | | 4 | "She circled the kiosk once," | | 5 | "She stopped at the back" | | 6 | "She leaned in close enough" | | 7 | "It was a circle, bisected" | | 8 | "She'd seen something like it" | | 9 | "He appeared beside her." | | 10 | "She directed the torch beam" | | 11 | "Her phone buzzed." | | 12 | "She checked the screen." | | 13 | "She stepped back from the" | | 14 | "She ended the call before" | | 15 | "She walked the alley again" | | 16 | "She stopped at the right" | | 17 | "She pressed her palm flat" | | 18 | "She said it quietly, more" | | 19 | "She didn't answer, because at" |
| | ratio | 0.375 | |
| 77.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 64 | | matches | | 0 | "The body had been folded" | | 1 | "Detective Harlow Quinn crouched at" | | 2 | "She studied the man inside" | | 3 | "His legs were bent at" | | 4 | "Death was never tidy." | | 5 | "She heard him clicking his" | | 6 | "She pulled a latex glove" | | 7 | "The victim's expression was placid." | | 8 | "The forensics team had photographed" | | 9 | "The phone box stood at" | | 10 | "Bins lined one wall." | | 11 | "The other wall was papered" | | 12 | "Somebody had stapled a flyer" | | 13 | "The alley smelled of rain-soaked" | | 14 | "Crane flipped his notebook open" | | 15 | "She circled the kiosk once," | | 16 | "The ground around it was" | | 17 | "The outgoing set was smaller," | | 18 | "She stopped at the back" | | 19 | "There, near the base of" |
| | ratio | 0.766 | |
| 78.13% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 64 | | matches | | 0 | "Either he'd been sedated before" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn crouched at the threshold of the old red kiosk, the kind that hadn't worked in years, its glass panels milky with grime and one pane knoc…" |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "She said, more to herself than to him" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.175 | | leniency | 0.35 | | rawRatio | 0 | | effectiveRatio | 0 | |