| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1396 | | 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) | |
| 67.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1396 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "dancing" | | 1 | "stark" | | 2 | "depths" | | 3 | "potential" | | 4 | "stomach" | | 5 | "charged" | | 6 | "profound" | | 7 | "maw" | | 8 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 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 | 1 | | hedgeCount | 0 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 157 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1394 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1346 | | uniqueNames | 14 | | maxNameDensity | 0.67 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 9 | | Herrera | 8 | | Morris | 5 | | Three | 1 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Victorian | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Market" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 98.98% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like an old service yard, a dead s" | | 1 | "something like blood" |
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| 56.53% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.435 | | wordCount | 1394 | | matches | | 0 | "no eyes but" | | 1 | "not toward the exit, but toward a dark archway behind him, leading deeper into the tu" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 157 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 30.98 | | std | 18.48 | | cv | 0.597 | | sampleLengths | | 0 | 60 | | 1 | 22 | | 2 | 31 | | 3 | 52 | | 4 | 29 | | 5 | 55 | | 6 | 15 | | 7 | 10 | | 8 | 70 | | 9 | 58 | | 10 | 25 | | 11 | 95 | | 12 | 34 | | 13 | 35 | | 14 | 30 | | 15 | 39 | | 16 | 33 | | 17 | 22 | | 18 | 2 | | 19 | 51 | | 20 | 33 | | 21 | 27 | | 22 | 13 | | 23 | 30 | | 24 | 59 | | 25 | 3 | | 26 | 45 | | 27 | 17 | | 28 | 49 | | 29 | 24 | | 30 | 23 | | 31 | 27 | | 32 | 35 | | 33 | 28 | | 34 | 19 | | 35 | 17 | | 36 | 21 | | 37 | 33 | | 38 | 19 | | 39 | 10 | | 40 | 11 | | 41 | 35 | | 42 | 17 | | 43 | 12 | | 44 | 19 |
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| 93.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 152 | | matches | | 0 | "was lost" | | 1 | "was gone" | | 2 | "was replaced" | | 3 | "were lined" | | 4 | "being given" |
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| 8.61% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 209 | | matches | | 0 | "was heading" | | 1 | "was scrambling" | | 2 | "was already moving" | | 3 | "was selling" | | 4 | "wasn’t running" | | 5 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 157 | | ratio | 0.006 | | matches | | 0 | "The smell of the city—the greasy food, the exhaust fumes, the wet concrete—was replaced by something richer." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 417 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 5 | | adverbRatio | 0.011990407673860911 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.002398081534772182 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 157 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 157 | | mean | 8.88 | | std | 5.75 | | cv | 0.647 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 11 | | 3 | 13 | | 4 | 2 | | 5 | 10 | | 6 | 10 | | 7 | 7 | | 8 | 17 | | 9 | 4 | | 10 | 3 | | 11 | 8 | | 12 | 15 | | 13 | 4 | | 14 | 2 | | 15 | 14 | | 16 | 9 | | 17 | 4 | | 18 | 2 | | 19 | 1 | | 20 | 22 | | 21 | 8 | | 22 | 20 | | 23 | 12 | | 24 | 15 | | 25 | 3 | | 26 | 11 | | 27 | 1 | | 28 | 10 | | 29 | 12 | | 30 | 13 | | 31 | 17 | | 32 | 11 | | 33 | 6 | | 34 | 11 | | 35 | 9 | | 36 | 4 | | 37 | 11 | | 38 | 10 | | 39 | 3 | | 40 | 7 | | 41 | 9 | | 42 | 5 | | 43 | 14 | | 44 | 5 | | 45 | 3 | | 46 | 3 | | 47 | 10 | | 48 | 20 | | 49 | 3 |
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| 28.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 33 | | diversityRatio | 0.2356687898089172 | | totalSentences | 157 | | uniqueOpeners | 37 | |
| 47.28% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 141 | | matches | | 0 | "Only a profound, weary resignation." | | 1 | "Officially, this place didn’t exist." |
| | ratio | 0.014 | |
| 75.32% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 141 | | matches | | 0 | "Her worn leather jacket, dark" | | 1 | "Her eyes, brown and unblinking," | | 2 | "He was fast, she’d give" | | 3 | "He cut through the late-night" | | 4 | "He didn’t look back." | | 5 | "He just ran." | | 6 | "She knew these streets." | | 7 | "He was heading for the" | | 8 | "She closed the distance." | | 9 | "He turned a corner, a" | | 10 | "He was scrambling over a" | | 11 | "He dropped down on the" | | 12 | "She hauled herself up, her" | | 13 | "She landed in what looked" | | 14 | "It was something else." | | 15 | "Her partner, DS Morris, had" | | 16 | "He’d called them the city’s" | | 17 | "He never came back out." | | 18 | "Her hand went to her" | | 19 | "She stood at the threshold," |
| | ratio | 0.362 | |
| 34.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 120 | | totalSentences | 141 | | matches | | 0 | "The rain hammered the pavement," | | 1 | "Detective Harlow Quinn moved through" | | 2 | "Her worn leather jacket, dark" | | 3 | "Her eyes, brown and unblinking," | | 4 | "Tommy to the paramedics who" | | 5 | "A ghost who'd been slipping" | | 6 | "He was fast, she’d give" | | 7 | "He cut through the late-night" | | 8 | "He didn’t look back." | | 9 | "He just ran." | | 10 | "Quinn’s lungs burned, but her" | | 11 | "She knew these streets." | | 12 | "He was heading for the" | | 13 | "A flash of grim satisfaction" | | 14 | "She closed the distance." | | 15 | "The sound of their footfalls" | | 16 | "He turned a corner, a" | | 17 | "Quinn followed, her worn leather" | | 18 | "The smell of wet rubbish" | | 19 | "He was scrambling over a" |
| | ratio | 0.851 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 141 | | matches | | 0 | "To go down was to" | | 1 | "To enter a world where" | | 2 | "To where, she couldn’t fathom." |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 3 | | matches | | 0 | "Her hand went to her hip, automatically checking for a radio that wasn’t there." | | 1 | "The other part of her, the part that still saw Morris’s empty desk every morning, the part that heard his voice in her dreams asking why she’d let him go in alo…" | | 2 | "The air hummed with conversation, the clink of coins, the sharp scent of things that shouldn't exist." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.5 | | effectiveRatio | 0.286 | |