| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.261 | | leniency | 0.522 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2208 | | 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) | |
| 88.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2208 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "tracing" | | 1 | "measured" | | 2 | "sense of" | | 3 | "pulse" | | 4 | "trembled" |
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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 | 220 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 220 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 254 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2208 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 81 | | wordCount | 2010 | | uniqueNames | 13 | | maxNameDensity | 1.69 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 5 | | Harlow | 1 | | Quinn | 34 | | Herrera | 27 | | Wardour | 1 | | Street | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 2 | | Camden | 2 | | London | 1 | | Ellis | 4 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Ellis" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Camden" | | 3 | "London" |
| | globalScore | 0.654 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 151 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared behind it, one grey eye fixed on her" | | 1 | "pulse beating visibly in his throat, much" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.453 | | wordCount | 2208 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 254 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 106 | | mean | 20.83 | | std | 17.09 | | cv | 0.821 | | sampleLengths | | 0 | 21 | | 1 | 33 | | 2 | 1 | | 3 | 23 | | 4 | 57 | | 5 | 2 | | 6 | 59 | | 7 | 25 | | 8 | 23 | | 9 | 14 | | 10 | 24 | | 11 | 7 | | 12 | 58 | | 13 | 24 | | 14 | 45 | | 15 | 37 | | 16 | 16 | | 17 | 8 | | 18 | 3 | | 19 | 3 | | 20 | 39 | | 21 | 7 | | 22 | 23 | | 23 | 27 | | 24 | 5 | | 25 | 13 | | 26 | 37 | | 27 | 48 | | 28 | 43 | | 29 | 13 | | 30 | 11 | | 31 | 27 | | 32 | 56 | | 33 | 25 | | 34 | 21 | | 35 | 9 | | 36 | 43 | | 37 | 3 | | 38 | 43 | | 39 | 45 | | 40 | 12 | | 41 | 21 | | 42 | 14 | | 43 | 20 | | 44 | 15 | | 45 | 4 | | 46 | 11 | | 47 | 12 | | 48 | 11 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 220 | | matches | | 0 | "were plastered" | | 1 | "been turned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 347 | | matches | | 0 | "was going" | | 1 | "was climbing" | | 2 | "was joking" | | 3 | "were calling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 254 | | ratio | 0.004 | | matches | | 0 | "She had heard him give evasive answers before; this wasn’t one." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2015 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.020347394540942927 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0024813895781637717 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 254 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 254 | | mean | 8.69 | | std | 5.37 | | cv | 0.617 | | sampleLengths | | 0 | 21 | | 1 | 11 | | 2 | 9 | | 3 | 13 | | 4 | 1 | | 5 | 3 | | 6 | 17 | | 7 | 3 | | 8 | 18 | | 9 | 12 | | 10 | 14 | | 11 | 13 | | 12 | 2 | | 13 | 4 | | 14 | 9 | | 15 | 6 | | 16 | 21 | | 17 | 14 | | 18 | 5 | | 19 | 12 | | 20 | 7 | | 21 | 6 | | 22 | 23 | | 23 | 3 | | 24 | 3 | | 25 | 8 | | 26 | 4 | | 27 | 8 | | 28 | 12 | | 29 | 7 | | 30 | 13 | | 31 | 24 | | 32 | 7 | | 33 | 14 | | 34 | 13 | | 35 | 11 | | 36 | 18 | | 37 | 16 | | 38 | 3 | | 39 | 8 | | 40 | 9 | | 41 | 10 | | 42 | 4 | | 43 | 14 | | 44 | 4 | | 45 | 8 | | 46 | 4 | | 47 | 8 | | 48 | 3 | | 49 | 3 |
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| 56.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3464566929133858 | | totalSentences | 254 | | uniqueOpeners | 88 | |
| 81.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 205 | | matches | | 0 | "Then he ran." | | 1 | "Then he twisted free." | | 2 | "Well past midnight." | | 3 | "Then it lengthened across the" | | 4 | "Instead she followed the spot" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 57 | | totalSentences | 205 | | matches | | 0 | "He was fast, but he" | | 1 | "She took the corner wide," | | 2 | "He vaulted a low chain" | | 3 | "Her radio crackled against her" | | 4 | "She tried again." | | 5 | "His short curls were plastered" | | 6 | "He’d smiled politely when she" | | 7 | "He’d told her he worked" | | 8 | "He reached a bus stop," | | 9 | "She got within arm’s reach" | | 10 | "She caught his sleeve." | | 11 | "He hissed in pain." | | 12 | "He kept running." | | 13 | "she heard him tell the" | | 14 | "She got the registration." | | 15 | "Her phone had one bar" | | 16 | "She sent the number and" | | 17 | "She looked at the time" | | 18 | "She had seen one like" | | 19 | "It had a small hole" |
| | ratio | 0.278 | |
| 50.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 168 | | totalSentences | 205 | | matches | | 0 | "The man came out of" | | 1 | "Detective Harlow Quinn pushed away" | | 2 | "Rain had soaked through the" | | 3 | "Tomás Herrera stopped." | | 4 | "Quinn crossed the road behind" | | 5 | "Herrera cut left into the" | | 6 | "He was fast, but he" | | 7 | "Whatever he’d taken out of" | | 8 | "A woman stepped aside." | | 9 | "Herrera slipped past her and" | | 10 | "Quinn followed, boots striking wet" | | 11 | "She took the corner wide," | | 12 | "He vaulted a low chain" | | 13 | "Quinn went around the post" | | 14 | "Her radio crackled against her" | | 15 | "Static swallowed her." | | 16 | "She tried again." | | 17 | "The transmission light blinked red," | | 18 | "His short curls were plastered" | | 19 | "That bothered her more than" |
| | ratio | 0.82 | |
| 97.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 205 | | matches | | 0 | "Even from twenty yards, Quinn" | | 1 | "By the time she cleared" | | 2 | "Whoever guarded the door might" | | 3 | "To the right, a rusted" |
| | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 95 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.196 | | leniency | 0.391 | | rawRatio | 0 | | effectiveRatio | 0 | |