| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1834 | | 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) | |
| 75.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1834 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "crystal" | | 1 | "footsteps" | | 2 | "whisper" | | 3 | "silence" | | 4 | "trembled" | | 5 | "velvet" | | 6 | "scanned" | | 7 | "warmth" | | 8 | "charged" |
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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 | 170 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 170 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 218 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1834 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1510 | | uniqueNames | 17 | | maxNameDensity | 1.59 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Tomás" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 24 | | Greek | 1 | | Street | 1 | | Camden | 1 | | Met | 1 | | Underground | 1 | | Morris | 1 | | Wapping | 1 | | Veil | 1 | | Market | 3 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 10 | | Rain | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" | | 7 | "Rain" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Camden" | | 3 | "Wapping" |
| | globalScore | 0.705 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 106 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a burn pressed into the skin" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1834 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 218 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 141 | | mean | 13.01 | | std | 15.17 | | cv | 1.166 | | sampleLengths | | 0 | 30 | | 1 | 60 | | 2 | 1 | | 3 | 15 | | 4 | 3 | | 5 | 28 | | 6 | 18 | | 7 | 59 | | 8 | 2 | | 9 | 8 | | 10 | 56 | | 11 | 4 | | 12 | 14 | | 13 | 30 | | 14 | 8 | | 15 | 2 | | 16 | 57 | | 17 | 8 | | 18 | 5 | | 19 | 44 | | 20 | 4 | | 21 | 3 | | 22 | 7 | | 23 | 6 | | 24 | 5 | | 25 | 52 | | 26 | 2 | | 27 | 8 | | 28 | 38 | | 29 | 6 | | 30 | 2 | | 31 | 22 | | 32 | 6 | | 33 | 4 | | 34 | 5 | | 35 | 12 | | 36 | 9 | | 37 | 11 | | 38 | 3 | | 39 | 2 | | 40 | 5 | | 41 | 36 | | 42 | 9 | | 43 | 2 | | 44 | 17 | | 45 | 5 | | 46 | 9 | | 47 | 2 | | 48 | 39 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 170 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 240 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 218 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1514 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.021136063408190225 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0006605019815059445 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 218 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 218 | | mean | 8.41 | | std | 6.75 | | cv | 0.802 | | sampleLengths | | 0 | 30 | | 1 | 19 | | 2 | 13 | | 3 | 28 | | 4 | 1 | | 5 | 15 | | 6 | 3 | | 7 | 14 | | 8 | 14 | | 9 | 7 | | 10 | 11 | | 11 | 32 | | 12 | 7 | | 13 | 6 | | 14 | 14 | | 15 | 2 | | 16 | 8 | | 17 | 33 | | 18 | 5 | | 19 | 2 | | 20 | 16 | | 21 | 4 | | 22 | 14 | | 23 | 8 | | 24 | 4 | | 25 | 6 | | 26 | 12 | | 27 | 8 | | 28 | 2 | | 29 | 7 | | 30 | 5 | | 31 | 16 | | 32 | 11 | | 33 | 4 | | 34 | 6 | | 35 | 8 | | 36 | 8 | | 37 | 5 | | 38 | 17 | | 39 | 9 | | 40 | 3 | | 41 | 15 | | 42 | 4 | | 43 | 3 | | 44 | 5 | | 45 | 2 | | 46 | 6 | | 47 | 5 | | 48 | 27 | | 49 | 8 |
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| 50.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3302752293577982 | | totalSentences | 218 | | uniqueOpeners | 72 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 150 | | matches | | 0 | "Then he vanished through the" | | 1 | "Only a description, a location," | | 2 | "Then he stepped through the" | | 3 | "Then the line went dead." | | 4 | "Instead, lanterns hung from the" | | 5 | "Then he looked at her" | | 6 | "Somewhere below, metal screamed against" | | 7 | "Then he raised something small" |
| | ratio | 0.053 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 150 | | matches | | 0 | "He moved with the city’s" | | 1 | "Her voice cracked off the" | | 2 | "He glanced back." | | 3 | "It banged against its stop" | | 4 | "He reached the pavement and" | | 5 | "Her leather watch had stopped" | | 6 | "She had looked at it" | | 7 | "He knows you’re looking." | | 8 | "Her right shoulder clipped a" | | 9 | "She ignored it and drove" | | 10 | "He had wanted her off" | | 11 | "It looked like a burn" | | 12 | "He turned his head." | | 13 | "Her radio hissed at her" | | 14 | "She thumbed the transmit button." | | 15 | "She pulled it away." | | 16 | "He had radioed her once." | | 17 | "His voice had carried the" | | 18 | "They had found his warrant" | | 19 | "It smelt of incense, cold" |
| | ratio | 0.24 | |
| 63.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 150 | | matches | | 0 | "Rain slapped Detective Harlow Quinn" | | 1 | "The man ahead of her" | | 2 | "He moved with the city’s" | | 3 | "Each time Quinn gained ground," | | 4 | "Her voice cracked off the" | | 5 | "He glanced back." | | 6 | "Streetlight caught a pale strip" | | 7 | "Quinn hit the gate a" | | 8 | "It banged against its stop" | | 9 | "The street beyond lay empty" | | 10 | "The suspect crossed the road" | | 11 | "A taxi tore past, horn" | | 12 | "He reached the pavement and" | | 13 | "Her leather watch had stopped" | | 14 | "She had looked at it" | | 15 | "He knows you’re looking." | | 16 | "The narrow passage spat her" | | 17 | "Bins crowded one wall." | | 18 | "The scent of fryer grease," | | 19 | "A dark shape darted across" |
| | ratio | 0.793 | |
| 33.33% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 150 | | matches | | 0 | "Now the second hand rested" |
| | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 3 | | matches | | 0 | "Each time Quinn gained ground, he found another turn: beneath a scaffold, through a delivery yard, over a chain barrier that sent her boots skidding on wet conc…" | | 1 | "The street beyond lay empty except for black cabs, streams of water running along the kerb, and a couple huddled beneath a clear umbrella that had turned inside…" | | 2 | "It had surfaced in the notebooks of fraudsters, smugglers, and a man who had vanished from a police interview room without opening the door." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 51 | | tagDensity | 0.039 | | leniency | 0.078 | | rawRatio | 0.5 | | effectiveRatio | 0.039 | |