| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1199 | | 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) | |
| 70.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1199 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "traced" | | 1 | "treacherous" | | 2 | "glint" | | 3 | "whisper" | | 4 | "flickered" | | 5 | "weight" | | 6 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 104 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | | 0 | "happened to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1199 | | 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 | 40 | | wordCount | 1152 | | uniqueNames | 20 | | maxNameDensity | 0.61 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Shaftesbury | 1 | | Avenue | 1 | | Harlow | 1 | | Quinn | 7 | | Raven | 1 | | Nest | 5 | | Herrera | 2 | | Saint | 1 | | Christopher | 1 | | Morris | 3 | | Whitechapel | 1 | | Camden | 2 | | Lock | 1 | | Road | 1 | | Underground | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Met | 1 | | Tomás | 7 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Market" | | 8 | "Tomás" |
| | places | | 0 | "Shaftesbury" | | 1 | "Avenue" | | 2 | "Whitechapel" | | 3 | "Camden" | | 4 | "Lock" | | 5 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1199 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 107 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 35.26 | | std | 28.93 | | cv | 0.82 | | sampleLengths | | 0 | 44 | | 1 | 96 | | 2 | 3 | | 3 | 63 | | 4 | 2 | | 5 | 127 | | 6 | 21 | | 7 | 3 | | 8 | 45 | | 9 | 9 | | 10 | 47 | | 11 | 8 | | 12 | 50 | | 13 | 50 | | 14 | 21 | | 15 | 4 | | 16 | 60 | | 17 | 88 | | 18 | 3 | | 19 | 50 | | 20 | 49 | | 21 | 52 | | 22 | 14 | | 23 | 16 | | 24 | 20 | | 25 | 18 | | 26 | 58 | | 27 | 37 | | 28 | 6 | | 29 | 24 | | 30 | 13 | | 31 | 42 | | 32 | 38 | | 33 | 18 |
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| 81.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 104 | | matches | | 0 | "is emptied" | | 1 | "been nudged" | | 2 | "been shut" | | 3 | "was rumored" | | 4 | "been traced" | | 5 | "been painted" | | 6 | "been killed" | | 7 | "been redacted" |
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| 27.29% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 193 | | matches | | 0 | "wasn't panicking" | | 1 | "was checking" | | 2 | "wasn't running" | | 3 | "was negotiating" | | 4 | "was holding" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 107 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1160 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.021551724137931036 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0017241379310344827 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 11.21 | | std | 8.62 | | cv | 0.769 | | sampleLengths | | 0 | 18 | | 1 | 26 | | 2 | 13 | | 3 | 9 | | 4 | 8 | | 5 | 32 | | 6 | 5 | | 7 | 29 | | 8 | 3 | | 9 | 2 | | 10 | 22 | | 11 | 21 | | 12 | 5 | | 13 | 13 | | 14 | 2 | | 15 | 4 | | 16 | 15 | | 17 | 27 | | 18 | 23 | | 19 | 9 | | 20 | 28 | | 21 | 8 | | 22 | 13 | | 23 | 4 | | 24 | 6 | | 25 | 3 | | 26 | 3 | | 27 | 5 | | 28 | 3 | | 29 | 17 | | 30 | 14 | | 31 | 5 | | 32 | 9 | | 33 | 6 | | 34 | 3 | | 35 | 2 | | 36 | 19 | | 37 | 7 | | 38 | 19 | | 39 | 8 | | 40 | 8 | | 41 | 20 | | 42 | 11 | | 43 | 7 | | 44 | 4 | | 45 | 9 | | 46 | 36 | | 47 | 5 | | 48 | 5 | | 49 | 12 |
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| 36.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.32710280373831774 | | totalSentences | 107 | | uniqueOpeners | 35 | |
| 67.34% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 99 | | matches | | 0 | "Just a single glass left" | | 1 | "Somewhere far below, a bell" |
| | ratio | 0.02 | |
| 46.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 99 | | matches | | 0 | "She had him." | | 1 | "She didn't want a uniform" | | 2 | "He knew the streets." | | 3 | "He cut down a narrow" | | 4 | "She still didn't know what" | | 5 | "She just knew she wasn't" | | 6 | "He wasn't panicking." | | 7 | "He was checking the gap." | | 8 | "She closed it." | | 9 | "He hit Camden Lock Road" | | 10 | "Her sharp jaw was set." | | 11 | "He didn't go for a" | | 12 | "He went down." | | 13 | "It carried a faint metallic" | | 14 | "She could hear water dripping," | | 15 | "Her radio was silent in" | | 16 | "She had a warrant for" | | 17 | "She had nothing for this." | | 18 | "She should call it in." | | 19 | "She should wait for forensics," |
| | ratio | 0.434 | |
| 35.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 99 | | matches | | 0 | "Rain came down hard off" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "The Raven's Nest had been" | | 3 | "She had him." | | 4 | "The scar on his left" | | 5 | "Quinn didn't call it in." | | 6 | "She didn't want a uniform" | | 7 | "He knew the streets." | | 8 | "Soho at night was a" | | 9 | "He cut down a narrow" | | 10 | "Quinn's leather watch on her" | | 11 | "She still didn't know what" | | 12 | "She just knew she wasn't" | | 13 | "Tomás looked back once." | | 14 | "That was worse." | | 15 | "He wasn't panicking." | | 16 | "He was checking the gap." | | 17 | "She closed it." | | 18 | "He hit Camden Lock Road" | | 19 | "Rain plastered her hair to" |
| | ratio | 0.848 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 61.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 5 | | matches | | 0 | "She had a warrant for the Nest, for questioning Herrera about off-the-books medical care, for the clique he was rumored to run with, for missing persons who'd b…" | | 1 | "The walls were damp brick, covered in old graffiti that had been painted over and painted over again until the surface looked bruised." | | 2 | "She could see the glint of glass vials filled with liquid that moved on its own, a stack of bound ledgers with wax seals, a woman holding out a bone token the s…" | | 3 | "The black-and-white photographs had been of people who had never been in the papers." | | 4 | "She thought of Morris, of the bruising around his neck that the pathologist had called inconsistent, of the file that had been redacted line by line." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, voice low and even," |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |