| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 118 | | tagDensity | 0.136 | | leniency | 0.271 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2367 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 91.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2367 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "silence" | | 1 | "grave" | | 2 | "traced" | | 3 | "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 | 1 | | narrationSentences | 169 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 169 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 271 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 60 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2367 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 76 | | wordCount | 1511 | | uniqueNames | 9 | | maxNameDensity | 1.59 | | worstName | "Reed" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Quinn | 1 | | Sergeant | 1 | | Nabil | 1 | | Reed | 24 | | Harlow | 24 | | Limehouse | 1 | | Eva | 20 | | London | 1 | | One | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Sergeant" | | 2 | "Reed" | | 3 | "Harlow" | | 4 | "Eva" | | 5 | "One" |
| | places | | | globalScore | 0.706 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like she had run all the way from" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.845 | | wordCount | 2367 | | matches | | 0 | "not as decoration but as borders" | | 1 | "not north but across the platform toward the dark tunnel beyond the body" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 271 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 184 | | mean | 12.86 | | std | 15.59 | | cv | 1.212 | | sampleLengths | | 0 | 12 | | 1 | 97 | | 2 | 18 | | 3 | 31 | | 4 | 11 | | 5 | 2 | | 6 | 28 | | 7 | 3 | | 8 | 12 | | 9 | 2 | | 10 | 6 | | 11 | 14 | | 12 | 1 | | 13 | 58 | | 14 | 52 | | 15 | 6 | | 16 | 50 | | 17 | 41 | | 18 | 4 | | 19 | 9 | | 20 | 10 | | 21 | 11 | | 22 | 7 | | 23 | 5 | | 24 | 42 | | 25 | 2 | | 26 | 4 | | 27 | 41 | | 28 | 2 | | 29 | 3 | | 30 | 49 | | 31 | 8 | | 32 | 26 | | 33 | 5 | | 34 | 2 | | 35 | 21 | | 36 | 70 | | 37 | 2 | | 38 | 13 | | 39 | 3 | | 40 | 1 | | 41 | 4 | | 42 | 9 | | 43 | 5 | | 44 | 1 | | 45 | 49 | | 46 | 5 | | 47 | 5 | | 48 | 2 | | 49 | 13 |
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| 96.96% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 169 | | matches | | 0 | "been arranged" | | 1 | "been brushed" | | 2 | "been scratched" | | 3 | "been replaced" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 267 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 271 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1516 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.025065963060686015 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0032981530343007917 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 271 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 271 | | mean | 8.73 | | std | 7.8 | | cv | 0.893 | | sampleLengths | | 0 | 12 | | 1 | 26 | | 2 | 43 | | 3 | 7 | | 4 | 9 | | 5 | 12 | | 6 | 18 | | 7 | 31 | | 8 | 11 | | 9 | 2 | | 10 | 6 | | 11 | 8 | | 12 | 14 | | 13 | 3 | | 14 | 12 | | 15 | 2 | | 16 | 6 | | 17 | 14 | | 18 | 1 | | 19 | 18 | | 20 | 27 | | 21 | 11 | | 22 | 2 | | 23 | 15 | | 24 | 10 | | 25 | 16 | | 26 | 11 | | 27 | 6 | | 28 | 1 | | 29 | 1 | | 30 | 1 | | 31 | 5 | | 32 | 4 | | 33 | 5 | | 34 | 4 | | 35 | 15 | | 36 | 14 | | 37 | 20 | | 38 | 21 | | 39 | 4 | | 40 | 6 | | 41 | 3 | | 42 | 10 | | 43 | 11 | | 44 | 7 | | 45 | 5 | | 46 | 42 | | 47 | 2 | | 48 | 4 | | 49 | 6 |
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| 59.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3763837638376384 | | totalSentences | 271 | | uniqueOpeners | 102 | |
| 23.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 142 | | matches | | 0 | "Then she crossed back to" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 142 | | matches | | 0 | "Her leather watch pressed cold" | | 1 | "She never touched it before" | | 2 | "She took in the arches" | | 3 | "She had expected a squat," | | 4 | "Her eyes settled on the" | | 5 | "His throat had been cut" | | 6 | "He had his jacket off" | | 7 | "He held up the notebook." | | 8 | "She leaned closer." | | 9 | "His coat hem had been" | | 10 | "She looked to the feet" | | 11 | "She moved to the ticket" | | 12 | "He barked a laugh." | | 13 | "His mouth thinned." | | 14 | "He hated crouching in his" | | 15 | "She shifted left." | | 16 | "She filed it away." | | 17 | "She carried a worn leather" | | 18 | "Her eyes snagged on the" | | 19 | "She hooked two gloved fingers" |
| | ratio | 0.204 | |
| 69.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 111 | | totalSentences | 142 | | matches | | 0 | "The train tunnel opened into" | | 1 | "Harlow Quinn stopped at the" | | 2 | "The station signs still clung" | | 3 | "Canvas stalls hunched along the" | | 4 | "A glass case lay on" | | 5 | "Uniforms ringed the platform in" | | 6 | "A constable at the foot" | | 7 | "Harlow ducked under and kept" | | 8 | "Her leather watch pressed cold" | | 9 | "She never touched it before" | | 10 | "The constable glanced down the" | | 11 | "She took in the arches" | | 12 | "She had expected a squat," | | 13 | "The stalls looked improvised until" | | 14 | "People built this to be" | | 15 | "Her eyes settled on the" | | 16 | "Shirt stiff with blood." | | 17 | "Hands laid palm up on" | | 18 | "His throat had been cut" | | 19 | "Detective Sergeant Nabil Reed stood" |
| | ratio | 0.782 | |
| 70.42% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 142 | | matches | | 0 | "If his throat had opened" | | 1 | "Whoever moved him had removed" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 3 | | matches | | 0 | "Uniforms ringed the platform in stiff clusters, each one wearing the same expression: annoyance cut with fear, the look of people who had stepped into somebody …" | | 1 | "Hands laid palm up on his thighs, as if somebody had posed him for confession." | | 2 | "Harlow turned her head toward the tunnel, then back to the machine, and the shape of it began to settle in her mind: the staged corpse, the shared witness story…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 118 | | tagDensity | 0.102 | | leniency | 0.203 | | rawRatio | 0.083 | | effectiveRatio | 0.017 | |