| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 94 | | tagDensity | 0.128 | | leniency | 0.255 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.72% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1525 | | 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) | |
| 93.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1525 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 160 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 160 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 242 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1525 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 25.96% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1169 | | uniqueNames | 8 | | maxNameDensity | 2.48 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 29 | | Reed | 22 | | Tube | 1 | | Metropolitan | 2 | | Police | 2 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Reed" | | 3 | "Police" | | 4 | "Morris" |
| | places | | | globalScore | 0.26 | | windowScore | 0.333 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared backwards then corrected themselves" | | 1 | "seemed scratched by a spider" |
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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 | 1525 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 242 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 177 | | mean | 8.62 | | std | 9.85 | | cv | 1.143 | | sampleLengths | | 0 | 16 | | 1 | 28 | | 2 | 6 | | 3 | 14 | | 4 | 2 | | 5 | 37 | | 6 | 32 | | 7 | 1 | | 8 | 4 | | 9 | 2 | | 10 | 5 | | 11 | 41 | | 12 | 8 | | 13 | 3 | | 14 | 38 | | 15 | 12 | | 16 | 3 | | 17 | 2 | | 18 | 1 | | 19 | 3 | | 20 | 4 | | 21 | 7 | | 22 | 4 | | 23 | 2 | | 24 | 3 | | 25 | 3 | | 26 | 43 | | 27 | 4 | | 28 | 5 | | 29 | 2 | | 30 | 3 | | 31 | 15 | | 32 | 6 | | 33 | 9 | | 34 | 34 | | 35 | 6 | | 36 | 2 | | 37 | 7 | | 38 | 43 | | 39 | 9 | | 40 | 8 | | 41 | 4 | | 42 | 9 | | 43 | 4 | | 44 | 11 | | 45 | 7 | | 46 | 6 | | 47 | 3 | | 48 | 1 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 160 | | matches | | 0 | "been etched" | | 1 | "were sewn" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 207 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 242 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1171 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.017933390264730998 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0025619128949615714 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 242 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 242 | | mean | 6.3 | | std | 4.48 | | cv | 0.711 | | sampleLengths | | 0 | 16 | | 1 | 6 | | 2 | 22 | | 3 | 6 | | 4 | 14 | | 5 | 2 | | 6 | 12 | | 7 | 9 | | 8 | 16 | | 9 | 11 | | 10 | 21 | | 11 | 1 | | 12 | 4 | | 13 | 2 | | 14 | 5 | | 15 | 6 | | 16 | 13 | | 17 | 22 | | 18 | 8 | | 19 | 3 | | 20 | 2 | | 21 | 15 | | 22 | 14 | | 23 | 2 | | 24 | 2 | | 25 | 3 | | 26 | 5 | | 27 | 7 | | 28 | 3 | | 29 | 2 | | 30 | 1 | | 31 | 3 | | 32 | 4 | | 33 | 2 | | 34 | 5 | | 35 | 4 | | 36 | 2 | | 37 | 3 | | 38 | 3 | | 39 | 14 | | 40 | 15 | | 41 | 14 | | 42 | 4 | | 43 | 5 | | 44 | 2 | | 45 | 3 | | 46 | 15 | | 47 | 6 | | 48 | 9 | | 49 | 6 |
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| 52.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.34297520661157027 | | totalSentences | 242 | | uniqueOpeners | 83 | |
| 99.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 134 | | matches | | 0 | "Then it pointed at the" | | 1 | "Then towards the tunnel." | | 2 | "Then back to Quinn." | | 3 | "Somewhere beneath the rails, something" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 134 | | matches | | 0 | "She caught the corpse by" | | 1 | "Its weight drove her to" | | 2 | "She eased him onto the" | | 3 | "His skull struck the platform" | | 4 | "He had one hand on" | | 5 | "He wore a dark suit" | | 6 | "She checked her leather watch." | | 7 | "Her face had no features," | | 8 | "She had worked eighteen years" | | 9 | "She had seen murderers hang" | | 10 | "She had seen men cut" | | 11 | "She had never seen a" | | 12 | "He stared at her." | | 13 | "She crouched beside the corpse." | | 14 | "She worked each finger loose" | | 15 | "She checked the dead man’s" | | 16 | "His eyes were sewn shut" | | 17 | "He smiled at Quinn." | | 18 | "She rose and approached him." | | 19 | "He tilted his head." |
| | ratio | 0.201 | |
| 1.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 123 | | totalSentences | 134 | | matches | | 0 | "The body fell out of" | | 1 | "She caught the corpse by" | | 2 | "Its weight drove her to" | | 3 | "Constable Reed called" | | 4 | "Quinn looked down at the" | | 5 | "She eased him onto the" | | 6 | "His skull struck the platform" | | 7 | "Reed descended the broken stairs" | | 8 | "He had one hand on" | | 9 | "Reed glanced towards the tunnel" | | 10 | "A black arch yawned beyond" | | 11 | "The dead man remained where" | | 12 | "He wore a dark suit" | | 13 | "She checked her leather watch." | | 14 | "The second hand ticked with" | | 15 | "Quinn stared at him." | | 16 | "A woman in a green" | | 17 | "Her face had no features," | | 18 | "Reed followed Quinn’s gaze." | | 19 | "A bell rang beneath the" |
| | ratio | 0.918 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 134 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 2 | | matches | | 0 | "Its weight drove her to one knee, cheek pressed against a coat that smelled of wet wool, iron and something sweetly rotten." | | 1 | "Glass jars held blue fingers, red teeth and pale things that twitched against their lids." |
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| 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 | 12 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 94 | | tagDensity | 0.128 | | leniency | 0.255 | | rawRatio | 0.083 | | effectiveRatio | 0.021 | |