| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 11 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0.167 | | effectiveRatio | 0.167 | |
| 90.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1626 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "quickly" | | 1 | "lightly" | | 2 | "slowly" |
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| 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) | |
| 60.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1626 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "glint" | | 1 | "familiar" | | 2 | "warmth" | | 3 | "gloom" | | 4 | "wavering" | | 5 | "footsteps" | | 6 | "pulse" | | 7 | "etched" | | 8 | "navigate" | | 9 | "fractured" | | 10 | "electric" |
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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 | 149 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 149 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 154 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1626 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.51% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1524 | | uniqueNames | 16 | | maxNameDensity | 1.05 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 16 | | Metropolitan | 1 | | Police | 1 | | Morris | 3 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Camden | 2 | | Tube | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | London | 1 | | Veil | 1 | | Market | 1 | | Three | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" |
| | places | | 0 | "Metropolitan" | | 1 | "Soho" | | 2 | "London" | | 3 | "Market" |
| | globalScore | 0.975 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 77.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.23 | | wordCount | 1626 | | matches | | 0 | "Not from bodies, but from iron braziers" | | 1 | "not with excitement, but with instinctive caution" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 154 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 54.2 | | std | 37.64 | | cv | 0.695 | | sampleLengths | | 0 | 100 | | 1 | 126 | | 2 | 84 | | 3 | 57 | | 4 | 43 | | 5 | 61 | | 6 | 89 | | 7 | 11 | | 8 | 96 | | 9 | 151 | | 10 | 81 | | 11 | 73 | | 12 | 7 | | 13 | 47 | | 14 | 19 | | 15 | 11 | | 16 | 40 | | 17 | 94 | | 18 | 7 | | 19 | 10 | | 20 | 56 | | 21 | 16 | | 22 | 69 | | 23 | 61 | | 24 | 68 | | 25 | 11 | | 26 | 17 | | 27 | 7 | | 28 | 66 | | 29 | 48 |
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| 98.20% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 149 | | matches | | 0 | "was plastered" | | 1 | "been told" | | 2 | "were written" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 263 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 154 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1538 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.025357607282184655 | | lyAdverbCount | 19 | | lyAdverbRatio | 0.01235370611183355 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 154 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 154 | | mean | 10.56 | | std | 7.55 | | cv | 0.715 | | sampleLengths | | 0 | 15 | | 1 | 7 | | 2 | 38 | | 3 | 21 | | 4 | 2 | | 5 | 17 | | 6 | 28 | | 7 | 15 | | 8 | 4 | | 9 | 8 | | 10 | 20 | | 11 | 25 | | 12 | 19 | | 13 | 7 | | 14 | 17 | | 15 | 27 | | 16 | 8 | | 17 | 14 | | 18 | 18 | | 19 | 17 | | 20 | 12 | | 21 | 15 | | 22 | 5 | | 23 | 4 | | 24 | 4 | | 25 | 7 | | 26 | 12 | | 27 | 18 | | 28 | 2 | | 29 | 2 | | 30 | 2 | | 31 | 7 | | 32 | 12 | | 33 | 15 | | 34 | 12 | | 35 | 15 | | 36 | 10 | | 37 | 17 | | 38 | 6 | | 39 | 16 | | 40 | 6 | | 41 | 34 | | 42 | 6 | | 43 | 5 | | 44 | 5 | | 45 | 15 | | 46 | 15 | | 47 | 7 | | 48 | 8 | | 49 | 14 |
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| 67.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.45454545454545453 | | totalSentences | 154 | | uniqueOpeners | 70 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 132 | | matches | | 0 | "Once-white ceramic tiles, now stained" | | 1 | "Somewhere above, through layers of" | | 2 | "Closely cropped salt-and-pepper hair damp" | | 3 | "Finally, he stepped back." |
| | ratio | 0.03 | |
| 92.73% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 132 | | matches | | 0 | "Her boots splashed through oil-slicked" | | 1 | "She caught the glint of" | | 2 | "He was a shadow given" | | 3 | "She did not panic." | | 4 | "Her sharp jaw set as" | | 5 | "She would not let another" | | 6 | "She passed the distinctive green" | | 7 | "Her suspect had ducked inside" | | 8 | "He had surfaced moments later" | | 9 | "She did not need backup." | | 10 | "He disappeared through a rusted" | | 11 | "Her flashlight clicked on, cutting" | | 12 | "She counted thirty-two steps before" | | 13 | "She stepped through the archway." | | 14 | "She caught snippets of conversation" | | 15 | "His short curly dark brown" | | 16 | "He operated in the gray" | | 17 | "She did not have time" | | 18 | "He had known the way" | | 19 | "He had known how to" |
| | ratio | 0.318 | |
| 88.79% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 132 | | matches | | 0 | "Rain fell in cold, relentless" | | 1 | "Harlow Quinn did not slow" | | 2 | "Her boots splashed through oil-slicked" | | 3 | "She caught the glint of" | | 4 | "The suspect had exactly that" | | 5 | "He was a shadow given" | | 6 | "Quinn’s brown eyes tracked the" | | 7 | "She did not panic." | | 8 | "Panic was a luxury for" | | 9 | "Her sharp jaw set as" | | 10 | "She would not let another" | | 11 | "She passed the distinctive green" | | 12 | "Her suspect had ducked inside" | | 13 | "He had surfaced moments later" | | 14 | "Quinn had followed, trusting the" | | 15 | "The suspect took a left" | | 16 | "The rain intensified, turning the" | | 17 | "Quinn adjusted her grip on" | | 18 | "She did not need backup." | | 19 | "Backup talked too much." |
| | ratio | 0.742 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 132 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 7 | | matches | | 0 | "Her boots splashed through oil-slicked puddles, water already seeping through the reinforced seams of her trench coat, but she moved with the same clipped, econ…" | | 1 | "Three years since DS Morris had vanished down a stairwell under circumstances that made sense only in the quiet hours when she could not sleep." | | 2 | "People moved through the narrow aisles in coats pulled tight, faces tilted upward or hidden behind hoods, their footsteps muffled by the thick carpet of fallen …" | | 3 | "Vendors hawked wares that made her pulse quicken not with excitement, but with instinctive caution." | | 4 | "Something that operated outside jurisdiction, outside statute, outside her comprehension." | | 5 | "She stopped beside a stall displaying vials of liquid that shifted color like oil on water." | | 6 | "Every instinct honed by a decade and a half of policing screamed to retreat, to file a report, to wait for warrants and forensic protocols that meant nothing in…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, voice low and rough as gravel" | | 1 | "she said, voice cutting through the ambient murmur like glass" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0 | | effectiveRatio | 0 | |