| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 95 | | tagDensity | 0.211 | | leniency | 0.421 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2113 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "very" | | 1 | "tightly" | | 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) | |
| 97.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2113 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 154 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 154 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "tried to" | | 2 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 229 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2113 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 64 | | wordCount | 1373 | | uniqueNames | 9 | | maxNameDensity | 1.75 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Camden | 1 | | Grant | 15 | | Quinn | 24 | | Aurora | 1 | | Bloomsbury | 1 | | Kowalski | 2 | | Venn | 7 | | Eva | 12 | | Morris | 1 |
| | persons | | 0 | "Grant" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Venn" | | 4 | "Eva" | | 5 | "Morris" |
| | places | | | globalScore | 0.626 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.473 | | wordCount | 2113 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 229 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 134 | | mean | 15.77 | | std | 15.57 | | cv | 0.987 | | sampleLengths | | 0 | 18 | | 1 | 70 | | 2 | 16 | | 3 | 31 | | 4 | 2 | | 5 | 38 | | 6 | 11 | | 7 | 31 | | 8 | 48 | | 9 | 44 | | 10 | 15 | | 11 | 3 | | 12 | 5 | | 13 | 62 | | 14 | 58 | | 15 | 4 | | 16 | 2 | | 17 | 3 | | 18 | 12 | | 19 | 34 | | 20 | 42 | | 21 | 5 | | 22 | 11 | | 23 | 1 | | 24 | 13 | | 25 | 33 | | 26 | 11 | | 27 | 1 | | 28 | 5 | | 29 | 3 | | 30 | 30 | | 31 | 4 | | 32 | 12 | | 33 | 29 | | 34 | 8 | | 35 | 5 | | 36 | 2 | | 37 | 9 | | 38 | 20 | | 39 | 9 | | 40 | 32 | | 41 | 5 | | 42 | 26 | | 43 | 5 | | 44 | 8 | | 45 | 6 | | 46 | 26 | | 47 | 4 | | 48 | 10 | | 49 | 34 |
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| 84.76% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 154 | | matches | | 0 | "been closed" | | 1 | "been opened" | | 2 | "been rubbed" | | 3 | "been bored" | | 4 | "were folded" | | 5 | "been photographed" | | 6 | "were cleared" | | 7 | "been fixed" | | 8 | "been found" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 239 | | matches | | 0 | "was not pointing" | | 1 | "was beginning" | | 2 | "was missing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 229 | | ratio | 0.009 | | matches | | 0 | "Above them, Camden was all shuttered shops and slick pavement; below, the old station held its breath." | | 1 | "She had brought neither the disc nor the compass; Eva’s eyes went to the photograph and then, unmistakably, toward the platform." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1379 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Underneath lay wet soot," |
| | adverbCount | 39 | | adverbRatio | 0.028281363306744016 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.009427121102248005 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 229 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 229 | | mean | 9.23 | | std | 6.58 | | cv | 0.713 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 12 | | 3 | 16 | | 4 | 7 | | 5 | 17 | | 6 | 10 | | 7 | 6 | | 8 | 31 | | 9 | 2 | | 10 | 22 | | 11 | 16 | | 12 | 6 | | 13 | 2 | | 14 | 3 | | 15 | 7 | | 16 | 24 | | 17 | 14 | | 18 | 11 | | 19 | 6 | | 20 | 17 | | 21 | 4 | | 22 | 18 | | 23 | 22 | | 24 | 3 | | 25 | 12 | | 26 | 3 | | 27 | 5 | | 28 | 18 | | 29 | 10 | | 30 | 15 | | 31 | 9 | | 32 | 10 | | 33 | 11 | | 34 | 10 | | 35 | 7 | | 36 | 15 | | 37 | 15 | | 38 | 4 | | 39 | 2 | | 40 | 3 | | 41 | 12 | | 42 | 5 | | 43 | 7 | | 44 | 7 | | 45 | 9 | | 46 | 6 | | 47 | 9 | | 48 | 18 | | 49 | 8 |
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| 53.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.34934497816593885 | | totalSentences | 229 | | uniqueOpeners | 80 | |
| 24.69% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 135 | | matches | | 0 | "Almost none came back." |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 135 | | matches | | 0 | "She went down the service" | | 1 | "His theory arrived before his" | | 2 | "She had last seen it" | | 3 | "Her eyes were green behind" | | 4 | "She stepped onto the platform." | | 5 | "His throat had been opened" | | 6 | "His left hand was closed" | | 7 | "His shoes were expensive and" | | 8 | "Its casing had gone green" | | 9 | "It was not pointing north." | | 10 | "He was not a stupid" | | 11 | "He simply preferred scenes that" | | 12 | "Her boots were wet, the" | | 13 | "She had brought neither the" | | 14 | "she said at last" | | 15 | "They were folded so tightly" | | 16 | "It could also teach them" | | 17 | "She held the lamp low." | | 18 | "They crossed and recrossed between" | | 19 | "He looked at her." |
| | ratio | 0.252 | |
| 63.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 107 | | totalSentences | 135 | | matches | | 0 | "The station had been closed" | | 1 | "Quinn smelled it beneath the" | | 2 | "She went down the service" | | 3 | "Rain jewelled her closely cropped" | | 4 | "DS Grant waited beside the" | | 5 | "His theory arrived before his" | | 6 | "Grant handed her an evidence" | | 7 | "Quinn checked her worn leather" | | 8 | "Grant looked toward the old" | | 9 | "A worn leather satchel rested" | | 10 | "Quinn knew the face." | | 11 | "She had last seen it" | | 12 | "Eva Kowalski, childhood friend, occult" | | 13 | "Kowalski looked up." | | 14 | "Her eyes were green behind" | | 15 | "That interested Quinn." | | 16 | "She stepped onto the platform." | | 17 | "Venn lay on his back" | | 18 | "His throat had been opened" | | 19 | "Blood had soaked his shirt" |
| | ratio | 0.793 | |
| 74.07% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 135 | | matches | | 0 | "Whoever had cleared the stalls" | | 1 | "Now she wondered whether she" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 1 | | matches | | 0 | "There were broad boots, narrow soles, one long print that might have been bare except for its unnatural width." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 95 | | tagDensity | 0.158 | | leniency | 0.316 | | rawRatio | 0 | | effectiveRatio | 0 | |