| 85.71% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 2 | | adverbTags | | 0 | "Patel rocked back [back]" | | 1 | "She nodded slowly [slowly]" |
| | dialogueSentences | 35 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.2 | | effectiveRatio | 0.114 | |
| 86.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1097 | | totalAiIsmAdverbs | 3 | | 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) | |
| 81.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1097 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "pristine" | | 1 | "footsteps" | | 2 | "pulse" | | 3 | "perfect" |
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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 | 0 | | narrationSentences | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 20 | | totalWords | 1097 | | ratio | 0.018 | | matches | | 0 | "Walk the edges before you touch the middle. The scene tells you a story. Don't let the corpse interrupt." | | 1 | "Trembling" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.94% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 899 | | uniqueNames | 8 | | maxNameDensity | 1 | | worstName | "Patel" | | maxWindowNameDensity | 2 | | worstWindowName | "Patel" | | discoveredNames | | Tube | 1 | | London | 1 | | Detective | 1 | | Sergeant | 1 | | Patel | 9 | | Quinn | 6 | | Morris | 3 | | Camden | 2 |
| | persons | | 0 | "Sergeant" | | 1 | "Patel" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Camden" |
| | places | | | globalScore | 0.999 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 1097 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 23.34 | | std | 19.4 | | cv | 0.831 | | sampleLengths | | 0 | 7 | | 1 | 59 | | 2 | 14 | | 3 | 42 | | 4 | 4 | | 5 | 40 | | 6 | 46 | | 7 | 6 | | 8 | 33 | | 9 | 4 | | 10 | 6 | | 11 | 13 | | 12 | 5 | | 13 | 66 | | 14 | 17 | | 15 | 2 | | 16 | 31 | | 17 | 59 | | 18 | 20 | | 19 | 5 | | 20 | 15 | | 21 | 24 | | 22 | 61 | | 23 | 23 | | 24 | 22 | | 25 | 3 | | 26 | 7 | | 27 | 69 | | 28 | 21 | | 29 | 20 | | 30 | 12 | | 31 | 9 | | 32 | 12 | | 33 | 56 | | 34 | 42 | | 35 | 20 | | 36 | 3 | | 37 | 23 | | 38 | 61 | | 39 | 16 | | 40 | 12 | | 41 | 9 | | 42 | 2 | | 43 | 11 | | 44 | 26 | | 45 | 17 | | 46 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 151 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 116 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 899 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.03114571746384872 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006674082313681869 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 9.46 | | std | 7.87 | | cv | 0.832 | | sampleLengths | | 0 | 7 | | 1 | 25 | | 2 | 6 | | 3 | 28 | | 4 | 8 | | 5 | 6 | | 6 | 17 | | 7 | 25 | | 8 | 4 | | 9 | 20 | | 10 | 20 | | 11 | 3 | | 12 | 24 | | 13 | 8 | | 14 | 6 | | 15 | 5 | | 16 | 6 | | 17 | 11 | | 18 | 13 | | 19 | 2 | | 20 | 3 | | 21 | 4 | | 22 | 4 | | 23 | 6 | | 24 | 9 | | 25 | 4 | | 26 | 5 | | 27 | 14 | | 28 | 10 | | 29 | 2 | | 30 | 3 | | 31 | 2 | | 32 | 35 | | 33 | 15 | | 34 | 2 | | 35 | 2 | | 36 | 18 | | 37 | 13 | | 38 | 2 | | 39 | 12 | | 40 | 5 | | 41 | 36 | | 42 | 3 | | 43 | 1 | | 44 | 1 | | 45 | 19 | | 46 | 5 | | 47 | 6 | | 48 | 3 | | 49 | 4 |
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| 76.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.5086206896551724 | | totalSentences | 116 | | uniqueOpeners | 59 | |
| 80.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 83 | | matches | | 0 | "Just the one set." | | 1 | "Then she drew the brass" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 83 | | matches | | 0 | "They drank light the way" | | 1 | "She walked the perimeter of" | | 2 | "Her own, fresh." | | 3 | "He went, and she heard" | | 4 | "She pulled on a glove" | | 5 | "She bent close." | | 6 | "His boots crunched back toward" | | 7 | "He inhaled, frowning." | | 8 | "She nodded slowly" | | 9 | "She studied the man's face." | | 10 | "They were open, and the" | | 11 | "She'd seen that in pressure" | | 12 | "She tilted his head" | | 13 | "He crouched opposite her, eager" | | 14 | "He had no answer for" | | 15 | "She'd taken it from Morris's" | | 16 | "She didn't take it out." | | 17 | "It only did that near" | | 18 | "She made herself release the" | | 19 | "She stood, knees cracking, and" |
| | ratio | 0.265 | |
| 92.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 83 | | matches | | 0 | "The body had no business" | | 1 | "Quinn ducked under the strip" | | 2 | "Camden's old Tube tunnels did" | | 3 | "They drank light the way" | | 4 | "Detective Sergeant Patel crouched beside" | | 5 | "A young man lay sprawled" | | 6 | "Patel rocked back on his" | | 7 | "Quinn said nothing." | | 8 | "She walked the perimeter of" | | 9 | "The scene tells you a" | | 10 | "The dust was the first" | | 11 | "A fine grey film coated" | | 12 | "The caretaker's prints came in" | | 13 | "Her own, fresh." | | 14 | "Patel gestured at the tunnel" | | 15 | "The torch beam tracked the" | | 16 | "The man lay in the" | | 17 | "Patel's voice had lost some" | | 18 | "He went, and she heard" | | 19 | "She pulled on a glove" |
| | ratio | 0.735 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 56.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 4 | | matches | | 0 | "A young man lay sprawled across the disused track, one arm flung out, palm up, fingers curled as though he'd reached for something and missed." | | 1 | "The man lay in the centre of a clean grey sheet, as if he'd been lowered from above by wire, except there was nothing above but the curved black ceiling and for…" | | 2 | "Rigor hadn't fully set in the jaw, yet the fingers had already locked rigid, which was backwards, which meant nothing she could explain standing in a tunnel tha…" | | 3 | "The trainers were spotless, the tread unworn, but the rubber along the edges had blistered, bubbled up in tiny grey beads as though held briefly to a flame." |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "She stood, knees cracking, and let the torch sweep the ceiling" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.057 | | leniency | 0.114 | | rawRatio | 0 | | effectiveRatio | 0 | |