| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 42 | | tagDensity | 0.405 | | leniency | 0.81 | | rawRatio | 0.059 | | effectiveRatio | 0.048 | |
| 79.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1987 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "quickly" | | 3 | "carefully" | | 4 | "gently" |
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| 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) | |
| 62.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1987 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "gloom" | | 1 | "familiar" | | 2 | "perfect" | | 3 | "weight" | | 4 | "chill" | | 5 | "stomach" | | 6 | "pulse" | | 7 | "scanning" | | 8 | "etched" | | 9 | "raced" | | 10 | "echoed" | | 11 | "footsteps" | | 12 | "depths" | | 13 | "calculating" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 144 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 144 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 169 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 7 | | totalWords | 1974 | | ratio | 0.004 | | matches | | 0 | "They watch through the cracks." | | 1 | "Eva Kowalski." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1443 | | uniqueNames | 13 | | maxNameDensity | 1.04 | | worstName | "Patel" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Patel" | | discoveredNames | | Camden | 1 | | Town | 1 | | Harlow | 13 | | Quinn | 3 | | Maglite | 1 | | Underground | 1 | | Patel | 15 | | Tube | 1 | | Morris | 2 | | Small | 1 | | British | 1 | | Museum | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Maglite" | | 3 | "Underground" | | 4 | "Patel" | | 5 | "Morris" | | 6 | "Museum" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "British" |
| | globalScore | 0.98 | | windowScore | 0.833 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | glossingSentenceCount | 8 | | matches | | 0 | "looked like a handful of tiny bones carve" | | 1 | "as though holding something invisible" | | 2 | "looked like fresh blood: *They watch thro" | | 3 | "quite match the objects creating them" | | 4 | "seemed deeper than physics allowed, swallowing her torch beam after only ten feet" | | 5 | "looked like it had been seared into the t" | | 6 | "as though straining toward something just beyond the range of human sight" | | 7 | "as though tracking something that was circling them" |
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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 | 1974 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 169 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 29.03 | | std | 19.79 | | cv | 0.682 | | sampleLengths | | 0 | 72 | | 1 | 18 | | 2 | 9 | | 3 | 65 | | 4 | 12 | | 5 | 44 | | 6 | 82 | | 7 | 23 | | 8 | 4 | | 9 | 42 | | 10 | 42 | | 11 | 12 | | 12 | 19 | | 13 | 21 | | 14 | 40 | | 15 | 48 | | 16 | 41 | | 17 | 24 | | 18 | 10 | | 19 | 67 | | 20 | 46 | | 21 | 60 | | 22 | 41 | | 23 | 11 | | 24 | 5 | | 25 | 4 | | 26 | 43 | | 27 | 11 | | 28 | 9 | | 29 | 37 | | 30 | 21 | | 31 | 44 | | 32 | 47 | | 33 | 3 | | 34 | 7 | | 35 | 41 | | 36 | 42 | | 37 | 41 | | 38 | 9 | | 39 | 20 | | 40 | 46 | | 41 | 11 | | 42 | 48 | | 43 | 20 | | 44 | 57 | | 45 | 47 | | 46 | 9 | | 47 | 69 | | 48 | 31 | | 49 | 13 |
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| 93.08% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 144 | | matches | | 0 | "been arranged" | | 1 | "is opened" | | 2 | "been burned" | | 3 | "been seared" | | 4 | "been written" |
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| 57.55% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 234 | | matches | | 0 | "was studying" | | 1 | "was losing" | | 2 | "was circling" | | 3 | "was having" | | 4 | "was already calculating" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 169 | | ratio | 0.059 | | matches | | 0 | "The abandoned Camden Town ticket hall smelled of damp concrete, rust, and something sharper underneath—like ozone after a lightning strike." | | 1 | "The victim had been arranged with deliberate care—arms at his sides, legs straight, head tilted back so the gaping wound in his neck formed a second, obscene smile." | | 2 | "Harlow glanced at her worn leather watch—5:47 a.m." | | 3 | "The cover was warm—too warm for the underground chill." | | 4 | "Her gaze snagged on something near the far tunnel mouth—a faint shimmer in the air, like heat haze." | | 5 | "That was when she saw it—the faint outline pressed into the grime on the wall." | | 6 | "The needle spun wildly for three seconds, then locked due north—straight into the tunnel where no north should be." | | 7 | "There—a small notch in the concrete that didn’t belong." | | 8 | "From the depths of the tunnel, something that might have been laughter drifted back—dry, ancient, and impossibly amused." | | 9 | "She was already calculating her next move—finding the girl with the curly red hair and round glasses who worked in the British Museum’s restricted archives." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1457 | | adjectiveStacks | 2 | | stackExamples | | 0 | "lay open beside him," | | 1 | "ice-cold against her" |
| | adverbCount | 58 | | adverbRatio | 0.03980782429649966 | | lyAdverbCount | 24 | | lyAdverbRatio | 0.016472203157172273 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 169 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 169 | | mean | 11.68 | | std | 9.43 | | cv | 0.808 | | sampleLengths | | 0 | 20 | | 1 | 23 | | 2 | 29 | | 3 | 6 | | 4 | 12 | | 5 | 7 | | 6 | 2 | | 7 | 18 | | 8 | 24 | | 9 | 14 | | 10 | 9 | | 11 | 9 | | 12 | 3 | | 13 | 43 | | 14 | 1 | | 15 | 7 | | 16 | 28 | | 17 | 47 | | 18 | 8 | | 19 | 9 | | 20 | 6 | | 21 | 4 | | 22 | 42 | | 23 | 3 | | 24 | 6 | | 25 | 5 | | 26 | 28 | | 27 | 9 | | 28 | 3 | | 29 | 19 | | 30 | 8 | | 31 | 1 | | 32 | 7 | | 33 | 5 | | 34 | 4 | | 35 | 36 | | 36 | 3 | | 37 | 6 | | 38 | 8 | | 39 | 4 | | 40 | 13 | | 41 | 14 | | 42 | 7 | | 43 | 9 | | 44 | 25 | | 45 | 19 | | 46 | 5 | | 47 | 10 | | 48 | 2 | | 49 | 43 |
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| 48.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.33727810650887574 | | totalSentences | 169 | | uniqueOpeners | 57 | |
| 76.34% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 131 | | matches | | 0 | "Too many ghosts down here," | | 1 | "Too smooth in places, as" | | 2 | "More like the rustle of" |
| | ratio | 0.023 | |
| 91.76% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 131 | | matches | | 0 | "She hated the Underground at" | | 1 | "His face looked tighter than" | | 2 | "She crouched, careful not to" | | 3 | "It should have spread further" | | 4 | "She pulled on a fresh" | | 5 | "She could see her breath." | | 6 | "She was studying the victim’s" | | 7 | "She lifted the smallest of" | | 8 | "She closed the book quickly." | | 9 | "she asked, keeping her voice" | | 10 | "He pointed his own torch" | | 11 | "They were still faintly glowing" | | 12 | "She recognised one of them" | | 13 | "She stood, scanning the platform" | | 14 | "Her gaze snagged on something" | | 15 | "Her hand moved instinctively to" | | 16 | "She didn’t take it out." | | 17 | "She lifted one again" | | 18 | "She moved away from the" | | 19 | "Her watch ticked louder than" |
| | ratio | 0.321 | |
| 43.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 109 | | totalSentences | 131 | | matches | | 0 | "The abandoned Camden Town ticket" | | 1 | "Detective Harlow Quinn stepped through" | | 2 | "The beam of her Maglite" | | 3 | "She hated the Underground at" | | 4 | "a familiar voice called from" | | 5 | "DS Patel waited at the" | | 6 | "His face looked tighter than" | | 7 | "Harlow descended the last steps" | | 8 | "Patel gestured toward the centre" | | 9 | "Harlow approached slowly, letting her" | | 10 | "The victim had been arranged" | | 11 | "A small leather satchel lay" | | 12 | "She crouched, careful not to" | | 13 | "The metallic scent mixed with" | | 14 | "Harlow’s jaw tightened." | | 15 | "That was the first wrong" | | 16 | "The second was the blood." | | 17 | "It should have spread further" | | 18 | "She pulled on a fresh" | | 19 | "Harlow glanced at her worn" |
| | ratio | 0.832 | |
| 76.34% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 131 | | matches | | 0 | "As though he knew her." | | 1 | "As though he’d been trying" |
| | ratio | 0.015 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 7 | | matches | | 0 | "The beam of her Maglite sliced through the gloom, picking out the graffiti-tagged walls and the faint green glow of emergency exit signs that hadn’t worked in t…" | | 1 | "These hands were clean, palms up, fingers slightly curled as though holding something invisible." | | 2 | "The emergency lighting cast long shadows that didn’t quite match the objects creating them." | | 3 | "Its needle stayed fixed, trembling slightly, as though straining toward something just beyond the range of human sight." | | 4 | "The needle was now spinning slowly, as though tracking something that was circling them." | | 5 | "From the depths of the tunnel, something that might have been laughter drifted back—dry, ancient, and impossibly amused." | | 6 | "She was already calculating her next move—finding the girl with the curly red hair and round glasses who worked in the British Museum’s restricted archives." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 42 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.143 | | effectiveRatio | 0.048 | |