| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 68 | | tagDensity | 0.265 | | leniency | 0.529 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1792 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "softly" | | 1 | "carefully" | | 2 | "slowly" | | 3 | "lightly" |
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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) | |
| 83.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1792 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "flicked" | | 2 | "standard" | | 3 | "etched" | | 4 | "flickered" | | 5 | "shattered" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
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| | highlights | | 0 | "eyes widened" | | 1 | "eyes narrowed" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 144 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 144 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 194 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1792 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1286 | | uniqueNames | 9 | | maxNameDensity | 2.26 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 29 | | Council | 1 | | Tube | 1 | | One | 2 | | Grey | 1 | | Kowalski | 1 | | Eva | 17 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Council" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0.372 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 86 | | 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 | 1792 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 194 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 93 | | mean | 19.27 | | std | 18.39 | | cv | 0.954 | | sampleLengths | | 0 | 3 | | 1 | 61 | | 2 | 5 | | 3 | 72 | | 4 | 36 | | 5 | 68 | | 6 | 7 | | 7 | 7 | | 8 | 40 | | 9 | 27 | | 10 | 5 | | 11 | 79 | | 12 | 14 | | 13 | 2 | | 14 | 29 | | 15 | 23 | | 16 | 9 | | 17 | 13 | | 18 | 58 | | 19 | 6 | | 20 | 10 | | 21 | 38 | | 22 | 6 | | 23 | 10 | | 24 | 10 | | 25 | 29 | | 26 | 58 | | 27 | 7 | | 28 | 10 | | 29 | 62 | | 30 | 8 | | 31 | 23 | | 32 | 11 | | 33 | 15 | | 34 | 12 | | 35 | 3 | | 36 | 25 | | 37 | 4 | | 38 | 5 | | 39 | 18 | | 40 | 17 | | 41 | 40 | | 42 | 14 | | 43 | 63 | | 44 | 2 | | 45 | 13 | | 46 | 39 | | 47 | 22 | | 48 | 9 | | 49 | 27 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 144 | | matches | | 0 | "been repainted" | | 1 | "been placed" | | 2 | "been left" | | 3 | "been swept" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 212 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 194 | | ratio | 0.005 | | matches | | 0 | "One side bore a spiral mark; the other, a sliver of bone had been inlaid into a groove and sealed with pitch." |
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| 99.02% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1289 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 53 | | adverbRatio | 0.041117145073700546 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008533747090768037 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 194 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 194 | | mean | 9.24 | | std | 7.72 | | cv | 0.836 | | sampleLengths | | 0 | 3 | | 1 | 28 | | 2 | 33 | | 3 | 5 | | 4 | 9 | | 5 | 18 | | 6 | 20 | | 7 | 25 | | 8 | 14 | | 9 | 2 | | 10 | 3 | | 11 | 2 | | 12 | 15 | | 13 | 8 | | 14 | 3 | | 15 | 2 | | 16 | 9 | | 17 | 27 | | 18 | 19 | | 19 | 4 | | 20 | 3 | | 21 | 3 | | 22 | 4 | | 23 | 5 | | 24 | 35 | | 25 | 18 | | 26 | 9 | | 27 | 5 | | 28 | 8 | | 29 | 21 | | 30 | 8 | | 31 | 24 | | 32 | 18 | | 33 | 8 | | 34 | 6 | | 35 | 2 | | 36 | 15 | | 37 | 14 | | 38 | 3 | | 39 | 20 | | 40 | 3 | | 41 | 6 | | 42 | 13 | | 43 | 18 | | 44 | 2 | | 45 | 10 | | 46 | 14 | | 47 | 4 | | 48 | 2 | | 49 | 1 |
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| 46.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.32989690721649484 | | totalSentences | 194 | | uniqueOpeners | 64 | |
| 27.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 123 | | matches | | 0 | "Then another voice cut through" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 123 | | matches | | 0 | "He let the torch beam" | | 1 | "Her worn leather watch creaked" | | 2 | "His right hand still clutched" | | 3 | "She looked up." | | 4 | "She tipped her torch towards" | | 5 | "She reached for the cloth" | | 6 | "She sniffed it once." | | 7 | "Her torch dipped to the" | | 8 | "She crouched lower and pressed" | | 9 | "He gave a curt nod" | | 10 | "Her round glasses caught the" | | 11 | "Her curly red hair had" | | 12 | "She tucked it behind her" | | 13 | "Her eyes had already moved" | | 14 | "She swallowed once, then leaned" | | 15 | "She nodded towards the cloth" | | 16 | "She drew a knife from" | | 17 | "Her fingers met paper." | | 18 | "It looked ordinary enough, if" | | 19 | "She turned the disk over." |
| | ratio | 0.179 | |
| 29.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 123 | | matches | | 0 | "Detective Harlow Quinn put a" | | 1 | "He let the torch beam" | | 2 | "Quinn crouched by the corpse" | | 3 | "Her worn leather watch creaked" | | 4 | "The dead man lay half" | | 5 | "Camden Council had once given" | | 6 | "The body wore a black" | | 7 | "His right hand still clutched" | | 8 | "Quinn took in the details" | | 9 | "Throat opened cleanly." | | 10 | "The dead man’s shoes, polished" | | 11 | "A thin ring of chalk" | | 12 | "A trainer sole." | | 13 | "She looked up." | | 14 | "The sergeant shifted his weight." | | 15 | "Quinn rose slowly, her sharp" | | 16 | "Stalls stood behind hanging sheets" | | 17 | "Some had been left half-packed," | | 18 | "A market gone to ground" | | 19 | "Quinn turned her attention back" |
| | ratio | 0.862 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 73.17% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 4 | | matches | | 0 | "The dead man lay half in shadow beside a tiled wall that had been repainted three times and vandalised four." | | 1 | "The dead man’s shoes, polished and expensive, sat on the stone platform with their toes angled out, as if he had been placed carefully rather than dropped." | | 2 | "Eva Kowalski stepped out from behind a hanging curtain of black beads, clutching her worn leather satchel to her side as if it held her ribs together." | | 3 | "The sigils around the edge formed a pattern she had seen once before, in a file that had vanished from her desk three years ago, the same week DS Morris had dis…" |
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| 97.22% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva said, voice clipped now" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 68 | | tagDensity | 0.176 | | leniency | 0.353 | | rawRatio | 0.083 | | effectiveRatio | 0.029 | |