| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 80.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1035 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "carefully" | | 1 | "slightly" | | 2 | "really" | | 3 | "very" |
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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) | |
| 13.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1035 | | totalAiIsms | 18 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | word | "down her spine" | | count | 1 |
| | 14 | |
| | highlights | | 0 | "gloom" | | 1 | "furrowed" | | 2 | "scanning" | | 3 | "racing" | | 4 | "churning" | | 5 | "quickened" | | 6 | "whisper" | | 7 | "furrowing" | | 8 | "scanned" | | 9 | "etched" | | 10 | "quivered" | | 11 | "intrigue" | | 12 | "chill" | | 13 | "down her spine" | | 14 | "weight" |
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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 narrowed" | | 1 | "eyes widened" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 56 | | matches | (empty) | |
| 66.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 56 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1034 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 657 | | uniqueNames | 10 | | maxNameDensity | 1.98 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Patel" | | discoveredNames | | Tube | 2 | | Camden | 1 | | Detective | 2 | | Harlow | 1 | | Quinn | 13 | | Samir | 1 | | Patel | 13 | | Veil | 1 | | Market | 1 | | Morris | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Samir" | | 4 | "Patel" | | 5 | "Morris" |
| | places | | | globalScore | 0.511 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1034 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 33.35 | | std | 16.69 | | cv | 0.5 | | sampleLengths | | 0 | 67 | | 1 | 43 | | 2 | 27 | | 3 | 52 | | 4 | 20 | | 5 | 66 | | 6 | 17 | | 7 | 23 | | 8 | 38 | | 9 | 23 | | 10 | 30 | | 11 | 45 | | 12 | 30 | | 13 | 34 | | 14 | 28 | | 15 | 37 | | 16 | 78 | | 17 | 12 | | 18 | 22 | | 19 | 15 | | 20 | 31 | | 21 | 26 | | 22 | 39 | | 23 | 14 | | 24 | 30 | | 25 | 21 | | 26 | 34 | | 27 | 66 | | 28 | 25 | | 29 | 15 | | 30 | 26 |
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| 86.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 56 | | matches | | 0 | "was furrowed" | | 1 | "were shoved" | | 2 | "was dressed" | | 3 | "was etched" |
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| 91.60% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 123 | | matches | | 0 | "were listening" | | 1 | "were closing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 78 | | ratio | 0.013 | | matches | | 0 | "The air in the abandoned Tube station beneath Camden was thick with the scent of damp concrete and something else—something metallic that clung to the back of Detective Harlow Quinn’s throat." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 661 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.0226928895612708 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.006051437216338881 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 13.26 | | std | 6.85 | | cv | 0.516 | | sampleLengths | | 0 | 31 | | 1 | 15 | | 2 | 21 | | 3 | 21 | | 4 | 16 | | 5 | 6 | | 6 | 7 | | 7 | 20 | | 8 | 18 | | 9 | 24 | | 10 | 10 | | 11 | 11 | | 12 | 9 | | 13 | 12 | | 14 | 15 | | 15 | 21 | | 16 | 9 | | 17 | 9 | | 18 | 4 | | 19 | 13 | | 20 | 7 | | 21 | 10 | | 22 | 1 | | 23 | 2 | | 24 | 3 | | 25 | 21 | | 26 | 17 | | 27 | 6 | | 28 | 13 | | 29 | 4 | | 30 | 4 | | 31 | 26 | | 32 | 10 | | 33 | 30 | | 34 | 5 | | 35 | 11 | | 36 | 19 | | 37 | 8 | | 38 | 14 | | 39 | 12 | | 40 | 16 | | 41 | 12 | | 42 | 13 | | 43 | 7 | | 44 | 17 | | 45 | 11 | | 46 | 19 | | 47 | 19 | | 48 | 11 | | 49 | 18 |
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| 67.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.41025641025641024 | | totalSentences | 78 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 94.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 54 | | matches | | 0 | "She stepped carefully over the" | | 1 | "His brow was furrowed, and" | | 2 | "she replied, her tone clipped" | | 3 | "She had seen enough in" | | 4 | "she murmured, her mind racing" | | 5 | "She reached out, brushing her" | | 6 | "she asked, her gaze flicking" | | 7 | "he hesitated, searching for the" | | 8 | "She had heard whispers of" | | 9 | "It was worn but not" | | 10 | "She stood, her gaze sweeping" | | 11 | "She scanned the area, her" | | 12 | "she said, holding the compass" | | 13 | "she replied, her mind racing" | | 14 | "She had lost him to" | | 15 | "She would uncover the truth," | | 16 | "she said, her voice steady" |
| | ratio | 0.315 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 54 | | matches | | 0 | "The air in the abandoned" | | 1 | "She stepped carefully over the" | | 2 | "The flickering fluorescent lights overhead" | | 3 | "A voice called from the" | | 4 | "His brow was furrowed, and" | | 5 | "she replied, her tone clipped" | | 6 | "She had seen enough in" | | 7 | "Patel gestured toward the far" | | 8 | "The victim, a young man" | | 9 | "Quinn approached, her sharp jawline" | | 10 | "Patel said, his voice low" | | 11 | "Quinn knelt beside the body," | | 12 | "The victim’s hands were empty," | | 13 | "The position of his limbs" | | 14 | "she murmured, her mind racing" | | 15 | "Patel leaned closer, squinting." | | 16 | "Quinn countered, her voice steady" | | 17 | "She reached out, brushing her" | | 18 | "Patel said, glancing around as" | | 19 | "Quinn straightened, her mind still" |
| | ratio | 0.926 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 2 | | matches | | 0 | "She had seen enough in her eighteen years on the force to be skeptical of anything that sounded too outrageous." | | 1 | "Quinn felt the weight of her partner’s loss pressing down on her, the memory of DS Morris haunting her thoughts." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 12 | | matches | | 0 | "she replied, her tone clipped" | | 1 | "Patel said, his voice low" | | 2 | "she murmured, her mind racing" | | 3 | "Quinn countered, her voice steady" | | 4 | "she asked, her gaze flicking to the ground" | | 5 | "Patel replied, his voice dropping to a conspiratorial whisper" | | 6 | "Patel suggested, his brow furrowing deeper" | | 7 | "Quinn said, her voice firm" | | 8 | "she replied, her mind racing" | | 9 | "Patel added, his voice dropping again" | | 10 | "Quinn said, her mind racing with possibilities" | | 11 | "she said, her voice steady" |
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| 75.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 3 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Patel suggested (suggest)" | | 2 | "Patel added (add)" |
| | dialogueSentences | 40 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.2 | | effectiveRatio | 0.15 | |