| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.063 | | leniency | 0.125 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.21% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1706 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "carefully" | | 2 | "gently" |
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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) | |
| 88.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1706 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "measured" | | 1 | "etched" | | 2 | "scanning" | | 3 | "silk" |
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| 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 | 76 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 76 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 88 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1706 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 909 | | uniqueNames | 9 | | maxNameDensity | 1.43 | | worstName | "Eva" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Quinn | 12 | | Camden | 1 | | Town | 1 | | Northern | 1 | | Line | 1 | | Eva | 13 | | Kowalski | 1 | | London | 1 | | June | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Line" | | 2 | "Eva" | | 3 | "Kowalski" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "London" | | 3 | "June" |
| | globalScore | 0.785 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 1706 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 26.66 | | std | 22.35 | | cv | 0.838 | | sampleLengths | | 0 | 86 | | 1 | 73 | | 2 | 53 | | 3 | 55 | | 4 | 5 | | 5 | 6 | | 6 | 8 | | 7 | 23 | | 8 | 2 | | 9 | 47 | | 10 | 49 | | 11 | 15 | | 12 | 3 | | 13 | 24 | | 14 | 21 | | 15 | 31 | | 16 | 7 | | 17 | 31 | | 18 | 18 | | 19 | 43 | | 20 | 3 | | 21 | 5 | | 22 | 21 | | 23 | 54 | | 24 | 5 | | 25 | 20 | | 26 | 24 | | 27 | 5 | | 28 | 37 | | 29 | 42 | | 30 | 25 | | 31 | 10 | | 32 | 11 | | 33 | 52 | | 34 | 8 | | 35 | 14 | | 36 | 14 | | 37 | 13 | | 38 | 52 | | 39 | 53 | | 40 | 2 | | 41 | 10 | | 42 | 59 | | 43 | 26 | | 44 | 87 | | 45 | 16 | | 46 | 22 | | 47 | 8 | | 48 | 7 | | 49 | 20 |
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| 91.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 76 | | matches | | 0 | "been drawn" | | 1 | "were smudged" | | 2 | "been rebuttoned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 145 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 106 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 915 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.034972677595628415 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009836065573770493 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 16.09 | | std | 14.55 | | cv | 0.904 | | sampleLengths | | 0 | 24 | | 1 | 19 | | 2 | 20 | | 3 | 23 | | 4 | 22 | | 5 | 26 | | 6 | 25 | | 7 | 13 | | 8 | 23 | | 9 | 9 | | 10 | 8 | | 11 | 13 | | 12 | 16 | | 13 | 6 | | 14 | 20 | | 15 | 5 | | 16 | 6 | | 17 | 8 | | 18 | 23 | | 19 | 2 | | 20 | 28 | | 21 | 19 | | 22 | 2 | | 23 | 11 | | 24 | 2 | | 25 | 4 | | 26 | 13 | | 27 | 5 | | 28 | 12 | | 29 | 15 | | 30 | 3 | | 31 | 24 | | 32 | 5 | | 33 | 16 | | 34 | 31 | | 35 | 7 | | 36 | 12 | | 37 | 12 | | 38 | 7 | | 39 | 18 | | 40 | 3 | | 41 | 8 | | 42 | 12 | | 43 | 20 | | 44 | 3 | | 45 | 5 | | 46 | 21 | | 47 | 17 | | 48 | 22 | | 49 | 15 |
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| 63.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4056603773584906 | | totalSentences | 106 | | uniqueOpeners | 43 | |
| 45.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 73 | | matches | | 0 | "Then she returned to the" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 73 | | matches | | 0 | "Her boots struck the concrete" | | 1 | "His right hand still clenched" | | 2 | "She held a notebook open" | | 3 | "She looked up at Eva." | | 4 | "She walked the length of" | | 5 | "She pocketed it without comment." | | 6 | "She studied the angle of" | | 7 | "She spoke without looking back." | | 8 | "She moved to the side" | | 9 | "She returned to the corpse" | | 10 | "She held it up between" | | 11 | "She walked the perimeter of" | | 12 | "She pinched it free and" | | 13 | "She faced the open tunnel" | | 14 | "She turned the compass in" | | 15 | "She pointed the torch beam" | | 16 | "She hoisted herself, boots finding" | | 17 | "She dropped back down, landing" | | 18 | "She wiped her hands on" |
| | ratio | 0.26 | |
| 35.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 73 | | matches | | 0 | "Harlow Quinn ducked under the" | | 1 | "The air that rolled out" | | 2 | "Her boots struck the concrete" | | 3 | "A single arc light on" | | 4 | "The man wore a long" | | 5 | "His right hand still clenched" | | 6 | "The worn leather satchel hung" | | 7 | "She held a notebook open" | | 8 | "Quinn crossed the platform and" | | 9 | "The wound sat high in" | | 10 | "The flesh around the hole" | | 11 | "She looked up at Eva." | | 12 | "Eva tucked a curl behind" | | 13 | "Eva pointed with the end" | | 14 | "She walked the length of" | | 15 | "A torn London A-Z." | | 16 | "A single bone chip no" | | 17 | "She pocketed it without comment." | | 18 | "The needle spun in lazy," | | 19 | "Eva stepped closer." |
| | ratio | 0.849 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 2 | | matches | | 0 | "Harlow Quinn ducked under the yellow tape that sagged between two rusted barrier poles at the mouth of the sealed Camden Town maintenance entrance." | | 1 | "Fluorescent strips hung askew from the ceiling, casting uneven pools of dirty light across the platform that had once served Northern Line trains." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |