| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1119 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 46.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1119 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "shattered" | | 1 | "pounding" | | 2 | "flicked" | | 3 | "unreadable" | | 4 | "tracing" | | 5 | "pulse" | | 6 | "velvet" | | 7 | "weight" | | 8 | "perfect" | | 9 | "whisper" |
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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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 87 | | filterMatches | | | 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 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1110 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 770 | | uniqueNames | 12 | | maxNameDensity | 1.43 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Ptolemy | 3 | | Moreau | 1 | | Marseille | 1 | | Brick | 2 | | Lane | 2 | | Lips | 1 | | See | 1 | | Eva | 6 | | Norse | 1 | | Heat | 1 | | Lucien | 11 | | Leverage | 1 |
| | persons | | 0 | "Ptolemy" | | 1 | "Moreau" | | 2 | "Lips" | | 3 | "Eva" | | 4 | "Heat" | | 5 | "Lucien" |
| | places | | 0 | "Marseille" | | 1 | "Brick" | | 2 | "Lane" |
| | globalScore | 0.786 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 1110 | | 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 | 58 | | mean | 19.14 | | std | 9.94 | | cv | 0.52 | | sampleLengths | | 0 | 53 | | 1 | 14 | | 2 | 31 | | 3 | 34 | | 4 | 20 | | 5 | 3 | | 6 | 26 | | 7 | 28 | | 8 | 20 | | 9 | 30 | | 10 | 25 | | 11 | 12 | | 12 | 18 | | 13 | 28 | | 14 | 18 | | 15 | 26 | | 16 | 7 | | 17 | 10 | | 18 | 27 | | 19 | 12 | | 20 | 37 | | 21 | 15 | | 22 | 28 | | 23 | 15 | | 24 | 18 | | 25 | 21 | | 26 | 7 | | 27 | 30 | | 28 | 21 | | 29 | 8 | | 30 | 11 | | 31 | 21 | | 32 | 22 | | 33 | 18 | | 34 | 15 | | 35 | 27 | | 36 | 10 | | 37 | 25 | | 38 | 37 | | 39 | 20 | | 40 | 13 | | 41 | 9 | | 42 | 12 | | 43 | 32 | | 44 | 31 | | 45 | 4 | | 46 | 20 | | 47 | 7 | | 48 | 13 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 141 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 116 | | ratio | 0.06 | | matches | | 0 | "The rapid pounding didn't belong on the street—no, that beat belonged to the front door." | | 1 | "Lips curled—almost amused, but somehow even that had an edge." | | 2 | "A kettle shrieked—Eva’s usual peace offering, always the right side of boiling." | | 3 | "Black one—hungry." | | 4 | "The flat's walls pressed in—the curry-house below offered muffled comfort, but even Brick Lane couldn’t keep the past out." | | 5 | "I jerked away from Lucien’s breath, every nerve lit up—then realised too late he’d blocked the door with his body, his cane now horizontal, trapping me in place." | | 6 | "Thudding feet on the restaurant stairs, something heavier than a man—Lucien and I locked eyes, every promise and threat hanging unfinished." |
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| 94.56% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 779 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.04621309370988447 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005134788189987163 | |
| 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 | 9.57 | | std | 5.97 | | cv | 0.624 | | sampleLengths | | 0 | 4 | | 1 | 8 | | 2 | 23 | | 3 | 3 | | 4 | 15 | | 5 | 3 | | 6 | 3 | | 7 | 8 | | 8 | 13 | | 9 | 18 | | 10 | 10 | | 11 | 9 | | 12 | 15 | | 13 | 6 | | 14 | 14 | | 15 | 3 | | 16 | 26 | | 17 | 17 | | 18 | 11 | | 19 | 13 | | 20 | 7 | | 21 | 9 | | 22 | 10 | | 23 | 11 | | 24 | 9 | | 25 | 4 | | 26 | 12 | | 27 | 5 | | 28 | 7 | | 29 | 10 | | 30 | 8 | | 31 | 12 | | 32 | 16 | | 33 | 18 | | 34 | 21 | | 35 | 3 | | 36 | 2 | | 37 | 7 | | 38 | 4 | | 39 | 6 | | 40 | 2 | | 41 | 25 | | 42 | 12 | | 43 | 12 | | 44 | 10 | | 45 | 15 | | 46 | 9 | | 47 | 6 | | 48 | 7 | | 49 | 11 |
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| 70.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.43103448275862066 | | totalSentences | 116 | | uniqueOpeners | 50 | |
| 87.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 76 | | matches | | 0 | "Too late to shut the" | | 1 | "Too many miles since Marseille," |
| | ratio | 0.026 | |
| 4.21% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 76 | | matches | | 0 | "I ducked to swerve a" | | 1 | "I shoved books off the" | | 2 | "His cane’s ivory handle reflected" | | 3 | "He stepped inside without waiting," | | 4 | "I muttered, clicking the last" | | 5 | "His pressure warped the cramped" | | 6 | "He never wasted syllables." | | 7 | "My skin prickled, memories slinking" | | 8 | "I forced a light tone." | | 9 | "He unclipped a cufflink, rolling" | | 10 | "I stalked to the kitchenette," | | 11 | "He settled against the kitchen" | | 12 | "He took in the flat," | | 13 | "I passed a chipped mug" | | 14 | "He pulled the mug away," | | 15 | "His cane clicked twice on" | | 16 | "I held my own spine" | | 17 | "He let them work like" | | 18 | "He sipped his tea." | | 19 | "He let the cup hang," |
| | ratio | 0.539 | |
| 6.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 76 | | matches | | 0 | "Glass shattered behind me." | | 1 | "Eva, somewhere in the flat," | | 2 | "I ducked to swerve a" | | 3 | "Breath came tight." | | 4 | "The rapid pounding didn't belong" | | 5 | "That was a knock you" | | 6 | "I shoved books off the" | | 7 | "The door stuttered on the" | | 8 | "Lucien Moreau filled the frame," | | 9 | "Amber and black eyes snagged" | | 10 | "His cane’s ivory handle reflected" | | 11 | "He stepped inside without waiting," | | 12 | "I muttered, clicking the last" | | 13 | "His pressure warped the cramped" | | 14 | "Ptolemy slinked between his feet," | | 15 | "Lips curled—almost amused, but somehow" | | 16 | "He never wasted syllables." | | 17 | "My skin prickled, memories slinking" | | 18 | "I forced a light tone." | | 19 | "He unclipped a cufflink, rolling" |
| | ratio | 0.908 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "I ducked to swerve a stack of books teetering on the hall tiles, my knuckles ghosting the bone-shaped scar peeking from my sleeve." | | 1 | "Ptolemy sprang on the table, claws catching Lucien’s cuff, startling us both." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 3 | | matches | | 0 | "I passed, my fingers brushing his" | | 1 | "I didn’t, but his gaze flicked there anyway" | | 2 | "I snorted, teeth grazing mug rim" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "I muttered (mutter)" | | 1 | "I snapped (snap)" |
| | dialogueSentences | 40 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0.4 | | effectiveRatio | 0.1 | |