| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 52 | | tagDensity | 0.269 | | leniency | 0.538 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1213 | | 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) | |
| 83.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1213 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "silence" | | 1 | "familiar" | | 2 | "whisper" | | 3 | "weight" |
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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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1213 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 821 | | uniqueNames | 12 | | maxNameDensity | 0.61 | | worstName | "Lucien" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 3 | | Moreau | 1 | | Ptolemy | 3 | | Frenchman | 1 | | Eva | 3 | | Golden | 1 | | Empress | 1 | | Marseilles | 1 | | Lane | 1 | | Friday | 1 | | Aurora | 1 | | Lucien | 5 |
| | persons | | 0 | "Rory" | | 1 | "Moreau" | | 2 | "Ptolemy" | | 3 | "Eva" | | 4 | "Lucien" |
| | places | | 0 | "Golden" | | 1 | "Marseilles" | | 2 | "Lane" | | 3 | "Friday" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | 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 | 1213 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 22.46 | | std | 21.92 | | cv | 0.976 | | sampleLengths | | 0 | 40 | | 1 | 44 | | 2 | 5 | | 3 | 28 | | 4 | 12 | | 5 | 5 | | 6 | 3 | | 7 | 6 | | 8 | 24 | | 9 | 3 | | 10 | 41 | | 11 | 15 | | 12 | 80 | | 13 | 10 | | 14 | 6 | | 15 | 8 | | 16 | 85 | | 17 | 2 | | 18 | 2 | | 19 | 2 | | 20 | 41 | | 21 | 6 | | 22 | 24 | | 23 | 3 | | 24 | 35 | | 25 | 4 | | 26 | 9 | | 27 | 4 | | 28 | 5 | | 29 | 30 | | 30 | 50 | | 31 | 14 | | 32 | 29 | | 33 | 5 | | 34 | 43 | | 35 | 63 | | 36 | 16 | | 37 | 10 | | 38 | 5 | | 39 | 56 | | 40 | 6 | | 41 | 49 | | 42 | 17 | | 43 | 4 | | 44 | 43 | | 45 | 3 | | 46 | 64 | | 47 | 8 | | 48 | 45 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 129 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 108 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 826 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.01937046004842615 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0036319612590799033 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 11.23 | | std | 9.81 | | cv | 0.873 | | sampleLengths | | 0 | 27 | | 1 | 13 | | 2 | 8 | | 3 | 9 | | 4 | 6 | | 5 | 21 | | 6 | 5 | | 7 | 26 | | 8 | 2 | | 9 | 9 | | 10 | 3 | | 11 | 5 | | 12 | 3 | | 13 | 3 | | 14 | 3 | | 15 | 16 | | 16 | 4 | | 17 | 4 | | 18 | 3 | | 19 | 13 | | 20 | 5 | | 21 | 23 | | 22 | 3 | | 23 | 12 | | 24 | 23 | | 25 | 7 | | 26 | 28 | | 27 | 4 | | 28 | 18 | | 29 | 4 | | 30 | 6 | | 31 | 6 | | 32 | 8 | | 33 | 2 | | 34 | 17 | | 35 | 26 | | 36 | 16 | | 37 | 24 | | 38 | 2 | | 39 | 2 | | 40 | 2 | | 41 | 8 | | 42 | 33 | | 43 | 6 | | 44 | 11 | | 45 | 4 | | 46 | 6 | | 47 | 3 | | 48 | 3 | | 49 | 9 |
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| 74.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4722222222222222 | | totalSentences | 108 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 59 | | matches | | 0 | "Then, traitorously, he purred." | | 1 | "Then the second." |
| | ratio | 0.034 | |
| 64.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 59 | | matches | | 0 | "She was wrong, and being" | | 1 | "She kept her shoulder behind" | | 2 | "She watched him spend it." | | 3 | "She stepped aside." | | 4 | "He entered the way he" | | 5 | "She shot all three bolts" | | 6 | "Her own rain jacket steamed" | | 7 | "She saw it land, the" | | 8 | "He watched her soak the" | | 9 | "He didn't flinch when the" | | 10 | "He had never flinched." | | 11 | "She used to find that" | | 12 | "Her hands stopped." | | 13 | "She pressed the gauze harder" | | 14 | "He breathed out through his" | | 15 | "His voice stayed level, which" | | 16 | "She swapped it out, and" | | 17 | "His hand came up, unhurried," | | 18 | "His thumb found the small" | | 19 | "He rose from the chair." |
| | ratio | 0.39 | |
| 19.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 59 | | matches | | 0 | "The third deadbolt stuck, the" | | 1 | "She was wrong, and being" | | 2 | "Lucien Moreau filled the doorway" | | 3 | "Platinum hair slipped loose from" | | 4 | "Charcoal suit, wet at the" | | 5 | "The mismatched eyes found her," | | 6 | "She kept her shoulder behind" | | 7 | "Ptolemy shouldered past her ankle," | | 8 | "The tabby's tail bottle-brushed." | | 9 | "Lucien's smile cost him something" | | 10 | "She watched him spend it." | | 11 | "She stepped aside." | | 12 | "He entered the way he" | | 13 | "She shot all three bolts" | | 14 | "The flat did its usual" | | 15 | "Her own rain jacket steamed" | | 16 | "She saw it land, the" | | 17 | "The cut ran along his" | | 18 | "He watched her soak the" | | 19 | "He didn't flinch when the" |
| | ratio | 0.881 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 28.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 4 | | matches | | 0 | "He entered the way he always did, as if rooms rearranged themselves to suit him, cane tapping a soft tip-tap across the floorboards." | | 1 | "She swapped it out, and her fingers found the tape, and his skin was warm under her hands, familiar in a way that made her chest do something unhelpful." | | 2 | "The street noise died with it, the mopeds, the drunks, theBrick Lane Friday, all of it gone, as if someone had pressed the world against wool." | | 3 | "The ivory handle of his cane twisted, and a slim blade slid out of it, whisper-quiet, and he put himself between her and the door with the ease of a man who had…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 52 | | tagDensity | 0.096 | | leniency | 0.192 | | rawRatio | 0.2 | | effectiveRatio | 0.038 | |