| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1607 | | 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) | |
| 78.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1607 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "weight" | | 1 | "flicked" | | 2 | "traced" | | 3 | "silence" | | 4 | "eyebrow" | | 5 | "throbbed" | | 6 | "glinting" |
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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 | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 102 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1607 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.85% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1173 | | uniqueNames | 15 | | maxNameDensity | 1.02 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Brick | 1 | | Lane | 1 | | Moreau | 1 | | Rory | 11 | | Eva | 3 | | Ptolemy | 6 | | French | 1 | | Brendan | 1 | | Carter | 1 | | Cardiff | 1 | | London | 1 | | Evan | 1 | | Latin | 1 | | Marseille | 1 | | Lucien | 12 |
| | persons | | 0 | "Moreau" | | 1 | "Rory" | | 2 | "Eva" | | 3 | "Ptolemy" | | 4 | "Brendan" | | 5 | "Carter" | | 6 | "Evan" | | 7 | "Lucien" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Cardiff" | | 3 | "London" | | 4 | "Marseille" |
| | globalScore | 0.988 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | 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 | 1607 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 151 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 78 | | mean | 20.6 | | std | 19.54 | | cv | 0.948 | | sampleLengths | | 0 | 60 | | 1 | 6 | | 2 | 53 | | 3 | 14 | | 4 | 4 | | 5 | 1 | | 6 | 7 | | 7 | 11 | | 8 | 39 | | 9 | 10 | | 10 | 9 | | 11 | 39 | | 12 | 71 | | 13 | 65 | | 14 | 11 | | 15 | 5 | | 16 | 10 | | 17 | 54 | | 18 | 13 | | 19 | 6 | | 20 | 12 | | 21 | 28 | | 22 | 5 | | 23 | 13 | | 24 | 10 | | 25 | 28 | | 26 | 13 | | 27 | 14 | | 28 | 47 | | 29 | 5 | | 30 | 10 | | 31 | 46 | | 32 | 3 | | 33 | 1 | | 34 | 19 | | 35 | 34 | | 36 | 12 | | 37 | 18 | | 38 | 39 | | 39 | 10 | | 40 | 15 | | 41 | 12 | | 42 | 69 | | 43 | 12 | | 44 | 5 | | 45 | 8 | | 46 | 49 | | 47 | 6 | | 48 | 13 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 184 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 151 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1178 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.016129032258064516 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 10.64 | | std | 6.86 | | cv | 0.644 | | sampleLengths | | 0 | 11 | | 1 | 11 | | 2 | 20 | | 3 | 8 | | 4 | 1 | | 5 | 5 | | 6 | 4 | | 7 | 6 | | 8 | 15 | | 9 | 15 | | 10 | 10 | | 11 | 3 | | 12 | 2 | | 13 | 8 | | 14 | 14 | | 15 | 4 | | 16 | 1 | | 17 | 7 | | 18 | 11 | | 19 | 8 | | 20 | 13 | | 21 | 18 | | 22 | 10 | | 23 | 9 | | 24 | 8 | | 25 | 9 | | 26 | 22 | | 27 | 8 | | 28 | 7 | | 29 | 9 | | 30 | 27 | | 31 | 12 | | 32 | 8 | | 33 | 13 | | 34 | 24 | | 35 | 14 | | 36 | 14 | | 37 | 11 | | 38 | 5 | | 39 | 10 | | 40 | 16 | | 41 | 14 | | 42 | 13 | | 43 | 11 | | 44 | 13 | | 45 | 6 | | 46 | 12 | | 47 | 2 | | 48 | 7 | | 49 | 19 |
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| 37.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.2781456953642384 | | totalSentences | 151 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 45.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 94 | | matches | | 0 | "His charcoal suit held no" | | 1 | "His weight rested on the" | | 2 | "He smiled with one corner" | | 3 | "His gaze dropped to the" | | 4 | "He lifted a pile of" | | 5 | "He lowered himself into the" | | 6 | "She kept her back to" | | 7 | "Her straight black hair fell" | | 8 | "She pushed it back behind" | | 9 | "Her blue eyes met his" | | 10 | "His amber eye burned in" | | 11 | "His black eye drank it." | | 12 | "Her nails bit into the" | | 13 | "He crossed the three steps" | | 14 | "He looked too large among" | | 15 | "She bent and scooped a" | | 16 | "His French accent thickened round" | | 17 | "He reached out and his" | | 18 | "She ran the tap and" | | 19 | "She filled the kettle and" |
| | ratio | 0.436 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 94 | | matches | | 0 | "The third deadbolt gave under" | | 1 | "Rory pulled the door in" | | 2 | "Voices from Brick Lane pushed" | | 3 | "Bass from a passing car." | | 4 | "Someone laughed too loud." | | 5 | "Lucien Moreau stood on the" | | 6 | "His charcoal suit held no" | | 7 | "His weight rested on the" | | 8 | "Both fixed on the gap" | | 9 | "Ptolemy wound round Rory's ankle" | | 10 | "He smiled with one corner" | | 11 | "His gaze dropped to the" | | 12 | "The small crescent mark on" | | 13 | "Rory slid the chain free" | | 14 | "The door swung wide on" | | 15 | "Lucien crossed the threshold without" | | 16 | "Every surface in Eva's one-bedroom" | | 17 | "Books leaned in towers from" | | 18 | "Scrolls tied with red cord" | | 19 | "The single window over the" |
| | ratio | 0.947 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 1 | | matches | | 0 | "Rory set the notes down on the worktop next to a chipped mug that read BEST BARRISTER in faded gold." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |