| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2181 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 58.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2181 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "echoed" | | 1 | "pulse" | | 2 | "silence" | | 3 | "warmth" | | 4 | "dancing" | | 5 | "traced" | | 6 | "tension" | | 7 | "weight" | | 8 | "pawn" | | 9 | "trembled" | | 10 | "stark" | | 11 | "vibrated" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
| | 1 | | label | "clenched jaw/fists" | | count | 1 |
| | 2 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "knuckles turned white" | | 1 | "clenched her fist" | | 2 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 404 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 404 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 404 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2177 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 122 | | wordCount | 2175 | | uniqueNames | 15 | | maxNameDensity | 1.79 | | worstName | "You" | | maxWindowNameDensity | 5 | | worstWindowName | "You" | | discoveredNames | | Rory | 33 | | Brick | 2 | | Lane | 2 | | London | 3 | | Marseille | 2 | | Paris | 1 | | Evan | 1 | | Eva | 2 | | Cardiff | 1 | | Lucien | 27 | | Avaros | 2 | | Carter | 2 | | Aurora | 2 | | You | 39 | | Tick | 3 |
| | persons | | 0 | "Rory" | | 1 | "Evan" | | 2 | "Eva" | | 3 | "Lucien" | | 4 | "Carter" | | 5 | "You" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "London" | | 3 | "Marseille" | | 4 | "Paris" | | 5 | "Cardiff" |
| | globalScore | 0.603 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 152 | | 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 | 2177 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 404 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 143 | | mean | 15.22 | | std | 13.57 | | cv | 0.891 | | sampleLengths | | 0 | 38 | | 1 | 74 | | 2 | 13 | | 3 | 5 | | 4 | 55 | | 5 | 4 | | 6 | 49 | | 7 | 21 | | 8 | 9 | | 9 | 3 | | 10 | 55 | | 11 | 18 | | 12 | 3 | | 13 | 42 | | 14 | 14 | | 15 | 13 | | 16 | 16 | | 17 | 21 | | 18 | 13 | | 19 | 17 | | 20 | 4 | | 21 | 55 | | 22 | 6 | | 23 | 10 | | 24 | 4 | | 25 | 8 | | 26 | 4 | | 27 | 29 | | 28 | 6 | | 29 | 42 | | 30 | 14 | | 31 | 3 | | 32 | 29 | | 33 | 3 | | 34 | 8 | | 35 | 15 | | 36 | 16 | | 37 | 22 | | 38 | 42 | | 39 | 12 | | 40 | 5 | | 41 | 3 | | 42 | 20 | | 43 | 39 | | 44 | 6 | | 45 | 1 | | 46 | 17 | | 47 | 25 | | 48 | 10 | | 49 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 404 | | matches | | 0 | "get intercepted" | | 1 | "get paid" | | 2 | "are listened" | | 3 | "are made" | | 4 | "were cornered" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 443 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 404 | | ratio | 0.002 | | matches | | 0 | "Water dripped from the brim of nothing—he wore no hat—yet his charcoal suit remained dry, the fabric repelling the London damp like oil on water." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 471 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.014861995753715499 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0021231422505307855 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 404 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 404 | | mean | 5.39 | | std | 3.1 | | cv | 0.575 | | sampleLengths | | 0 | 14 | | 1 | 19 | | 2 | 5 | | 3 | 5 | | 4 | 25 | | 5 | 12 | | 6 | 12 | | 7 | 9 | | 8 | 6 | | 9 | 5 | | 10 | 4 | | 11 | 3 | | 12 | 6 | | 13 | 5 | | 14 | 3 | | 15 | 9 | | 16 | 24 | | 17 | 9 | | 18 | 10 | | 19 | 4 | | 20 | 6 | | 21 | 9 | | 22 | 18 | | 23 | 7 | | 24 | 9 | | 25 | 2 | | 26 | 2 | | 27 | 2 | | 28 | 7 | | 29 | 8 | | 30 | 4 | | 31 | 5 | | 32 | 3 | | 33 | 4 | | 34 | 8 | | 35 | 15 | | 36 | 2 | | 37 | 19 | | 38 | 4 | | 39 | 3 | | 40 | 4 | | 41 | 8 | | 42 | 6 | | 43 | 3 | | 44 | 9 | | 45 | 4 | | 46 | 7 | | 47 | 7 | | 48 | 15 | | 49 | 8 |
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| 27.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 92 | | diversityRatio | 0.1782178217821782 | | totalSentences | 404 | | uniqueOpeners | 72 | |
| 18.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 358 | | matches | | 0 | "Then prove it." | | 1 | "Then I walk out that" |
| | ratio | 0.006 | |
| 33.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 167 | | totalSentences | 358 | | matches | | 0 | "She pulled the door open." | | 1 | "His platinum hair stayed slicked" | | 2 | "Her knuckles whitened." | | 3 | "You should not be here." | | 4 | "He smelled of ozone and" | | 5 | "We need to talk." | | 6 | "She stood five foot six," | | 7 | "Her straight black hair hung" | | 8 | "She wore an old t-shirt" | | 9 | "He wore a suit worth" | | 10 | "We talked until there were" | | 11 | "We never finished." | | 12 | "He pushed past her." | | 13 | "She engaged the first bolt," | | 14 | "Her hand hovered over the" | | 15 | "He knows I stay with" | | 16 | "Your ex-boyfriend poses little threat" | | 17 | "He poses a threat to" | | 18 | "He knows where I sleep." | | 19 | "He knows when I walk." |
| | ratio | 0.466 | |
| 25.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 311 | | totalSentences | 358 | | matches | | 0 | "The third deadbolt slid home" | | 1 | "Rain lashed against the brickwork" | | 2 | "She pulled the door open." | | 3 | "Lucien stood on the landing." | | 4 | "Water dripped from the brim" | | 5 | "His platinum hair stayed slicked" | | 6 | "The ivory handle of his" | | 7 | "The light caught the heterochromatic" | | 8 | "Rory gripped the doorframe." | | 9 | "Her knuckles whitened." | | 10 | "The wood dug into her" | | 11 | "You should not be here." | | 12 | "Lucien stepped forward." | | 13 | "The toe of his polished" | | 14 | "He smelled of ozone and" | | 15 | "The smell of turmeric and" | | 16 | "We need to talk." | | 17 | "Rory moved to block his" | | 18 | "She stood five foot six," | | 19 | "Her straight black hair hung" |
| | ratio | 0.869 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 9 | | totalSentences | 358 | | matches | | 0 | "Now it clashed with the" | | 1 | "If Evan finds me here," | | 2 | "Now we decide." | | 3 | "Whether you fight." | | 4 | "If I tell you, you" | | 5 | "To keep you safe." | | 6 | "To keep you hidden." | | 7 | "If I take it, everything" | | 8 | "Because the deal expires at" |
| | ratio | 0.025 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "He smelled of ozone and old paper, a scent that clung to him regardless of the curry aromas wafting up from the floor below." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |