| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1289 | | totalAiIsmAdverbs | 2 | | 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) | |
| 68.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1289 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "tracing" | | 1 | "traced" | | 2 | "fluttered" | | 3 | "pulse" | | 4 | "flickered" | | 5 | "flicker" | | 6 | "silence" | | 7 | "reminder" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "flicker of emotion" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "a spark of hope" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 90 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1268 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1266 | | uniqueNames | 16 | | maxNameDensity | 0.87 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 8 | | Avarosian | 9 | | Rory | 11 | | Cardiff | 2 | | University | 1 | | Golden | 1 | | Empress | 1 | | Soho | 1 | | Hyde | 1 | | Park | 1 | | East | 1 | | London | 1 | | Ptolemy | 4 | | Lucien | 3 | | Evan | 2 | | You | 8 |
| | persons | | 0 | "Eva" | | 1 | "Rory" | | 2 | "Ptolemy" | | 3 | "Lucien" | | 4 | "Evan" | | 5 | "You" |
| | places | | 0 | "Cardiff" | | 1 | "Soho" | | 2 | "Hyde" | | 3 | "Park" | | 4 | "East" | | 5 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | 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 | 1268 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 33.37 | | std | 26.42 | | cv | 0.792 | | sampleLengths | | 0 | 107 | | 1 | 53 | | 2 | 8 | | 3 | 34 | | 4 | 13 | | 5 | 31 | | 6 | 4 | | 7 | 31 | | 8 | 23 | | 9 | 34 | | 10 | 21 | | 11 | 30 | | 12 | 22 | | 13 | 35 | | 14 | 11 | | 15 | 22 | | 16 | 32 | | 17 | 18 | | 18 | 39 | | 19 | 21 | | 20 | 66 | | 21 | 9 | | 22 | 30 | | 23 | 36 | | 24 | 6 | | 25 | 22 | | 26 | 5 | | 27 | 39 | | 28 | 6 | | 29 | 8 | | 30 | 60 | | 31 | 55 | | 32 | 45 | | 33 | 33 | | 34 | 46 | | 35 | 6 | | 36 | 88 | | 37 | 119 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 90 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 213 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 90 | | ratio | 0.133 | | matches | | 0 | "The wood creaked open an inch before she slammed a palm against it, her left wrist’s crescent scar pulling tight enough to make her grind her teeth—until she caught a glimpse of slicked-back platinum blond hair and a tailored charcoal suit sleeve." | | 1 | "The cane’s hidden blade didn’t rattle—he’d oiled it, a habit she’d mocked once when they’d snuck into a supernatural smuggling ring’s warehouse together." | | 2 | "He gestured to the books and scrolls caking every surface—Eva’s research notes on Avarosian portals, Rory’s pre-law textbooks from Cardiff University, a stack of faded fairy wing sketches tucked under a coffee table leg." | | 3 | "He plucked a smoked salmon treat from a hidden pocket in his suit—an old stash he’d left for the cat months prior." | | 4 | "The scroll’s Avarosian script blurs under her fist—she’d helped Eva transcribe it for three nights straight after a wraith attacked her bar flat." | | 5 | "Rory’s breath caught—he remembered the smallest details, even after six months." | | 6 | "His cane tapped twice, a signal he’d used when they were in danger—once when they’d been cornered by a vampire in Soho, once when they’d snuck into a fairy court’s garden." | | 7 | "My human half clung to every detail—the way you laugh when you burn dim sum wrappers, the way you tuck your hair behind your ear when you’re solving a puzzle, the way you kissed me on the bar’s rooftop the night before I left." | | 8 | "She marks a spot on the curry house’s basement with a red pen—her quick out-of-the-box thinking kicking in." | | 9 | "The curry house’s tandoor oven—its fire is the only thing hot enough to power the wraiths’ spell." | | 10 | "The hurt lingers too—six months of silence, a half-scrawled note, the fear that he’d abandoned her." | | 11 | "Rory’s pre-law textbook falls off the shelf, its pages fluttering to a chapter on criminal defense—something she’d studied before fleeing Cardiff, before she met Lucien, before everything changed." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1016 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.023622047244094488 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002952755905511811 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 14.09 | | std | 8.24 | | cv | 0.585 | | sampleLengths | | 0 | 31 | | 1 | 28 | | 2 | 42 | | 3 | 6 | | 4 | 14 | | 5 | 23 | | 6 | 16 | | 7 | 8 | | 8 | 34 | | 9 | 13 | | 10 | 15 | | 11 | 16 | | 12 | 4 | | 13 | 9 | | 14 | 22 | | 15 | 4 | | 16 | 19 | | 17 | 11 | | 18 | 23 | | 19 | 6 | | 20 | 15 | | 21 | 19 | | 22 | 11 | | 23 | 8 | | 24 | 14 | | 25 | 4 | | 26 | 11 | | 27 | 20 | | 28 | 4 | | 29 | 7 | | 30 | 5 | | 31 | 17 | | 32 | 21 | | 33 | 11 | | 34 | 3 | | 35 | 15 | | 36 | 10 | | 37 | 11 | | 38 | 18 | | 39 | 6 | | 40 | 15 | | 41 | 31 | | 42 | 14 | | 43 | 21 | | 44 | 9 | | 45 | 12 | | 46 | 18 | | 47 | 13 | | 48 | 23 | | 49 | 4 |
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| 33.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.24444444444444444 | | totalSentences | 90 | | uniqueOpeners | 22 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 87 | | matches | | 0 | "She’d stacked three leather-bound grimoires" | | 1 | "She let the door swing" | | 2 | "He stepped over the fallen" | | 3 | "His heterochromatic eyes locked on" | | 4 | "You don’t get to show" | | 5 | "He gestured to the books" | | 6 | "He rubbed against the stranger’s" | | 7 | "He bent slightly, his cane" | | 8 | "He plucked a smoked salmon" | | 9 | "He’s not a stranger." | | 10 | "I left him a treat" | | 11 | "You left more than salmon" | | 12 | "You left a half-scrawled note" | | 13 | "He picked up a research" | | 14 | "His half-demon blood hummed at" | | 15 | "I couldn’t risk you getting" | | 16 | "It fluttered to the floor," | | 17 | "Her hand flew to her" | | 18 | "You didn’t ask me." | | 19 | "I don’t need you to" |
| | ratio | 0.552 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 87 | | matches | | 0 | "Rory fumbled with the third" | | 1 | "She’d stacked three leather-bound grimoires" | | 2 | "The wood creaked open an" | | 3 | "She let the door swing" | | 4 | "He stepped over the fallen" | | 5 | "The cane’s hidden blade didn’t" | | 6 | "His heterochromatic eyes locked on" | | 7 | "You don’t get to show" | | 8 | "He gestured to the books" | | 9 | "Eva’s flat is the only" | | 10 | "Ptolemy slinked out from under" | | 11 | "He rubbed against the stranger’s" | | 12 | "Ptolemy doesn’t like strangers." | | 13 | "He bent slightly, his cane" | | 14 | "He plucked a smoked salmon" | | 15 | "He’s not a stranger." | | 16 | "I left him a treat" | | 17 | "Rory snatches a scroll from" | | 18 | "The scroll’s Avarosian script blurs" | | 19 | "You left more than salmon" |
| | ratio | 0.966 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 87 | | matches | | 0 | "In case I came back?" | | 1 | "Before she can speak, the" |
| | ratio | 0.023 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 3 | | matches | | 0 | "I left him a treat last time I helped Eva track a fairy thief who’d stolen a witch’s spellbook." | | 1 | "His thumb brushes her crescent scar, a gentle touch that contrasts with his usual sharp demeanor." | | 2 | "Rory’s pre-law textbook falls off the shelf, its pages fluttering to a chapter on criminal defense—something she’d studied before fleeing Cardiff, before she me…" |
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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 | |