| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1574 | | 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) | |
| 26.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1574 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "rhythmic" | | 1 | "footsteps" | | 2 | "weight" | | 3 | "tracing" | | 4 | "echo" | | 5 | "silence" | | 6 | "predator" | | 7 | "warmth" | | 8 | "throb" | | 9 | "glistening" | | 10 | "pulse" | | 11 | "intensity" | | 12 | "searing" | | 13 | "flicker" | | 14 | "normalcy" | | 15 | "cacophony" | | 16 | "etched" | | 17 | "chilled" |
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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 | 116 | | matches | (empty) | |
| 19.70% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 8 | | narrationSentences | 116 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 12 | | totalWords | 1563 | | ratio | 0.008 | | matches | | 0 | "Intelligent people don’t get lost in city parks," | | 1 | "They analyze the data." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1531 | | uniqueNames | 21 | | maxNameDensity | 0.65 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Golden | 2 | | Empress | 2 | | Richmond | 3 | | Park | 3 | | Royal | 1 | | Ballet | 1 | | School | 1 | | Yu-Fei | 1 | | Peking | 2 | | Heartstone | 3 | | Pendant | 2 | | London | 3 | | November | 1 | | Fae | 2 | | Grove | 4 | | Silas | 1 | | Earth | 1 | | Gentry | 1 | | Hel | 1 | | Rory | 10 | | McDonald | 1 |
| | persons | | 0 | "Yu-Fei" | | 1 | "Pendant" | | 2 | "Silas" | | 3 | "Earth" | | 4 | "Rory" | | 5 | "McDonald" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Peking" | | 3 | "London" | | 4 | "Fae" | | 5 | "Grove" | | 6 | "Hel" |
| | globalScore | 1 | | windowScore | 1 | |
| 14.13% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 5 | | matches | | 0 | "sounded like the chime of glass breaking i" | | 1 | "limbs that seemed to have too many joints" | | 2 | "looked like gossamer and dried moss" | | 3 | "light that seemed to sear the shadows back" | | 4 | "felt like pushing through a heavy curta" |
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| 72.04% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.28 | | wordCount | 1563 | | matches | | 0 | "Not the absence of noise, but a thick, pressurized quiet" | | 1 | "not a shadow this time, but a distortion in the air, a ripple like heat rising from asph" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 38.12 | | std | 23.24 | | cv | 0.61 | | sampleLengths | | 0 | 63 | | 1 | 83 | | 2 | 63 | | 3 | 72 | | 4 | 12 | | 5 | 58 | | 6 | 55 | | 7 | 20 | | 8 | 36 | | 9 | 77 | | 10 | 28 | | 11 | 49 | | 12 | 28 | | 13 | 59 | | 14 | 5 | | 15 | 18 | | 16 | 13 | | 17 | 23 | | 18 | 53 | | 19 | 10 | | 20 | 31 | | 21 | 73 | | 22 | 19 | | 23 | 15 | | 24 | 28 | | 25 | 41 | | 26 | 55 | | 27 | 29 | | 28 | 42 | | 29 | 2 | | 30 | 59 | | 31 | 27 | | 32 | 51 | | 33 | 29 | | 34 | 19 | | 35 | 4 | | 36 | 33 | | 37 | 36 | | 38 | 18 | | 39 | 29 | | 40 | 98 |
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| 78.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 116 | | matches | | 0 | "was attached" | | 1 | "was blinked" | | 2 | "was carpeted" | | 3 | "was swallowed" | | 4 | "get lost" | | 5 | "were gone" | | 6 | "being replaced" | | 7 | "was torn" | | 8 | "were etched" | | 9 | "was gone" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 9 | | totalVerbs | 242 | | matches | | 0 | "was twitching" | | 1 | "was warning" | | 2 | "was coming" | | 3 | "was closing" | | 4 | "were beginning" | | 5 | "were waiting" | | 6 | "was falling" | | 7 | "was following" | | 8 | "was currently enjoying" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 2 | | flaggedSentences | 12 | | totalSentences | 120 | | ratio | 0.1 | | matches | | 0 | "London at night was her element—the hum of traffic, the distant sirens, the rhythmic clack of her own footsteps." | | 1 | "Rory slowed her pace, her thumb tracing the small crescent-shaped scar on her left wrist—a nervous habit she’d never quite broken." | | 2 | "Her voice didn't echo; it was swallowed instantly by the lush greenery." | | 3 | "*They analyze the data.* The data told her she was in the Fae Grove—the place Silas had warned her about over drinks at the bar." | | 4 | "It wasn't the gentle warmth of a hearth; it was a sharp, biting throb." | | 5 | "Something shifted in the periphery of her vision—a flash of white, like a pale hand retracting into the brush." | | 6 | "There was a sound now—a low, rhythmic scraping, like bone dragging over stone." | | 7 | "A movement caught her eye—not a shadow this time, but a distortion in the air, a ripple like heat rising from asphalt." | | 8 | "As she straightened, she saw them—dozens of them." | | 9 | "She hit a wall of resistance—a membrane of cold, stagnant air that felt like pushing through a heavy curtain." | | 10 | "Her sleeve was torn, and five thin, red lines were etched into her skin—marks that looked suspiciously like the sweep of a pale, needle-fingered hand." | | 11 | "The Peking duck was gone, left on a mossy rock in a place that didn't exist, and Rory knew, with a certainty that chilled her more than the fog, that someone—or something—was currently enjoying the tip." |
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| 76.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1551 | | adjectiveStacks | 4 | | stackExamples | | 0 | "small crescent-shaped scar" | | 1 | "faint, rhythmic inner light," | | 2 | "pale, needle-fingered hand." | | 3 | "dull, dormant red against her" |
| | adverbCount | 51 | | adverbRatio | 0.03288201160541586 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.01160541586073501 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 13.03 | | std | 7.87 | | cv | 0.604 | | sampleLengths | | 0 | 37 | | 1 | 26 | | 2 | 15 | | 3 | 18 | | 4 | 6 | | 5 | 19 | | 6 | 25 | | 7 | 17 | | 8 | 13 | | 9 | 33 | | 10 | 11 | | 11 | 4 | | 12 | 17 | | 13 | 18 | | 14 | 22 | | 15 | 12 | | 16 | 7 | | 17 | 22 | | 18 | 16 | | 19 | 13 | | 20 | 21 | | 21 | 6 | | 22 | 28 | | 23 | 4 | | 24 | 12 | | 25 | 4 | | 26 | 2 | | 27 | 15 | | 28 | 4 | | 29 | 15 | | 30 | 15 | | 31 | 11 | | 32 | 25 | | 33 | 26 | | 34 | 10 | | 35 | 14 | | 36 | 4 | | 37 | 6 | | 38 | 8 | | 39 | 16 | | 40 | 19 | | 41 | 11 | | 42 | 14 | | 43 | 3 | | 44 | 21 | | 45 | 11 | | 46 | 5 | | 47 | 13 | | 48 | 9 | | 49 | 2 |
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| 30.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.3 | | totalSentences | 120 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 112 | | matches | | 0 | "Usually, she didn't mind the" | | 1 | "Instead, the trees seemed to" | | 2 | "Then from the right." | | 3 | "Then from the roots beneath" | | 4 | "Then, she was falling." |
| | ratio | 0.045 | |
| 55.71% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 112 | | matches | | 0 | "She checked the thermal bag" | | 1 | "It was cold tonight, a" | | 2 | "She walked for ten minutes," | | 3 | "She passed a set of" | | 4 | "She looked down at her" | | 5 | "she called out" | | 6 | "Her voice didn't echo; it" | | 7 | "She checked her watch." | | 8 | "*They analyze the data.* The" | | 9 | "He’d talked about the pocket" | | 10 | "It wasn't the gentle warmth" | | 11 | "It was warning her." | | 12 | "She turned in a slow" | | 13 | "She unslung the Golden Empress" | | 14 | "She began to walk in" | | 15 | "It was coming from the" | | 16 | "She took three quick steps." | | 17 | "she lied, her eyes darting" | | 18 | "It wasn't human." | | 19 | "It sounded like the chime" |
| | ratio | 0.411 | |
| 58.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 112 | | matches | | 0 | "The delivery instructions had been" | | 1 | "Aurora hooked her bike to" | | 2 | "The fog was thick tonight," | | 3 | "London at night was her" | | 4 | "She checked the thermal bag" | | 5 | "It was cold tonight, a" | | 6 | "She walked for ten minutes," | | 7 | "The geography felt wrong." | | 8 | "She passed a set of" | | 9 | "The moment she crossed the" | | 10 | "The distant roar of the" | | 11 | "The city’s light pollution, that" | | 12 | "The air grew impossibly sweet," | | 13 | "Rory slowed her pace, her" | | 14 | "She looked down at her" | | 15 | "she called out" | | 16 | "Her voice didn't echo; it" | | 17 | "She checked her watch." | | 18 | "The second hand was twitching" | | 19 | "Rory took a steadying breath," |
| | ratio | 0.804 | |
| 44.64% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 112 | | matches | | | ratio | 0.009 | |
| 84.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 74 | | technicalSentenceCount | 6 | | matches | | 0 | "The fog was thick tonight, a grey, clinging shroud that turned the distant streetlamps into blurry, jaundiced eyes." | | 1 | "The city’s light pollution, that orange haze that usually defined the London sky, was blinked out as if by a closing eyelid." | | 2 | "The heat from the pendant was becoming unbearable, a localized fever that made her skin itch." | | 3 | "The crimson gem began to pulse with a faint, rhythmic inner light, casting long, rhythmic shadows that bled into the wildflowers." | | 4 | "She hit a wall of resistance—a membrane of cold, stagnant air that felt like pushing through a heavy curtain." | | 5 | "The Peking duck was gone, left on a mossy rock in a place that didn't exist, and Rory knew, with a certainty that chilled her more than the fog, that someone—or…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "Rory said, her voice tighter now" | | 1 | "she lied, her eyes darting between the silver-barked trunks" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she lied (lie)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.667 | | effectiveRatio | 0.5 | |