| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.714 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 79.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1236 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "sharply" | | 1 | "completely" | | 2 | "slightly" | | 3 | "slowly" |
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| 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) | |
| 23.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1236 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "rhythmic" | | 1 | "footsteps" | | 2 | "maw" | | 3 | "familiar" | | 4 | "flickered" | | 5 | "resolving" | | 6 | "loomed" | | 7 | "hulking" | | 8 | "ensure" | | 9 | "intricate" | | 10 | "facade" | | 11 | "flicker" | | 12 | "scanning" | | 13 | "shattered" | | 14 | "raced" | | 15 | "weight" | | 16 | "silence" | | 17 | "chaotic" | | 18 | "synchronized" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 63 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1236 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1125 | | uniqueNames | 17 | | maxNameDensity | 1.87 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 21 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | London | 2 | | Camden | 1 | | Metropolitan | 1 | | Police | 1 | | Morris | 2 | | Edwardian | 1 | | Last | 1 | | Tube | 1 | | Tomás | 1 | | Herrera | 7 | | You | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Police" | | 4 | "Morris" | | 5 | "Last" | | 6 | "Tomás" | | 7 | "Herrera" | | 8 | "You" |
| | places | | | globalScore | 0.567 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.809 | | wordCount | 1236 | | matches | | 0 | "not the familiar musty smell of London underground damp, but something older, metallic" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 28.74 | | std | 18.34 | | cv | 0.638 | | sampleLengths | | 0 | 2 | | 1 | 66 | | 2 | 35 | | 3 | 71 | | 4 | 14 | | 5 | 56 | | 6 | 37 | | 7 | 45 | | 8 | 14 | | 9 | 57 | | 10 | 26 | | 11 | 35 | | 12 | 9 | | 13 | 19 | | 14 | 10 | | 15 | 36 | | 16 | 17 | | 17 | 50 | | 18 | 27 | | 19 | 10 | | 20 | 12 | | 21 | 64 | | 22 | 45 | | 23 | 17 | | 24 | 10 | | 25 | 37 | | 26 | 3 | | 27 | 21 | | 28 | 27 | | 29 | 2 | | 30 | 45 | | 31 | 36 | | 32 | 6 | | 33 | 25 | | 34 | 45 | | 35 | 12 | | 36 | 24 | | 37 | 25 | | 38 | 31 | | 39 | 26 | | 40 | 9 | | 41 | 55 | | 42 | 23 |
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| 99.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 63 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 196 | | matches | | 0 | "was chasing" | | 1 | "was treating" |
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| 58.82% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 68 | | ratio | 0.029 | | matches | | 0 | "He did not look at her face; his eyes drifted immediately to her hands." | | 1 | "From the shadows of the passageway ahead, three figures materialized, stepping slowly into the faint light of the market's edge. They did not walk like humans; their movements were too fluid, too synchronized. Their eyes gleamed with an predatory, unnatural hunger, and the air around them turned freezing cold, turning Quinn's breath into white plumes." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 560 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.017857142857142856 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.007142857142857143 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 18.18 | | std | 13.41 | | cv | 0.738 | | sampleLengths | | 0 | 2 | | 1 | 28 | | 2 | 16 | | 3 | 22 | | 4 | 5 | | 5 | 7 | | 6 | 23 | | 7 | 18 | | 8 | 21 | | 9 | 32 | | 10 | 14 | | 11 | 18 | | 12 | 16 | | 13 | 17 | | 14 | 5 | | 15 | 7 | | 16 | 14 | | 17 | 16 | | 18 | 8 | | 19 | 10 | | 20 | 27 | | 21 | 14 | | 22 | 18 | | 23 | 21 | | 24 | 18 | | 25 | 5 | | 26 | 21 | | 27 | 2 | | 28 | 19 | | 29 | 14 | | 30 | 9 | | 31 | 7 | | 32 | 12 | | 33 | 5 | | 34 | 5 | | 35 | 4 | | 36 | 15 | | 37 | 17 | | 38 | 15 | | 39 | 2 | | 40 | 50 | | 41 | 27 | | 42 | 10 | | 43 | 12 | | 44 | 64 | | 45 | 45 | | 46 | 17 | | 47 | 10 | | 48 | 37 | | 49 | 3 |
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| 74.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.45588235294117646 | | totalSentences | 68 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 58 | | matches | | 0 | "She checked her left wrist." | | 1 | "She reached the iron sheet," | | 2 | "It was not the familiar" | | 3 | "It was a sound that" | | 4 | "She stood in a tiled" | | 5 | "He did not look at" | | 6 | "She took a step closer," | | 7 | "It was a nightmare given" | | 8 | "It was Tomás Herrera, the" | | 9 | "She surged forward, weaving through" | | 10 | "She coughed, waving her hand" | | 11 | "She closed the gap to" | | 12 | "They crashed into a heap" | | 13 | "You have no idea what" |
| | ratio | 0.241 | |
| 46.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 58 | | matches | | 0 | "The heel of Detective Harlow" | | 1 | "Rain pelted the Soho pavement," | | 2 | "She checked her left wrist." | | 3 | "The worn leather watch read" | | 4 | "The suspect veered sharply left," | | 5 | "Quinn rounded the corner, her" | | 6 | "The alley led to a" | | 7 | "Quinn muttered under her breath," | | 8 | "She reached the iron sheet," | | 9 | "It was not the familiar" | | 10 | "A metal ladder descended into" | | 11 | "It was a sound that" | | 12 | "A heavy decision hung in" | | 13 | "This was uncharted territory, far" | | 14 | "Quinn grabbed the cold rungs" | | 15 | "The descent was quick, her" | | 16 | "She stood in a tiled" | | 17 | "A massive archway loomed ahead." | | 18 | "A hulking man with a" | | 19 | "He did not look at" |
| | ratio | 0.828 | |
| 86.21% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 58 | | matches | | | ratio | 0.017 | |
| 7.72% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 7 | | matches | | 0 | "The suspect veered sharply left, darting down an alleyway that smelled of damp cardboard and rancid cooking oil." | | 1 | "Quinn rounded the corner, her shoulder brushing the brick wall and leaving a streak of wet soot on her trench coat." | | 2 | "Suspended above it sat a crude sign fashioned from rusted steel, illuminated by torches that burned with an unsettling blue flame." | | 3 | "People, or things that looked mostly like people, bustled between the stalls. A woman with fingers too long and eyes too wide haggled over a jar of shimmering s…" | | 4 | "Quinn turned, her eyes scanning the crowd. Thirty yards away, pushing past a merchant selling antique clocks that ticked in reverse, was her suspect. He had los…" | | 5 | "The suspect checked over his shoulder, his warm brown eyes widening in sheer panic as he spotted her. He abandoned all subtlety, knocking over a display of glas…" | | 6 | "They crashed into a heap of empty burlap sacks, rolling onto the hard, damp concrete. Herrera struggled, his limbs flailing, but Quinn was a trained officer. Sh…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 4 | | matches | | 0 | "Quinn muttered, her hand dropping to her baton" | | 1 | "the guard replied, his expression completely vacant of fear" | | 2 | "Quinn muttered, accelerating" | | 3 | "Herrera gasped, his face pressed against the cold stone" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 8 | | fancyTags | | 0 | "Quinn muttered (mutter)" | | 1 | "he muttered (mutter)" | | 2 | "a voice hissed (hiss)" | | 3 | "Quinn whispered (whisper)" | | 4 | "Quinn muttered (mutter)" | | 5 | "Quinn panted (pant)" | | 6 | "Herrera gasped (gasp)" | | 7 | "Herrera whispered (whisper)" |
| | dialogueSentences | 21 | | tagDensity | 0.524 | | leniency | 1 | | rawRatio | 0.727 | | effectiveRatio | 0.727 | |