| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said softly [softly]" |
| | dialogueSentences | 47 | | tagDensity | 0.362 | | leniency | 0.723 | | rawRatio | 0.059 | | effectiveRatio | 0.043 | |
| 91.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1768 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "sharply" | | 1 | "really" | | 2 | "softly" |
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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) | |
| 60.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1768 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "glistening" | | 1 | "tension" | | 2 | "gloom" | | 3 | "shattered" | | 4 | "etched" | | 5 | "loomed" | | 6 | "stomach" | | 7 | "flicked" | | 8 | "pulse" | | 9 | "whisper" | | 10 | "silence" | | 11 | "weight" | | 12 | "sense of" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "stomach dropped" | | 1 | "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 | 168 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 168 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 194 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 20 | | markdownWords | 36 | | totalWords | 1750 | | ratio | 0.021 | | matches | | 0 | "unsolved" | | 1 | "City banker" | | 2 | "corporate lawyer" | | 3 | "placed" | | 4 | "E.K." | | 5 | "neat" | | 6 | "misplaced" | | 7 | "E.K." | | 8 | "guilty" | | 9 | "know" | | 10 | "that" | | 11 | "There’s a market, Harlow. A real fucking market. And it’s not for us." | | 12 | "underground fairy bazaar" | | 13 | "cleaned" | | 14 | "depth" | | 15 | "weight" | | 16 | "Stop" | | 17 | "wrong" | | 18 | "warning" | | 19 | "stop" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 64 | | wordCount | 1405 | | uniqueNames | 11 | | maxNameDensity | 1.57 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Harlow | 2 | | Quinn | 22 | | Morris | 7 | | Richard | 1 | | Graves | 15 | | Styrofoam | 1 | | Oxfords | 1 | | Kowalski | 1 | | Eva | 12 | | Urgency | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Richard" | | 4 | "Graves" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | (empty) | | globalScore | 0.717 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 1 | | matches | | 0 | "something like it once before—in Morris’s ef" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1750 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 194 | | matches | | 0 | "knew that symbol" | | 1 | "knew that story" | | 2 | "knew that fear" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 28.23 | | std | 26.32 | | cv | 0.932 | | sampleLengths | | 0 | 133 | | 1 | 81 | | 2 | 9 | | 3 | 10 | | 4 | 85 | | 5 | 63 | | 6 | 28 | | 7 | 41 | | 8 | 69 | | 9 | 17 | | 10 | 32 | | 11 | 59 | | 12 | 2 | | 13 | 74 | | 14 | 8 | | 15 | 57 | | 16 | 11 | | 17 | 4 | | 18 | 6 | | 19 | 30 | | 20 | 7 | | 21 | 74 | | 22 | 4 | | 23 | 22 | | 24 | 16 | | 25 | 6 | | 26 | 14 | | 27 | 38 | | 28 | 42 | | 29 | 15 | | 30 | 2 | | 31 | 16 | | 32 | 22 | | 33 | 28 | | 34 | 14 | | 35 | 65 | | 36 | 40 | | 37 | 17 | | 38 | 31 | | 39 | 36 | | 40 | 64 | | 41 | 6 | | 42 | 7 | | 43 | 9 | | 44 | 46 | | 45 | 40 | | 46 | 27 | | 47 | 8 | | 48 | 8 | | 49 | 13 |
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| 82.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 11 | | totalSentences | 168 | | matches | | 0 | "was turned" | | 1 | "was positioned" | | 2 | "been *placed" | | 3 | "been wiped" | | 4 | "been chased" | | 5 | "been *misplaced" | | 6 | "been *cleaned" | | 7 | "been found" | | 8 | "was sheened" | | 9 | "were relaxed" | | 10 | "was smudged" | | 11 | "been burned" | | 12 | "was puckered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 247 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 194 | | ratio | 0.062 | | matches | | 0 | "The air was thick with the hum of fluorescent lights, their sickly glow reflecting off the peeling posters still clinging to the walls—advertisements for long-forgotten plays and wartime propaganda, their edges curled like dying leaves." | | 1 | "A figure stepped forward—tall, broad-shouldered, the kind of man who filled a doorway just by standing in it." | | 2 | "His suit was expensive—tailored, charcoal gray, the kind that whispered *City banker* or *corporate lawyer*." | | 3 | "Instead, she studied the victim’s hands—manicured nails, a signet ring on the pinky, the gold dull in the poor light." | | 4 | "The blood spatter was wrong—too controlled, too *neat*." | | 5 | "And the victim’s shoes—polished Oxfords, not a scuff on them." | | 6 | "She’d seen something like it once before—in Morris’s effects, after he’d vanished." | | 7 | "The back was smooth, unmarked except for a single symbol carved near the hinge—a crescent moon cradling a star." | | 8 | "He opened his mouth—" | | 9 | "Quinn studied her—the way her pulse jumped in her throat, the way she kept tucking the same strand of hair behind her left ear, over and over." | | 10 | "Graves made a sound—half laugh, half choke." | | 11 | "The edges of the symbol were *wrong*—too precise, like they’d been burned rather than drawn." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1422 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.03234880450070324 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.007032348804500703 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 194 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 194 | | mean | 9.02 | | std | 7.37 | | cv | 0.817 | | sampleLengths | | 0 | 22 | | 1 | 16 | | 2 | 35 | | 3 | 21 | | 4 | 12 | | 5 | 27 | | 6 | 24 | | 7 | 18 | | 8 | 9 | | 9 | 17 | | 10 | 13 | | 11 | 3 | | 12 | 6 | | 13 | 5 | | 14 | 5 | | 15 | 7 | | 16 | 32 | | 17 | 15 | | 18 | 24 | | 19 | 7 | | 20 | 7 | | 21 | 5 | | 22 | 2 | | 23 | 20 | | 24 | 3 | | 25 | 5 | | 26 | 21 | | 27 | 11 | | 28 | 17 | | 29 | 2 | | 30 | 39 | | 31 | 3 | | 32 | 7 | | 33 | 3 | | 34 | 8 | | 35 | 15 | | 36 | 10 | | 37 | 10 | | 38 | 5 | | 39 | 3 | | 40 | 5 | | 41 | 13 | | 42 | 4 | | 43 | 5 | | 44 | 27 | | 45 | 3 | | 46 | 14 | | 47 | 17 | | 48 | 6 | | 49 | 8 |
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| 41.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.2422680412371134 | | totalSentences | 194 | | uniqueOpeners | 47 | |
| 67.11% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 149 | | matches | | 0 | "Instead, she studied the victim’s" | | 1 | "Just the brick, and the" | | 2 | "Then, barely a whisper:" |
| | ratio | 0.02 | |
| 80.40% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 149 | | matches | | 0 | "She adjusted the cuff of" | | 1 | "His tie was loose, his" | | 2 | "He held a Styrofoam cup" | | 3 | "She didn’t owe him an" | | 4 | "She followed the line of" | | 5 | "His suit was expensive—tailored, charcoal" | | 6 | "His face was turned away," | | 7 | "She didn’t touch the brick." | | 8 | "she asked, though she already" | | 9 | "He nudged the victim’s jacket" | | 10 | "She reached for the handkerchief," | | 11 | "She glanced at the tracks." | | 12 | "She stood, rolling her shoulders" | | 13 | "She stepped closer to the" | | 14 | "She crouched again, her breath" | | 15 | "She pulled a pencil from" | | 16 | "It twitched erratically, as if" | | 17 | "She’d seen something like it" | | 18 | "She flipped the compass over." | | 19 | "Her fingers tingled." |
| | ratio | 0.349 | |
| 33.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 149 | | matches | | 0 | "The abandoned Tube station reeked" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The air was thick with" | | 3 | "She adjusted the cuff of" | | 4 | "The voice came from the" | | 5 | "A figure stepped forward—tall, broad-shouldered," | | 6 | "His tie was loose, his" | | 7 | "He held a Styrofoam cup" | | 8 | "Quinn didn’t answer." | | 9 | "She didn’t owe him an" | | 10 | "Graves gestured toward the body." | | 11 | "She followed the line of" | | 12 | "The victim lay sprawled near" | | 13 | "His suit was expensive—tailored, charcoal" | | 14 | "His face was turned away," | | 15 | "A brick lay nearby, its" | | 16 | "Quinn crouched, her knees popping" | | 17 | "She didn’t touch the brick." | | 18 | "she asked, though she already" | | 19 | "The wallet was still in" |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 149 | | matches | (empty) | | ratio | 0 | |
| 68.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 5 | | matches | | 0 | "The abandoned Tube station reeked of damp concrete and something older, something that clung to the back of the throat like rust." | | 1 | "Three years of files stamped *unsolved*, of superiors suggesting she take leave, of nights spent staring at case photos that made less sense the longer she look…" | | 2 | "A figure stepped forward—tall, broad-shouldered, the kind of man who filled a doorway just by standing in it." | | 3 | "The victim lay sprawled near the edge of the platform, one leg bent at an unnatural angle, the other stretched toward the tracks as if he’d been mid-stride when…" | | 4 | "His suit was expensive—tailored, charcoal gray, the kind that whispered *City banker* or *corporate lawyer*." |
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| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn asked, her voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.085 | | leniency | 0.17 | | rawRatio | 0 | | effectiveRatio | 0 | |