| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1454 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 34.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1454 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "familiar" | | 1 | "footfall" | | 2 | "cascading" | | 3 | "fractured" | | 4 | "kaleidoscope" | | 5 | "weight" | | 6 | "flickered" | | 7 | "echoes" | | 8 | "footsteps" | | 9 | "pulse" | | 10 | "almost alive" | | 11 | "velvet" | | 12 | "tension" | | 13 | "charged" | | 14 | "scanned" | | 15 | "reminder" | | 16 | "standard" | | 17 | "glint" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 133 | | matches | (empty) | |
| 67.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 2 | | narrationSentences | 133 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1454 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1454 | | uniqueNames | 19 | | maxNameDensity | 0.76 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Herrera | 8 | | Saint | 2 | | Christopher | 2 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Morris | 2 | | Camden | 3 | | Danger | 1 | | Entry | 1 | | Tomás | 7 | | Veil | 1 | | Market | 1 | | You | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Camden" | | 9 | "Tomás" | | 10 | "You" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | glossingSentenceCount | 1 | | matches | | 0 | "symbols that seemed to crawl across the page" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.688 | | wordCount | 1454 | | matches | | 0 | "not the harsh white of tube lighting but something shifting, almost alive" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 42.76 | | std | 28.98 | | cv | 0.678 | | sampleLengths | | 0 | 73 | | 1 | 4 | | 2 | 77 | | 3 | 45 | | 4 | 13 | | 5 | 8 | | 6 | 13 | | 7 | 81 | | 8 | 10 | | 9 | 54 | | 10 | 92 | | 11 | 15 | | 12 | 5 | | 13 | 12 | | 14 | 65 | | 15 | 74 | | 16 | 41 | | 17 | 6 | | 18 | 68 | | 19 | 56 | | 20 | 6 | | 21 | 94 | | 22 | 70 | | 23 | 67 | | 24 | 53 | | 25 | 43 | | 26 | 15 | | 27 | 74 | | 28 | 45 | | 29 | 11 | | 30 | 6 | | 31 | 65 | | 32 | 41 | | 33 | 52 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 272 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 133 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1462 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.03146374829001368 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.007523939808481532 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 10.93 | | std | 5.87 | | cv | 0.537 | | sampleLengths | | 0 | 20 | | 1 | 21 | | 2 | 13 | | 3 | 19 | | 4 | 1 | | 5 | 3 | | 6 | 6 | | 7 | 4 | | 8 | 20 | | 9 | 10 | | 10 | 9 | | 11 | 28 | | 12 | 10 | | 13 | 16 | | 14 | 10 | | 15 | 9 | | 16 | 6 | | 17 | 7 | | 18 | 8 | | 19 | 5 | | 20 | 8 | | 21 | 9 | | 22 | 8 | | 23 | 15 | | 24 | 15 | | 25 | 10 | | 26 | 11 | | 27 | 13 | | 28 | 10 | | 29 | 4 | | 30 | 29 | | 31 | 5 | | 32 | 16 | | 33 | 13 | | 34 | 14 | | 35 | 8 | | 36 | 23 | | 37 | 13 | | 38 | 13 | | 39 | 2 | | 40 | 6 | | 41 | 6 | | 42 | 9 | | 43 | 5 | | 44 | 6 | | 45 | 6 | | 46 | 14 | | 47 | 15 | | 48 | 18 | | 49 | 7 |
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| 71.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.43609022556390975 | | totalSentences | 133 | | uniqueOpeners | 58 | |
| 76.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 130 | | matches | | 0 | "Instead his shoulders hunched and" | | 1 | "Maybe more in this weather." | | 2 | "Only her own pulse and" |
| | ratio | 0.023 | |
| 90.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 130 | | matches | | 0 | "Her sharp jaw angled forward" | | 1 | "Her words bounced off wet" | | 2 | "He didn't break stride." | | 3 | "Her brown eyes narrowed against" | | 4 | "He cut left down an" | | 5 | "It clanged and rolled, forcing" | | 6 | "Her coat flared behind her" | | 7 | "You can't outrun this forever," | | 8 | "I know what you did" | | 9 | "His answer drifted back, half-lost" | | 10 | "You have no idea what" | | 11 | "She burst from the alley" | | 12 | "He glanced back once, warm" | | 13 | "She ignored the jab." | | 14 | "Her partner's face tried to" | | 15 | "She shoved the memory down." | | 16 | "They pounded north, leaving Soho's" | | 17 | "He righted himself and plunged" | | 18 | "His laugh came out ragged." | | 19 | "You still think it's just" |
| | ratio | 0.323 | |
| 52.31% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 130 | | matches | | 0 | "Detective Harlow Quinn exploded from" | | 1 | "Her sharp jaw angled forward" | | 2 | "Tomás Herrera's short curly dark" | | 3 | "Her words bounced off wet" | | 4 | "He didn't break stride." | | 5 | "Quinn gave chase, leather watch" | | 6 | "Her brown eyes narrowed against" | | 7 | "Each footfall sent cold water" | | 8 | "He cut left down an" | | 9 | "The scar on his left" | | 10 | "It clanged and rolled, forcing" | | 11 | "Her coat flared behind her" | | 12 | "You can't outrun this forever," | | 13 | "I know what you did" | | 14 | "His answer drifted back, half-lost" | | 15 | "You have no idea what" | | 16 | "She burst from the alley" | | 17 | "Traffic crawled past, tires hissing" | | 18 | "Headlights fractured in puddles, turning" | | 19 | "Tomás wove between two black" |
| | ratio | 0.815 | |
| 76.92% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 130 | | matches | | 0 | "If she let him vanish" | | 1 | "If she followed, she entered" |
| | ratio | 0.015 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 7 | | matches | | 0 | "Focus stayed on the curly head bobbing ahead, on the medallion that bounced like a taunt." | | 1 | "Street vendors had long packed away their wares, leaving only empty stalls and scattered litter that floated in miniature rivers along the gutters." | | 2 | "The chase carried them past locked gates and graffiti-scrawled walls that flickered under unreliable lamps." | | 3 | "Echoes rose from below, Tomás's footsteps fading into a steady drip-drip-drip that sounded too regular, too deliberate." | | 4 | "Lanterns swayed from iron hooks, casting colors no normal bulb produced: deep violet, acid green, hues that hurt to stare at directly." | | 5 | "He slid a small bone token across the counter, something carved with runes that caught the unnatural light." | | 6 | "The air itself felt charged, as though the rain above had followed her down in electrical form." |
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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 | |