| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.393 | | leniency | 0.786 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1913 | | 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) | |
| 63.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1913 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "flickered" | | 1 | "pulse" | | 2 | "glint" | | 3 | "traced" | | 4 | "measured" | | 5 | "stomach" | | 6 | "flicked" | | 7 | "lilt" | | 8 | "shimmered" | | 9 | "etched" | | 10 | "scanned" | | 11 | "echoed" | | 12 | "pounding" | | 13 | "searing" |
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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 | 1 | | narrationSentences | 185 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 185 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 202 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1914 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1647 | | uniqueNames | 18 | | maxNameDensity | 1.46 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 24 | | Raven | 2 | | Nest | 2 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | London | 2 | | Tomás | 14 | | Saint | 3 | | Christopher | 3 | | Seville | 1 | | Calm | 1 | | Spanish | 2 | | Morris | 4 | | Northern | 1 | | Line | 1 | | Heads | 1 | | Black | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Market" | | 3 | "Herrera" | | 4 | "Tomás" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Line" | | 9 | "Heads" | | 10 | "Black" |
| | places | | | globalScore | 0.771 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 123 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like black feathers" |
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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 | 1914 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 202 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 34.18 | | std | 21.36 | | cv | 0.625 | | sampleLengths | | 0 | 68 | | 1 | 56 | | 2 | 83 | | 3 | 72 | | 4 | 51 | | 5 | 54 | | 6 | 31 | | 7 | 35 | | 8 | 7 | | 9 | 50 | | 10 | 37 | | 11 | 16 | | 12 | 60 | | 13 | 52 | | 14 | 29 | | 15 | 64 | | 16 | 54 | | 17 | 18 | | 18 | 38 | | 19 | 37 | | 20 | 34 | | 21 | 38 | | 22 | 66 | | 23 | 3 | | 24 | 62 | | 25 | 93 | | 26 | 51 | | 27 | 3 | | 28 | 46 | | 29 | 48 | | 30 | 15 | | 31 | 12 | | 32 | 52 | | 33 | 29 | | 34 | 19 | | 35 | 43 | | 36 | 25 | | 37 | 18 | | 38 | 22 | | 39 | 21 | | 40 | 2 | | 41 | 25 | | 42 | 22 | | 43 | 29 | | 44 | 25 | | 45 | 11 | | 46 | 9 | | 47 | 4 | | 48 | 36 | | 49 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 185 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 309 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 202 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1652 | | adjectiveStacks | 2 | | stackExamples | | 0 | "raw against sweat-slick skin." | | 1 | "same calming paramedic gesture." |
| | adverbCount | 53 | | adverbRatio | 0.03208232445520581 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.006658595641646489 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 202 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 202 | | mean | 9.48 | | std | 6.59 | | cv | 0.696 | | sampleLengths | | 0 | 11 | | 1 | 29 | | 2 | 13 | | 3 | 15 | | 4 | 10 | | 5 | 4 | | 6 | 8 | | 7 | 4 | | 8 | 23 | | 9 | 5 | | 10 | 2 | | 11 | 13 | | 12 | 12 | | 13 | 14 | | 14 | 19 | | 15 | 4 | | 16 | 17 | | 17 | 4 | | 18 | 15 | | 19 | 16 | | 20 | 6 | | 21 | 3 | | 22 | 4 | | 23 | 14 | | 24 | 3 | | 25 | 6 | | 26 | 5 | | 27 | 15 | | 28 | 4 | | 29 | 25 | | 30 | 4 | | 31 | 3 | | 32 | 10 | | 33 | 13 | | 34 | 2 | | 35 | 17 | | 36 | 8 | | 37 | 2 | | 38 | 2 | | 39 | 5 | | 40 | 9 | | 41 | 10 | | 42 | 7 | | 43 | 9 | | 44 | 9 | | 45 | 17 | | 46 | 7 | | 47 | 10 | | 48 | 19 | | 49 | 14 |
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| 61.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.38613861386138615 | | totalSentences | 202 | | uniqueOpeners | 78 | |
| 78.90% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 169 | | matches | | 0 | "Instead he shoved a bin" | | 1 | "Instead she drew her baton," | | 2 | "Further along, two figures that" | | 3 | "Then he was gone." |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 169 | | matches | | 0 | "She narrowed the gap by" | | 1 | "She used it now, lungs" | | 2 | "He did not slow." | | 3 | "His face caught the glow" | | 4 | "She had seen that face" | | 5 | "He bolted again, trainers slapping" | | 6 | "She had raided the Raven's" | | 7 | "He had to." | | 8 | "He pressed something small into" | | 9 | "He disappeared down the concrete" | | 10 | "Her hand hovered near the" | | 11 | "She had read the file." | | 12 | "His voice carried the faint" | | 13 | "She ignored the jab." | | 14 | "She still woke tasting it" | | 15 | "He kept his hands visible," | | 16 | "His gaze flicked to the" | | 17 | "She could force it, but" | | 18 | "She stepped forward." | | 19 | "His voice dropped, the Spanish" |
| | ratio | 0.284 | |
| 28.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 146 | | totalSentences | 169 | | matches | | 0 | "Quinn's boots hammered the pavement" | | 1 | "She narrowed the gap by" | | 2 | "She used it now, lungs" | | 3 | "The words sliced the air" | | 4 | "He did not slow." | | 5 | "Metal clanged against brick." | | 6 | "Quinn leapt it without breaking" | | 7 | "Morris had chased someone once." | | 8 | "The alley spat them out" | | 9 | "Camden lay ahead, its market" | | 10 | "The suspect's stride faltered for" | | 11 | "His face caught the glow" | | 12 | "Recognition flickered in Quinn." | | 13 | "She had seen that face" | | 14 | "He bolted again, trainers slapping" | | 15 | "Quinn followed, coat flapping, the" | | 16 | "Military precision kept her movements" | | 17 | "The Veil Market." | | 18 | "The suspect veered toward the" | | 19 | "Quinn's pulse kicked harder." |
| | ratio | 0.864 | |
| 29.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 169 | | matches | | 0 | "Now stalls crowded the space," |
| | ratio | 0.006 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn leapt it without breaking stride, the scar on her memory itching the way it always did when a chase turned this desperate." | | 1 | "She had raided the Raven's Nest twice in the past month, found nothing but old maps on the walls and a bartender who claimed ignorance." | | 2 | "Vendors hunched over tables laden with dried herbs that moved on their own, glass jars containing things that blinked, blades etched with symbols that hurt to l…" | | 3 | "The two tattooed women stepped apart, revealing a narrow gap behind them that led deeper into the market." | | 4 | "Figures advanced from every direction, eyes glowing, claws extended, teeth bared in smiles that promised pain." |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "His voice dropped, the Spanish lilt thickening with urgency" |
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| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "Tomás muttered (mutter)" | | 1 | "he urged (urge)" |
| | dialogueSentences | 28 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.5 | | effectiveRatio | 0.143 | |