| 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 | 1042 | | 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) | |
| 56.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1042 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "measured" | | 1 | "reminder" | | 2 | "streaming" | | 3 | "flickered" | | 4 | "resolved" | | 5 | "footsteps" | | 6 | "velvet" | | 7 | "weight" | | 8 | "scanned" |
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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 | 0 | | narrationSentences | 98 | | matches | (empty) | |
| 99.13% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | 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 | 1033 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.40% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1033 | | uniqueNames | 12 | | maxNameDensity | 1.45 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 15 | | Dean | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Morris | 2 | | Whitechapel | 1 | | Glock | 1 | | Tube | 1 | | Camden | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Camden" |
| | places | | 0 | "Soho" | | 1 | "Dean" | | 2 | "Street" | | 3 | "Whitechapel" |
| | globalScore | 0.774 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1033 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 33.32 | | std | 22.2 | | cv | 0.666 | | sampleLengths | | 0 | 52 | | 1 | 14 | | 2 | 61 | | 3 | 12 | | 4 | 63 | | 5 | 49 | | 6 | 5 | | 7 | 42 | | 8 | 48 | | 9 | 40 | | 10 | 2 | | 11 | 47 | | 12 | 18 | | 13 | 64 | | 14 | 48 | | 15 | 5 | | 16 | 54 | | 17 | 18 | | 18 | 4 | | 19 | 64 | | 20 | 66 | | 21 | 50 | | 22 | 39 | | 23 | 30 | | 24 | 2 | | 25 | 57 | | 26 | 31 | | 27 | 4 | | 28 | 31 | | 29 | 6 | | 30 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 44.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 172 | | matches | | 0 | "was climbing" | | 1 | "was already crossing" | | 2 | "was happening" | | 3 | "was standing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 1 | | flaggedSentences | 8 | | totalSentences | 98 | | ratio | 0.082 | | matches | | 0 | "The suspect moved like something that knew the architecture intimately—feet finding rungs without looking, hands gripping rusted railings without slipping." | | 1 | "No—a grate in the pavement, half-rolled back." | | 2 | "Unwise to go into that with a gun drawn; she'd lose the tactical advantage, announce herself as hostile." | | 3 | "Backup would take at least twenty minutes to get here, and she hadn't radioed her location—too much noise, too sloppy." | | 4 | "She stepped off the ladder onto a platform that had once been a Tube station platform—tiled walls, arched ceiling, tracks long since removed." | | 5 | "She didn't have a bone token—she didn't even know what that was—but no one stopped her." | | 6 | "The suspect knocked—three quick, two slow." | | 7 | "She knew—with the certainty that came from eighteen years of chasing monsters, both human and otherwise—that whatever lay beyond that door was not something she could unsee." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1045 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.023923444976076555 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0019138755980861245 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 10.54 | | std | 6.84 | | cv | 0.648 | | sampleLengths | | 0 | 8 | | 1 | 19 | | 2 | 25 | | 3 | 14 | | 4 | 9 | | 5 | 12 | | 6 | 29 | | 7 | 7 | | 8 | 4 | | 9 | 12 | | 10 | 3 | | 11 | 22 | | 12 | 4 | | 13 | 4 | | 14 | 25 | | 15 | 5 | | 16 | 9 | | 17 | 14 | | 18 | 15 | | 19 | 11 | | 20 | 5 | | 21 | 5 | | 22 | 5 | | 23 | 14 | | 24 | 5 | | 25 | 13 | | 26 | 2 | | 27 | 20 | | 28 | 14 | | 29 | 12 | | 30 | 12 | | 31 | 28 | | 32 | 2 | | 33 | 7 | | 34 | 13 | | 35 | 21 | | 36 | 6 | | 37 | 5 | | 38 | 7 | | 39 | 6 | | 40 | 18 | | 41 | 16 | | 42 | 15 | | 43 | 6 | | 44 | 1 | | 45 | 1 | | 46 | 7 | | 47 | 4 | | 48 | 11 | | 49 | 16 |
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| 47.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.336734693877551 | | totalSentences | 98 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 92 | | matches | | 0 | "She'd clocked the exchange from" | | 1 | "She was faster." | | 2 | "Her lungs were good." | | 3 | "Her legs were good." | | 4 | "She never took it off." | | 5 | "She'd broken a rib on" | | 6 | "She grabbed the bottom ladder" | | 7 | "She pulled herself up over" | | 8 | "Her shoes skidded on the" | | 9 | "She grabbed a satellite dish" | | 10 | "She reached the edge and" | | 11 | "She went down the fire" | | 12 | "She hit the ground and" | | 13 | "She'd dismissed it as folklore," | | 14 | "She holstered her weapon." | | 15 | "She checked her watch: 11:47" | | 16 | "She stepped off the ladder" | | 17 | "She didn't have a bone" | | 18 | "They were too busy, too" | | 19 | "She stood in the wet" |
| | ratio | 0.283 | |
| 36.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 92 | | matches | | 0 | "The rain had turned Soho" | | 1 | "Every streetlamp doubled itself in" | | 2 | "Detective Harlow Quinn ran with" | | 3 | "Quinn had been watching the" | | 4 | "The Raven's Nest, with its" | | 5 | "She'd clocked the exchange from" | | 6 | "Nothing she could arrest on," | | 7 | "The suspect had bolted the" | | 8 | "She was faster." | | 9 | "Her lungs were good." | | 10 | "Her legs were good." | | 11 | "The worn leather watch on" | | 12 | "She never took it off." | | 13 | "The suspect took a sharp" | | 14 | "Quinn followed without hesitation, her" | | 15 | "The alley was narrow, choked" | | 16 | "Rain funneled through a broken" | | 17 | "The hooded figure was climbing." | | 18 | "Quinn swore under her breath." | | 19 | "She'd broken a rib on" |
| | ratio | 0.848 | |
| 54.35% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 92 | | matches | | 0 | "Before he died in that" |
| | ratio | 0.011 | |
| 49.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 6 | | matches | | 0 | "Detective Harlow Quinn ran with her shoulders low and her breath coming in measured bursts, the soles of her brogues slapping against the slick pavement." | | 1 | "The suspect moved like something that knew the architecture intimately—feet finding rungs without looking, hands gripping rusted railings without slipping." | | 2 | "The suspect was already crossing to the far edge of the building, moving toward a fire escape on the opposite side that led down to a smaller street." | | 3 | "A market that moved with the full moon, hidden in the abandoned spaces beneath the city." | | 4 | "She stood in the wet darkness, rain still dripping from her hair, her heart hammering against her ribs." | | 5 | "She knew—with the certainty that came from eighteen years of chasing monsters, both human and otherwise—that whatever lay beyond that door was not something she…" |
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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 | |