| 91.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 2 | | adverbTags | | 0 | "Herrera's voice dropped further [further]" | | 1 | "He laughed once [once]" |
| | dialogueSentences | 37 | | tagDensity | 0.486 | | leniency | 0.973 | | rawRatio | 0.111 | | effectiveRatio | 0.108 | |
| 94.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1722 | | 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) | |
| 59.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1722 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "treacherous" | | 1 | "glint" | | 2 | "flickered" | | 3 | "velvet" | | 4 | "lilt" | | 5 | "echoed" | | 6 | "predator" | | 7 | "shattered" | | 8 | "pulsed" | | 9 | "footsteps" | | 10 | "glistening" | | 11 | "weight" |
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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 | 0 | | narrationSentences | 166 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 166 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 185 | | 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 | 1723 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1384 | | uniqueNames | 15 | | maxNameDensity | 1.45 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 20 | | Soho | 1 | | Morris | 4 | | Herrera | 19 | | Raven | 2 | | Nest | 2 | | Greek | 1 | | Street | 1 | | Tube | 1 | | Veil | 1 | | Market | 3 | | Spanish | 3 | | Saint | 2 | | Christopher | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Nest" | | 6 | "Market" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | | globalScore | 0.777 | | windowScore | 0.667 | |
| 58.26% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 109 | | glossingSentenceCount | 4 | | matches | | 0 | "looked like liquid midnight" | | 1 | "as if expecting a blow that had already landed years ago" | | 2 | "looked like bones but moved" | | 3 | "quite human" |
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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 | 1723 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 185 | | matches | | 0 | "examine that possibility" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 65 | | mean | 26.51 | | std | 17.91 | | cv | 0.676 | | sampleLengths | | 0 | 50 | | 1 | 45 | | 2 | 61 | | 3 | 46 | | 4 | 57 | | 5 | 25 | | 6 | 18 | | 7 | 2 | | 8 | 84 | | 9 | 3 | | 10 | 30 | | 11 | 17 | | 12 | 55 | | 13 | 22 | | 14 | 58 | | 15 | 40 | | 16 | 5 | | 17 | 19 | | 18 | 29 | | 19 | 31 | | 20 | 29 | | 21 | 22 | | 22 | 28 | | 23 | 34 | | 24 | 9 | | 25 | 44 | | 26 | 41 | | 27 | 5 | | 28 | 22 | | 29 | 31 | | 30 | 11 | | 31 | 6 | | 32 | 1 | | 33 | 14 | | 34 | 34 | | 35 | 2 | | 36 | 48 | | 37 | 5 | | 38 | 36 | | 39 | 31 | | 40 | 24 | | 41 | 36 | | 42 | 28 | | 43 | 25 | | 44 | 54 | | 45 | 5 | | 46 | 21 | | 47 | 4 | | 48 | 31 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 166 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 268 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 185 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1390 | | adjectiveStacks | 1 | | stackExamples | | 0 | "thicker, heavier, pressing against her" |
| | adverbCount | 51 | | adverbRatio | 0.03669064748201439 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.01223021582733813 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 185 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 185 | | mean | 9.31 | | std | 5.97 | | cv | 0.641 | | sampleLengths | | 0 | 24 | | 1 | 12 | | 2 | 14 | | 3 | 11 | | 4 | 24 | | 5 | 5 | | 6 | 5 | | 7 | 5 | | 8 | 12 | | 9 | 2 | | 10 | 25 | | 11 | 10 | | 12 | 7 | | 13 | 14 | | 14 | 13 | | 15 | 7 | | 16 | 12 | | 17 | 18 | | 18 | 16 | | 19 | 7 | | 20 | 2 | | 21 | 14 | | 22 | 9 | | 23 | 5 | | 24 | 4 | | 25 | 7 | | 26 | 12 | | 27 | 6 | | 28 | 2 | | 29 | 17 | | 30 | 9 | | 31 | 16 | | 32 | 13 | | 33 | 29 | | 34 | 3 | | 35 | 6 | | 36 | 9 | | 37 | 2 | | 38 | 13 | | 39 | 3 | | 40 | 6 | | 41 | 5 | | 42 | 3 | | 43 | 17 | | 44 | 11 | | 45 | 10 | | 46 | 17 | | 47 | 11 | | 48 | 11 | | 49 | 3 |
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| 70.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.42702702702702705 | | totalSentences | 185 | | uniqueOpeners | 79 | |
| 47.28% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 141 | | matches | | 0 | "Instead, they crawled with living" | | 1 | "Somewhere far above, the rain" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 141 | | matches | | 0 | "she shouted, but the words" | | 1 | "Her worn leather watch caught" | | 2 | "She never wound it anymore." | | 3 | "She had watched him slip" | | 4 | "Her salt-and-pepper hair clung to" | | 5 | "She rounded the corner in" | | 6 | "She drew her baton, not" | | 7 | "She still heard his screams" | | 8 | "she muttered to herself, testing" | | 9 | "It tasted of copper and" | | 10 | "She had heard rumours, of" | | 11 | "She moved along the platform" | | 12 | "it whispered in a voice" | | 13 | "Her focus stayed on Herrera's" | | 14 | "He stopped at a stall" | | 15 | "Their accents mixed Spanish curses" | | 16 | "He slipped it into his" | | 17 | "His warm brown eyes held" | | 18 | "she said, voice flat" | | 19 | "He glanced at the crowd," |
| | ratio | 0.298 | |
| 16.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 125 | | totalSentences | 141 | | matches | | 0 | "Detective Harlow Quinn slammed her" | | 1 | "Rain lashed the Soho pavements," | | 2 | "The suspect moved with unnatural" | | 3 | "she shouted, but the words" | | 4 | "Her worn leather watch caught" | | 5 | "She never wound it anymore." | | 6 | "Reminders kept a detective sharp." | | 7 | "The figure glanced back once." | | 8 | "A flash of olive skin," | | 9 | "She had watched him slip" | | 10 | "Tonight, he would lead her" | | 11 | "Herrera vaulted a low railing" | | 12 | "Quinn followed, boots splashing through" | | 13 | "Her salt-and-pepper hair clung to" | | 14 | "Military precision kept her strides" | | 15 | "She rounded the corner in" | | 16 | "The green neon of The" | | 17 | "She drew her baton, not" | | 18 | "Rules existed for a reason," | | 19 | "The door led to concrete" |
| | ratio | 0.887 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 141 | | matches | (empty) | | ratio | 0 | |
| 51.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 7 | | matches | | 0 | "Lanterns swung from makeshift rigging, throwing warped shadows across tiled walls that should have been derelict." | | 1 | "Vendors hunched over tables piled with things that should not exist: vials of liquid starlight, knives forged from frozen screams, books bound in what looked su…" | | 2 | "Their accents mixed Spanish curses with something older, something that made her teeth ache." | | 3 | "He slipped it into his coat, next to the Saint Christopher medallion that glinted mockingly." | | 4 | "He grunted but held on, dragging her toward a rusted gate that led deeper into the abandoned tunnels." | | 5 | "Beyond lay tunnels that shouldn't exist, lit by floating orbs that pulsed like diseased hearts." | | 6 | "Herrera's footsteps echoed ahead, steady despite the scar that must have pained him." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 2 | | matches | | 0 | "she shouted, but the words dissolved in the downpour" | | 1 | "she said, voice flat" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 12 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she muttered (mutter)" | | 2 | "it whispered (whisper)" | | 3 | "she spat (spit)" | | 4 | "she demanded (demand)" | | 5 | "she ordered (order)" | | 6 | "He laughed once (laugh)" | | 7 | "Herrera shouted (shout)" | | 8 | "he hissed (hiss)" | | 9 | "he yelled (yell)" | | 10 | "she panted (pant)" | | 11 | "he warned (warn)" |
| | dialogueSentences | 37 | | tagDensity | 0.405 | | leniency | 0.811 | | rawRatio | 0.8 | | effectiveRatio | 0.649 | |