| 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 | 1077 | | 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) | |
| 39.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1077 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "flicker" | | 1 | "chill" | | 2 | "weight" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "silence" | | 6 | "etched" | | 7 | "velvet" | | 8 | "predictable" |
| |
| 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 | 69 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 69 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 79 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1075 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 936 | | uniqueNames | 13 | | maxNameDensity | 1.07 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 10 | | Herrera | 1 | | Victorian | 2 | | Saint | 1 | | Christopher | 1 | | London | 2 | | Underground | 1 | | Tomás | 7 | | Morris | 1 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Victorian" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Morris" |
| | places | | | globalScore | 0.966 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | 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 | 1075 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 79 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 30.71 | | std | 19.94 | | cv | 0.649 | | sampleLengths | | 0 | 20 | | 1 | 51 | | 2 | 7 | | 3 | 71 | | 4 | 50 | | 5 | 14 | | 6 | 11 | | 7 | 21 | | 8 | 51 | | 9 | 48 | | 10 | 23 | | 11 | 43 | | 12 | 25 | | 13 | 57 | | 14 | 11 | | 15 | 12 | | 16 | 16 | | 17 | 14 | | 18 | 12 | | 19 | 47 | | 20 | 41 | | 21 | 19 | | 22 | 24 | | 23 | 10 | | 24 | 39 | | 25 | 35 | | 26 | 8 | | 27 | 98 | | 28 | 41 | | 29 | 21 | | 30 | 16 | | 31 | 18 | | 32 | 25 | | 33 | 40 | | 34 | 36 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 69 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 147 | | matches | (empty) | |
| 70.52% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 79 | | ratio | 0.025 | | matches | | 0 | "Below her, footsteps echoed—hasty, erratic, slapping against stagnant pools of water." | | 1 | "A dry, biting frost touched Quinn’s cheek—the exact same sudden cold that had filled the sealed evidence vault three years ago, the night DS Morris died with his eyes wide and his chest crushed from the inside out." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 943 | | adjectiveStacks | 1 | | stackExamples | | 0 | "over cracked white Victorian" |
| | adverbCount | 11 | | adverbRatio | 0.01166489925768823 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0021208907741251328 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 79 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 79 | | mean | 13.61 | | std | 6.04 | | cv | 0.444 | | sampleLengths | | 0 | 20 | | 1 | 16 | | 2 | 16 | | 3 | 9 | | 4 | 10 | | 5 | 7 | | 6 | 5 | | 7 | 18 | | 8 | 18 | | 9 | 10 | | 10 | 20 | | 11 | 8 | | 12 | 7 | | 13 | 21 | | 14 | 14 | | 15 | 14 | | 16 | 11 | | 17 | 7 | | 18 | 14 | | 19 | 18 | | 20 | 15 | | 21 | 18 | | 22 | 10 | | 23 | 17 | | 24 | 8 | | 25 | 13 | | 26 | 5 | | 27 | 18 | | 28 | 7 | | 29 | 11 | | 30 | 10 | | 31 | 15 | | 32 | 7 | | 33 | 9 | | 34 | 9 | | 35 | 11 | | 36 | 18 | | 37 | 18 | | 38 | 10 | | 39 | 11 | | 40 | 12 | | 41 | 3 | | 42 | 13 | | 43 | 14 | | 44 | 12 | | 45 | 8 | | 46 | 17 | | 47 | 22 | | 48 | 8 | | 49 | 11 |
| |
| 57.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4050632911392405 | | totalSentences | 79 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 69 | | matches | | 0 | "He clutched a canvas trauma" | | 1 | "He hooked a sharp left" | | 2 | "His sneakers skidded across wet" | | 3 | "Her salt-and-pepper hair dripped into" | | 4 | "She wiped her forehead with" | | 5 | "Her right hand rested against" | | 6 | "It clattered across the alley," | | 7 | "She vaulted the metal edge" | | 8 | "Her worn leather watch scraped" | | 9 | "He threw his weight against" | | 10 | "He squeezed through the gap." | | 11 | "She drew her tactical flashlight" | | 12 | "It carried no scent of" | | 13 | "It smelled of sulfur, burned" | | 14 | "She kept her back to" | | 15 | "She rounded the final spiral" | | 16 | "He reached into his leather" | | 17 | "He pressed it into a" | | 18 | "He looked back at her" | | 19 | "He turned and melted into" |
| | ratio | 0.333 | |
| 39.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 69 | | matches | | 0 | "Rain came down in sheets" | | 1 | "Detective Harlow Quinn lunged across" | | 2 | "Water sprayed from beneath his" | | 3 | "He clutched a canvas trauma" | | 4 | "Tomás Herrera did not slow." | | 5 | "He hooked a sharp left" | | 6 | "His sneakers skidded across wet" | | 7 | "The rain flattened his dark" | | 8 | "Quinn rounded the corner three" | | 9 | "Her salt-and-pepper hair dripped into" | | 10 | "She wiped her forehead with" | | 11 | "Her right hand rested against" | | 12 | "Tomás shoved a rusted wheelie" | | 13 | "It clattered across the alley," | | 14 | "She vaulted the metal edge" | | 15 | "Her worn leather watch scraped" | | 16 | "Tomás reached a dead end" | | 17 | "He threw his weight against" | | 18 | "The wood splintered with a" | | 19 | "He squeezed through the gap." |
| | ratio | 0.841 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 84.55% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 4 | | matches | | 0 | "The beam caught Tomás standing before a massive iron security grille that blocked the defunct platform tunnel." | | 1 | "The gate was forged from heavy, blackened iron, etched with deep geometric grooves that did not belong to any municipal transit authority." | | 2 | "Figures in heavy cloaks and tailored coats moved between the stalls, bartering in low, resonant whispers that made Quinn’s teeth ache." | | 3 | "Ahead lay a labyrinth that defied every report she had ever filed, reeking of the cold mystery that had killed her partner." |
| |
| 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 | |