| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.263 | | leniency | 0.526 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1624 | | totalAiIsmAdverbs | 1 | | 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) | |
| 78.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1624 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "footsteps" | | 2 | "echoing" | | 3 | "echoed" | | 4 | "flicker" | | 5 | "etched" | | 6 | "pulsed" |
| |
| 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 | 125 | | matches | (empty) | |
| 97.14% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 125 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 140 | | 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 | 1625 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 70 | | wordCount | 1383 | | uniqueNames | 24 | | maxNameDensity | 1.23 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Quinn | 17 | | Old | 1 | | Compton | 1 | | Street | 3 | | Raven | 3 | | Nest | 4 | | Tomás | 1 | | Herrera | 13 | | Charing | 1 | | Cross | 2 | | Road | 1 | | Saint | 1 | | Christopher | 1 | | Greek | 1 | | Morris | 4 | | Camden | 4 | | West | 1 | | End | 1 | | Town | 1 | | High | 1 | | Tube | 3 | | Veil | 1 | | Market | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Old" | | 2 | "Compton" | | 3 | "Street" | | 4 | "Charing" | | 5 | "Cross" | | 6 | "Road" | | 7 | "Greek" | | 8 | "Camden" | | 9 | "West" | | 10 | "End" | | 11 | "Town" | | 12 | "High" |
| | globalScore | 0.885 | | windowScore | 0.833 | |
| 64.77% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 3 | | matches | | 0 | "sigil that seemed to shift when she stared at it" | | 1 | "not quite human, and the clink of glass vials" | | 2 | "smelled like wet earth and incense" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.615 | | wordCount | 1625 | | matches | | 0 | "not the front of the bar, but the narrow service alley" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 140 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 31.25 | | std | 22.35 | | cv | 0.715 | | sampleLengths | | 0 | 8 | | 1 | 79 | | 2 | 34 | | 3 | 59 | | 4 | 11 | | 5 | 43 | | 6 | 45 | | 7 | 11 | | 8 | 2 | | 9 | 24 | | 10 | 4 | | 11 | 10 | | 12 | 67 | | 13 | 8 | | 14 | 33 | | 15 | 27 | | 16 | 15 | | 17 | 18 | | 18 | 20 | | 19 | 2 | | 20 | 6 | | 21 | 62 | | 22 | 2 | | 23 | 90 | | 24 | 3 | | 25 | 35 | | 26 | 31 | | 27 | 10 | | 28 | 39 | | 29 | 53 | | 30 | 60 | | 31 | 42 | | 32 | 40 | | 33 | 40 | | 34 | 66 | | 35 | 5 | | 36 | 19 | | 37 | 36 | | 38 | 18 | | 39 | 4 | | 40 | 37 | | 41 | 48 | | 42 | 39 | | 43 | 25 | | 44 | 11 | | 45 | 39 | | 46 | 39 | | 47 | 78 | | 48 | 16 | | 49 | 21 |
| |
| 94.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 125 | | matches | | 0 | "been pulled" | | 1 | "was supposed" | | 2 | "was supposed" | | 3 | "was bent" |
| |
| 34.71% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 242 | | matches | | 0 | "wasn't telling" | | 1 | "was trying" | | 2 | "wasn't trying" | | 3 | "wasn't ducking" | | 4 | "was heading" | | 5 | "was going" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 140 | | ratio | 0.007 | | matches | | 0 | "The old entrance to the abandoned Tube station, bricked up since the seventies, fenced off with corrugated iron and a council sign that said DANGER - KEEP OUT in letters half washed away." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 480 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.03125 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.008333333333333333 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 140 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 140 | | mean | 11.61 | | std | 8.03 | | cv | 0.692 | | sampleLengths | | 0 | 8 | | 1 | 36 | | 2 | 10 | | 3 | 17 | | 4 | 16 | | 5 | 10 | | 6 | 1 | | 7 | 23 | | 8 | 4 | | 9 | 11 | | 10 | 24 | | 11 | 20 | | 12 | 7 | | 13 | 4 | | 14 | 22 | | 15 | 21 | | 16 | 11 | | 17 | 10 | | 18 | 13 | | 19 | 11 | | 20 | 11 | | 21 | 2 | | 22 | 2 | | 23 | 11 | | 24 | 11 | | 25 | 4 | | 26 | 10 | | 27 | 2 | | 28 | 20 | | 29 | 31 | | 30 | 14 | | 31 | 8 | | 32 | 21 | | 33 | 12 | | 34 | 27 | | 35 | 3 | | 36 | 12 | | 37 | 18 | | 38 | 4 | | 39 | 10 | | 40 | 6 | | 41 | 2 | | 42 | 6 | | 43 | 3 | | 44 | 22 | | 45 | 13 | | 46 | 12 | | 47 | 12 | | 48 | 2 | | 49 | 3 |
| |
| 47.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.33093525179856115 | | totalSentences | 139 | | uniqueOpeners | 46 | |
| 29.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 113 | | matches | | 0 | "Then she clicked her torch" |
| | ratio | 0.009 | |
| 81.95% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 113 | | matches | | 0 | "She had watched Tomás Herrera" | | 1 | "She checked the worn leather" | | 2 | "She knew the layout." | | 3 | "She'd never had a warrant" | | 4 | "Her radio crackled low against" | | 5 | "She turned it down." | | 6 | "He moved like a man" | | 7 | "His head snapped around and" | | 8 | "He looked over his shoulder" | | 9 | "He was fast." | | 10 | "He cut hard left onto" | | 11 | "He didn't stop." | | 12 | "He vaulted a stack of" | | 13 | "She pushed harder." | | 14 | "He glanced back, saw she" | | 15 | "He wasn't ducking into doorways." | | 16 | "He was heading somewhere specific." | | 17 | "He wanted to get to" | | 18 | "Her breath sawed." | | 19 | "He hit Camden High Street" |
| | ratio | 0.345 | |
| 30.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 113 | | matches | | 0 | "The rain had turned Soho" | | 1 | "Harlow Quinn kept to the" | | 2 | "The windows were dark, the" | | 3 | "She had watched Tomás Herrera" | | 4 | "She checked the worn leather" | | 5 | "The strap was soft with" | | 6 | "She knew the layout." | | 7 | "The Nest had a hidden" | | 8 | "She'd never had a warrant" | | 9 | "Men like Herrera went into" | | 10 | "Her radio crackled low against" | | 11 | "She turned it down." | | 12 | "A door slammed somewhere to" | | 13 | "Quinn moved with military precision," | | 14 | "Herrera came out of the" | | 15 | "He moved like a man" | | 16 | "Quinn let him get ten" | | 17 | "His head snapped around and" | | 18 | "He looked over his shoulder" | | 19 | "The scar running along his" |
| | ratio | 0.858 | |
| 44.25% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 113 | | matches | | | ratio | 0.009 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn followed, her knee twinging, clipping the top crate and sending it skittering." | | 1 | "The old entrance to the abandoned Tube station, bricked up since the seventies, fenced off with corrugated iron and a council sign that said DANGER - KEEP OUT i…" | | 2 | "A hidden supernatural black market that sold enchanted goods and information." | | 3 | "He was bent over with his hands on his knees, breathing hard, water dripping from his curly hair down his neck to the medallion." | | 4 | "Many voices, low and overlapping, and a flicker of amber light that had no business being on in a station with no power." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.263 | | leniency | 0.526 | | rawRatio | 0 | | effectiveRatio | 0 | |