| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 84.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1654 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | | | 3 | |
| | highlights | | 0 | "slightly" | | 1 | "deliberately" | | 2 | "sharply" | | 3 | "slowly" |
| |
| 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) | |
| 30.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1654 | | totalAiIsms | 23 | | found | | | highlights | | 0 | "fractured" | | 1 | "mosaic" | | 2 | "perfect" | | 3 | "standard" | | 4 | "scanned" | | 5 | "weight" | | 6 | "synthetic" | | 7 | "unreadable" | | 8 | "wavered" | | 9 | "etched" | | 10 | "mechanical" | | 11 | "magnetic" | | 12 | "familiar" | | 13 | "unwavering" | | 14 | "shimmered" |
| |
| 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 | 186 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 186 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 186 | | 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 | 1654 | | ratio | 0 | | matches | (empty) | |
| 20.83% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 5 | | matches | | 0 | "Fentanyl ring, he said without turning." | | 1 | "Miller, she called over her shoulder." | | 2 | "It exists, Quinn said." | | 3 | "We file the report you need to sleep at night, Quinn said." | | 4 | "I know, Quinn said." |
| |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1654 | | uniqueNames | 22 | | maxNameDensity | 0.91 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 15 | | Metropolitan | 1 | | Police | 1 | | Sergeant | 1 | | Miller | 11 | | Detective | 6 | | Savile | 1 | | Row | 1 | | Path | 3 | | Commissioner | 1 | | Veil | 2 | | Compass | 1 | | Morris | 2 | | Market | 1 | | British | 1 | | Museum | 1 | | Ancient | 1 | | History | 1 | | Oxford | 1 | | You | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Sergeant" | | 4 | "Miller" | | 5 | "Commissioner" | | 6 | "Compass" | | 7 | "Morris" | | 8 | "Market" | | 9 | "You" |
| | places | | 0 | "Savile" | | 1 | "Veil" | | 2 | "British" | | 3 | "Ancient" | | 4 | "Oxford" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 111 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.605 | | wordCount | 1654 | | matches | | 0 | "not dismissing the odd details, but the evidence points to trafficking" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 186 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 51.69 | | std | 33.85 | | cv | 0.655 | | sampleLengths | | 0 | 110 | | 1 | 75 | | 2 | 28 | | 3 | 38 | | 4 | 121 | | 5 | 61 | | 6 | 78 | | 7 | 6 | | 8 | 37 | | 9 | 7 | | 10 | 39 | | 11 | 75 | | 12 | 15 | | 13 | 22 | | 14 | 67 | | 15 | 50 | | 16 | 80 | | 17 | 25 | | 18 | 120 | | 19 | 25 | | 20 | 8 | | 21 | 73 | | 22 | 18 | | 23 | 45 | | 24 | 35 | | 25 | 12 | | 26 | 52 | | 27 | 14 | | 28 | 107 | | 29 | 52 | | 30 | 62 | | 31 | 97 |
| |
| 92.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 186 | | matches | | 0 | "been forced" | | 1 | "were disturbed" | | 2 | "was placed" | | 3 | "was embedded" | | 4 | "trained" | | 5 | "been mishandled" | | 6 | "was etched" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 296 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 186 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1665 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.022822822822822823 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.00960960960960961 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 186 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 186 | | mean | 8.89 | | std | 6.26 | | cv | 0.704 | | sampleLengths | | 0 | 29 | | 1 | 25 | | 2 | 22 | | 3 | 12 | | 4 | 22 | | 5 | 12 | | 6 | 12 | | 7 | 18 | | 8 | 5 | | 9 | 28 | | 10 | 13 | | 11 | 15 | | 12 | 6 | | 13 | 6 | | 14 | 15 | | 15 | 5 | | 16 | 6 | | 17 | 3 | | 18 | 19 | | 19 | 16 | | 20 | 7 | | 21 | 22 | | 22 | 9 | | 23 | 5 | | 24 | 28 | | 25 | 2 | | 26 | 2 | | 27 | 8 | | 28 | 6 | | 29 | 6 | | 30 | 16 | | 31 | 2 | | 32 | 5 | | 33 | 8 | | 34 | 4 | | 35 | 6 | | 36 | 4 | | 37 | 4 | | 38 | 13 | | 39 | 9 | | 40 | 14 | | 41 | 15 | | 42 | 15 | | 43 | 12 | | 44 | 4 | | 45 | 2 | | 46 | 2 | | 47 | 16 | | 48 | 7 | | 49 | 12 |
| |
| 55.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3763440860215054 | | totalSentences | 186 | | uniqueOpeners | 70 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 166 | | matches | | 0 | "Probably handled some reagent without" | | 1 | "Then the body was placed." | | 2 | "Just a ledger full of" | | 3 | "Then, with a quiet mechanical" | | 4 | "Instead, she palmed the compass," | | 5 | "Always wears round glasses and" | | 6 | "Somewhere ahead, beyond the collapsed" |
| | ratio | 0.042 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 166 | | matches | | 0 | "She knew the difference between" | | 1 | "He was twenty-nine, earnest, and" | | 2 | "We found three vacuum-sealed bags," | | 3 | "You're early for the briefing," | | 4 | "She stepped past him, boots" | | 5 | "Her posture held the rigid" | | 6 | "She ran her torch along" | | 7 | "You got the toxicology request" | | 8 | "She stood, the sharp line" | | 9 | "It had compressed evenly, as" | | 10 | "They said a rat scuttled" | | 11 | "We got neither." | | 12 | "Her torch swept over the" | | 13 | "She knelt again, pressing two" | | 14 | "They're lunar cycles." | | 15 | "It's a threshold." | | 16 | "I'm not dismissing the odd" | | 17 | "Her brown eyes were flat," | | 18 | "You're looking at the surface" | | 19 | "They're trafficking in thresholds." |
| | ratio | 0.289 | |
| 98.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 120 | | totalSentences | 166 | | matches | | 0 | "The rain had turned the" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "She knew the difference between" | | 3 | "The maintenance hatch to the" | | 4 | "Iron doors buckled inward, hinges" | | 5 | "Quinn clicked on her torch." | | 6 | "The beam carved a hard" | | 7 | "Detective Sergeant Miller stood over" | | 8 | "He was twenty-nine, earnest, and" | | 9 | "Fentanyl ring, he said without" | | 10 | "We found three vacuum-sealed bags," | | 11 | "Council has the perimeter locked." | | 12 | "You're early for the briefing," | | 13 | "Quinn said nothing." | | 14 | "She stepped past him, boots" | | 15 | "Her posture held the rigid" | | 16 | "She ran her torch along" | | 17 | "The face was pale, features" | | 18 | "The left palm bore a" | | 19 | "The tissue looked less burned" |
| | ratio | 0.723 | |
| 60.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 166 | | matches | | 0 | "If anyone knows what these" | | 1 | "if this is what you" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 4 | | matches | | 0 | "Eighteen years of decorated service with the Metropolitan Police had hardened her into a woman who read crime scenes like topographic maps." | | 1 | "Male, mid-forties, dressed in a tailored wool coat that belonged in a Savile Row archive rather than a damp underground transit tunnel." | | 2 | "The face was etched with protective sigils that caught the torchlight like shallow cuts, crafted by hands that understood what lay beyond the veil." | | 3 | "Cold air washed over her face, carrying a faint sound like dry leaves skittering across glass." |
| |
| 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 | |