| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 71.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 877 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "very" | | 1 | "suddenly" | | 2 | "cautiously" |
| |
| 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) | |
| 14.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 877 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "silence" | | 1 | "cacophony" | | 2 | "pounding" | | 3 | "depths" | | 4 | "flickered" | | 5 | "electric" | | 6 | "sinister" | | 7 | "hulking" | | 8 | "charged" | | 9 | "racing" | | 10 | "loomed" | | 11 | "fluttered" |
| |
| 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 | 74 | | matches | (empty) | |
| 84.94% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "tried to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 878 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 757 | | uniqueNames | 8 | | maxNameDensity | 2.64 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 2 | | Harlow | 2 | | Quinn | 20 | | London | 1 | | European | 1 | | Saint | 1 | | Christopher | 1 | | Scene | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Scene" |
| | places | | | globalScore | 0.179 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | 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 | 878 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 82 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 23.11 | | std | 15.97 | | cv | 0.691 | | sampleLengths | | 0 | 54 | | 1 | 32 | | 2 | 27 | | 3 | 30 | | 4 | 23 | | 5 | 30 | | 6 | 31 | | 7 | 23 | | 8 | 31 | | 9 | 22 | | 10 | 16 | | 11 | 18 | | 12 | 35 | | 13 | 13 | | 14 | 19 | | 15 | 6 | | 16 | 2 | | 17 | 10 | | 18 | 26 | | 19 | 4 | | 20 | 29 | | 21 | 60 | | 22 | 27 | | 23 | 45 | | 24 | 28 | | 25 | 4 | | 26 | 25 | | 27 | 20 | | 28 | 3 | | 29 | 20 | | 30 | 60 | | 31 | 3 | | 32 | 53 | | 33 | 1 | | 34 | 17 | | 35 | 1 | | 36 | 27 | | 37 | 3 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 74 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 143 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 82 | | ratio | 0.012 | | matches | | 0 | "Arrest, interrogation, case closure--the obligations of her badge." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 648 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.029320987654320986 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.016975308641975308 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 10.71 | | std | 8.91 | | cv | 0.832 | | sampleLengths | | 0 | 18 | | 1 | 14 | | 2 | 3 | | 3 | 19 | | 4 | 32 | | 5 | 5 | | 6 | 2 | | 7 | 20 | | 8 | 10 | | 9 | 20 | | 10 | 15 | | 11 | 8 | | 12 | 16 | | 13 | 5 | | 14 | 9 | | 15 | 9 | | 16 | 4 | | 17 | 18 | | 18 | 12 | | 19 | 2 | | 20 | 9 | | 21 | 10 | | 22 | 8 | | 23 | 7 | | 24 | 6 | | 25 | 7 | | 26 | 5 | | 27 | 4 | | 28 | 6 | | 29 | 9 | | 30 | 5 | | 31 | 2 | | 32 | 1 | | 33 | 4 | | 34 | 10 | | 35 | 2 | | 36 | 1 | | 37 | 13 | | 38 | 13 | | 39 | 9 | | 40 | 13 | | 41 | 3 | | 42 | 16 | | 43 | 5 | | 44 | 1 | | 45 | 2 | | 46 | 10 | | 47 | 4 | | 48 | 9 | | 49 | 13 |
| |
| 79.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5121951219512195 | | totalSentences | 82 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 64 | | matches | | 0 | "Discreetly, Quinn checked her watch." | | 1 | "Suddenly, a monstrous howl pierced" | | 2 | "Cautiously, she inched toward the" | | 3 | "Then, a soft click." | | 4 | "Dim electric light bathed the" |
| | ratio | 0.078 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 64 | | matches | | 0 | "Her breath billowed in the" | | 1 | "Her prey, now scarcely ahead," | | 2 | "She emerged in a cramped" | | 3 | "Its lips curled from teeth" | | 4 | "She was on her back" | | 5 | "he murmured in an lilting" | | 6 | "She sat up abruptly, an" | | 7 | "He held her firm." |
| | ratio | 0.125 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 64 | | matches | | 0 | "The rain pounded the pavement" | | 1 | "Her breath billowed in the" | | 2 | "Rain sluiced down her face" | | 3 | "This case had morphed, putting" | | 4 | "The eerie silence of the" | | 5 | "Her prey, now scarcely ahead," | | 6 | "The cover was illuminating this" | | 7 | "The suspect must have sensed" | | 8 | "Quinn ran forward, heart pounding." | | 9 | "A hidden door yawned open," | | 10 | "Quinn glanced back at the" | | 11 | "A muffled scream from the" | | 12 | "Quinn drew her pistol and" | | 13 | "She emerged in a cramped" | | 14 | "Quinn crept forward, senses on" | | 15 | "A faint scuffle came from" | | 16 | "Quinn froze, blood running cold." | | 17 | "The candlelight flickered and died." | | 18 | "A hulking grey wolf, easily" | | 19 | "Its lips curled from teeth" |
| | ratio | 0.719 | |
| 78.13% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 64 | | matches | | 0 | "If that scene had been" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "he murmured (murmur)" | | 1 | "Tommy explained (explain)" |
| | dialogueSentences | 14 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 1 | | effectiveRatio | 0.286 | |