| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.18 | | leniency | 0.36 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1753 | | 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) | |
| 94.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1753 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 149 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 149 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 190 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1753 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 77 | | wordCount | 1434 | | uniqueNames | 14 | | maxNameDensity | 2.37 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Town | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 34 | | Saint | 1 | | Christopher | 1 | | Herrera | 27 | | High | 1 | | Street | 1 | | Raven | 1 | | Nest | 2 | | Morris | 1 | | Blood | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Nest" | | 6 | "Morris" | | 7 | "Blood" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "High" | | 3 | "Street" | | 4 | "Raven" |
| | globalScore | 0.315 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | 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 | 1753 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 190 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 86 | | mean | 20.38 | | std | 20.21 | | cv | 0.991 | | sampleLengths | | 0 | 48 | | 1 | 3 | | 2 | 32 | | 3 | 52 | | 4 | 3 | | 5 | 46 | | 6 | 63 | | 7 | 19 | | 8 | 15 | | 9 | 12 | | 10 | 18 | | 11 | 8 | | 12 | 5 | | 13 | 17 | | 14 | 24 | | 15 | 19 | | 16 | 66 | | 17 | 11 | | 18 | 4 | | 19 | 29 | | 20 | 4 | | 21 | 27 | | 22 | 8 | | 23 | 11 | | 24 | 7 | | 25 | 24 | | 26 | 5 | | 27 | 10 | | 28 | 4 | | 29 | 5 | | 30 | 27 | | 31 | 10 | | 32 | 2 | | 33 | 39 | | 34 | 44 | | 35 | 5 | | 36 | 5 | | 37 | 44 | | 38 | 10 | | 39 | 7 | | 40 | 52 | | 41 | 46 | | 42 | 14 | | 43 | 8 | | 44 | 3 | | 45 | 11 | | 46 | 2 | | 47 | 31 | | 48 | 10 | | 49 | 70 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 248 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 190 | | ratio | 0.011 | | matches | | 0 | "One hand caught Herrera’s elbow; the other took the white disc." | | 1 | "Behind her, rain struck the pavement; ahead, the stairs dropped past the reach of the work lamp." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1436 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.01532033426183844 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0006963788300835655 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 190 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 190 | | mean | 9.23 | | std | 5.7 | | cv | 0.617 | | sampleLengths | | 0 | 21 | | 1 | 5 | | 2 | 6 | | 3 | 16 | | 4 | 3 | | 5 | 3 | | 6 | 8 | | 7 | 19 | | 8 | 2 | | 9 | 9 | | 10 | 13 | | 11 | 19 | | 12 | 11 | | 13 | 3 | | 14 | 10 | | 15 | 5 | | 16 | 12 | | 17 | 6 | | 18 | 13 | | 19 | 20 | | 20 | 12 | | 21 | 2 | | 22 | 3 | | 23 | 26 | | 24 | 14 | | 25 | 5 | | 26 | 9 | | 27 | 6 | | 28 | 12 | | 29 | 3 | | 30 | 15 | | 31 | 8 | | 32 | 5 | | 33 | 6 | | 34 | 11 | | 35 | 24 | | 36 | 5 | | 37 | 6 | | 38 | 5 | | 39 | 3 | | 40 | 6 | | 41 | 20 | | 42 | 10 | | 43 | 17 | | 44 | 13 | | 45 | 4 | | 46 | 7 | | 47 | 4 | | 48 | 2 | | 49 | 4 |
| |
| 61.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3894736842105263 | | totalSentences | 190 | | uniqueOpeners | 74 | |
| 23.98% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 139 | | matches | | 0 | "Somewhere below, machinery clanked into" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 139 | | matches | | 0 | "Its rider went over the" | | 1 | "His hand went to the" | | 2 | "She caught a glimpse of" | | 3 | "Her shoes struck wet pavement" | | 4 | "She knew that scar from" | | 5 | "He had left without it." | | 6 | "He knocked into a woman’s" | | 7 | "She stopped short, let it" | | 8 | "She caught her name, then" | | 9 | "She tried again." | | 10 | "He cut through a side" | | 11 | "He leaned against the boards," | | 12 | "He raised one." | | 13 | "His other hand stayed pressed" | | 14 | "She twisted free and forced" | | 15 | "She had come in without" | | 16 | "He looked up at Quinn." | | 17 | "It showed a pattern of" | | 18 | "Its knuckles bent where knuckles" | | 19 | "She had kept the recording" |
| | ratio | 0.23 | |
| 71.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 139 | | matches | | 0 | "The man came out of" | | 1 | "The bicycle hit the kerb." | | 2 | "Its rider went over the" | | 3 | "Herrera looked back." | | 4 | "Rain flattened his dark curls" | | 5 | "His hand went to the" | | 6 | "Quinn stepped around the fallen" | | 7 | "A bus swung between them," | | 8 | "She caught a glimpse of" | | 9 | "Quinn drove through the gap." | | 10 | "Her shoes struck wet pavement" | | 11 | "A taxi braked hard enough" | | 12 | "Quinn took a narrower crossing" | | 13 | "She knew that scar from" | | 14 | "Tonight he had come out" | | 15 | "He had left without it." | | 16 | "Quinn held up her warrant" | | 17 | "Herrera’s face changed." | | 18 | "He knocked into a woman’s" | | 19 | "A motorbike cut across Quinn’s" |
| | ratio | 0.777 | |
| 71.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 139 | | matches | | 0 | "By the time the man" | | 1 | "By the time the fire" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 50 | | tagDensity | 0.16 | | leniency | 0.32 | | rawRatio | 0.125 | | effectiveRatio | 0.04 | |