| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 147 | | tagDensity | 0.177 | | leniency | 0.354 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 98.33% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2989 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 88.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2989 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "silence" | | 1 | "familiar" | | 2 | "warmth" | | 3 | "weight" | | 4 | "potential" | | 5 | "footsteps" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 218 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 218 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 339 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2989 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 35 | | unquotedAttributions | 1 | | matches | | 0 | "For the first time since she had spoken her name, Aurora laughed without guarding it." |
| |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 144 | | wordCount | 1805 | | uniqueNames | 14 | | maxNameDensity | 3.38 | | worstName | "Eva" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Soho | 1 | | Aurora | 59 | | Raven | 1 | | Nest | 1 | | Silas | 13 | | Ellis | 1 | | Eva | 61 | | Cathays | 1 | | Eastern | 1 | | Europe | 1 | | Evan | 1 | | Wardour | 1 | | Street | 1 | | Yu-Fei | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Ellis" | | 4 | "Eva" | | 5 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "Eastern" | | 2 | "Europe" | | 3 | "Wardour" | | 4 | "Street" | | 5 | "Yu-Fei" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 127 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a bad marriage" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.335 | | wordCount | 2989 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 339 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 206 | | mean | 14.51 | | std | 16.35 | | cv | 1.127 | | sampleLengths | | 0 | 97 | | 1 | 18 | | 2 | 8 | | 3 | 16 | | 4 | 9 | | 5 | 34 | | 6 | 60 | | 7 | 6 | | 8 | 1 | | 9 | 14 | | 10 | 17 | | 11 | 82 | | 12 | 7 | | 13 | 15 | | 14 | 3 | | 15 | 9 | | 16 | 6 | | 17 | 12 | | 18 | 6 | | 19 | 7 | | 20 | 8 | | 21 | 2 | | 22 | 2 | | 23 | 5 | | 24 | 10 | | 25 | 27 | | 26 | 19 | | 27 | 5 | | 28 | 16 | | 29 | 44 | | 30 | 1 | | 31 | 4 | | 32 | 7 | | 33 | 15 | | 34 | 15 | | 35 | 11 | | 36 | 22 | | 37 | 30 | | 38 | 22 | | 39 | 4 | | 40 | 2 | | 41 | 5 | | 42 | 14 | | 43 | 2 | | 44 | 2 | | 45 | 5 | | 46 | 69 | | 47 | 5 | | 48 | 4 | | 49 | 20 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 218 | | matches | | 0 | "been pinned" | | 1 | "been frightened" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 301 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 339 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1808 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.025995575221238937 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0027654867256637168 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 339 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 339 | | mean | 8.82 | | std | 8.13 | | cv | 0.922 | | sampleLengths | | 0 | 8 | | 1 | 37 | | 2 | 21 | | 3 | 8 | | 4 | 23 | | 5 | 7 | | 6 | 11 | | 7 | 8 | | 8 | 16 | | 9 | 3 | | 10 | 6 | | 11 | 14 | | 12 | 20 | | 13 | 10 | | 14 | 9 | | 15 | 5 | | 16 | 8 | | 17 | 28 | | 18 | 6 | | 19 | 1 | | 20 | 14 | | 21 | 6 | | 22 | 11 | | 23 | 11 | | 24 | 5 | | 25 | 16 | | 26 | 10 | | 27 | 19 | | 28 | 21 | | 29 | 5 | | 30 | 2 | | 31 | 15 | | 32 | 3 | | 33 | 9 | | 34 | 6 | | 35 | 10 | | 36 | 2 | | 37 | 5 | | 38 | 1 | | 39 | 6 | | 40 | 1 | | 41 | 8 | | 42 | 2 | | 43 | 2 | | 44 | 5 | | 45 | 6 | | 46 | 4 | | 47 | 9 | | 48 | 5 | | 49 | 13 |
| |
| 44.99% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.24778761061946902 | | totalSentences | 339 | | uniqueOpeners | 84 | |
| 55.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 181 | | matches | | 0 | "Then the woman’s eyes met" | | 1 | "Then the moment shut again." | | 2 | "Somewhere at the back of" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 181 | | matches | | 0 | "His silver signet ring flashed" | | 1 | "She had her gloves half" | | 2 | "He did not ask questions." | | 3 | "She took the glass, felt" | | 4 | "His limp marked itself in" | | 5 | "They used to call it" | | 6 | "She rubbed the condensation ring" | | 7 | "She had not thought about" | | 8 | "They glanced round, took the" | | 9 | "She climbed railings in the" | | 10 | "She snuck them into clubs" | | 11 | "She once told a lecturer" | | 12 | "It fit this one." | | 13 | "She set the glass down" | | 14 | "Her knuckles were dry from" | | 15 | "She picked at a loose" | | 16 | "He set them between them" | | 17 | "She had wrapped a hundred" | | 18 | "I should have seen." | | 19 | "I should have listened." |
| | ratio | 0.149 | |
| 9.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 163 | | totalSentences | 181 | | matches | | 0 | "Rain hung over Soho like" | | 1 | "The green neon outside smeared" | | 2 | "Someone had fed the fire" | | 3 | "The room smelt of wet" | | 4 | "Silas looked up from polishing" | | 5 | "His silver signet ring flashed" | | 6 | "Silas’s mouth bent." | | 7 | "Aurora dropped her helmet on" | | 8 | "She had her gloves half" | | 9 | "Camel coat folded over the" | | 10 | "Cream blouse without a wrinkle." | | 11 | "A narrow glass of soda" | | 12 | "The old name struck with" | | 13 | "Aurora’s hand stopped on the" | | 14 | "The crescent scar on her" | | 15 | "Eva Ellis rose so fast" | | 16 | "Years had pared her down." | | 17 | "The wild nest of dark" | | 18 | "The old Eva had filled" | | 19 | "This woman stood inside her" |
| | ratio | 0.901 | |
| 55.25% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 181 | | matches | | 0 | "By the time Aurora pushed" | | 1 | "Before men like Evan ever" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 1 | | matches | | 0 | "None of it embarrassed her until it met the cream blouse and the clean cufflinks and the smooth city polish that clung to Eva like a second skin." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 2 | | fancyTags | | 0 | "Silas gave (give)" | | 1 | "she admitted (admit)" |
| | dialogueSentences | 147 | | tagDensity | 0.116 | | leniency | 0.231 | | rawRatio | 0.118 | | effectiveRatio | 0.027 | |