| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1334 | | 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) | |
| 92.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1334 | | 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 | 58 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 58 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1339 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 10 | | wordCount | 730 | | uniqueNames | 9 | | maxNameDensity | 0.27 | | worstName | "Eva" | | maxWindowNameDensity | 0.5 | | worstWindowName | "Eva" | | discoveredNames | | Moreau | 1 | | Water | 1 | | Dylan | 1 | | Thomas | 1 | | Eva | 2 | | Rain | 1 | | Brick | 1 | | Lane | 1 | | March | 1 |
| | persons | | 0 | "Moreau" | | 1 | "Water" | | 2 | "Dylan" | | 3 | "Thomas" | | 4 | "Eva" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | 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 | 1339 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 23.09 | | std | 21.32 | | cv | 0.923 | | sampleLengths | | 0 | 21 | | 1 | 35 | | 2 | 3 | | 3 | 51 | | 4 | 21 | | 5 | 68 | | 6 | 6 | | 7 | 17 | | 8 | 16 | | 9 | 5 | | 10 | 1 | | 11 | 44 | | 12 | 5 | | 13 | 43 | | 14 | 16 | | 15 | 15 | | 16 | 20 | | 17 | 6 | | 18 | 51 | | 19 | 3 | | 20 | 13 | | 21 | 87 | | 22 | 36 | | 23 | 4 | | 24 | 6 | | 25 | 4 | | 26 | 1 | | 27 | 58 | | 28 | 19 | | 29 | 57 | | 30 | 2 | | 31 | 17 | | 32 | 5 | | 33 | 4 | | 34 | 20 | | 35 | 53 | | 36 | 31 | | 37 | 3 | | 38 | 6 | | 39 | 5 | | 40 | 8 | | 41 | 35 | | 42 | 33 | | 43 | 2 | | 44 | 49 | | 45 | 27 | | 46 | 21 | | 47 | 19 | | 48 | 33 | | 49 | 16 |
| |
| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 58 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 122 | | matches | (empty) | |
| 22.56% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 2 | | flaggedSentences | 4 | | totalSentences | 95 | | ratio | 0.042 | | matches | | 0 | "A laugh escaped before she could catch it — one syllable, and she strangled the rest." | | 1 | "\"I crossed London for an excuse. It fell behind the hall table in March; I found it the week after you—\" He stopped, chose again." | | 2 | "He didn't cage her in; he occupied the air she needed, warm through the sleeve of her cardigan." | | 3 | "Two mugs came down from the shelf — hers, and the blue one with the chipped rim he'd claimed on his first visit and she'd never had the heart to rehouse." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 728 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.012362637362637362 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0013736263736263737 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 14.09 | | std | 11.55 | | cv | 0.82 | | sampleLengths | | 0 | 21 | | 1 | 21 | | 2 | 5 | | 3 | 9 | | 4 | 3 | | 5 | 25 | | 6 | 11 | | 7 | 15 | | 8 | 7 | | 9 | 14 | | 10 | 11 | | 11 | 27 | | 12 | 2 | | 13 | 28 | | 14 | 6 | | 15 | 2 | | 16 | 15 | | 17 | 16 | | 18 | 5 | | 19 | 1 | | 20 | 28 | | 21 | 16 | | 22 | 5 | | 23 | 43 | | 24 | 16 | | 25 | 11 | | 26 | 4 | | 27 | 6 | | 28 | 14 | | 29 | 6 | | 30 | 25 | | 31 | 26 | | 32 | 3 | | 33 | 10 | | 34 | 3 | | 35 | 26 | | 36 | 61 | | 37 | 21 | | 38 | 15 | | 39 | 4 | | 40 | 6 | | 41 | 4 | | 42 | 1 | | 43 | 54 | | 44 | 4 | | 45 | 11 | | 46 | 8 | | 47 | 23 | | 48 | 34 | | 49 | 2 |
| |
| 77.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4842105263157895 | | totalSentences | 95 | | uniqueOpeners | 46 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 55 | | matches | | 0 | "Then she folded her arms" |
| | ratio | 0.018 | |
| 30.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 55 | | matches | | 0 | "Her hand went to the" | | 1 | "She could shut him out" | | 2 | "She threw all three deadbolts" | | 3 | "He turned the rain-dark collar" | | 4 | "She took the paperback from" | | 5 | "He stopped, chose again" | | 6 | "He didn't check his watch" | | 7 | "Her spine went rigid." | | 8 | "He set one hand flat" | | 9 | "He would have had the" | | 10 | "Her voice came out with" | | 11 | "His arm rested along the" | | 12 | "He didn't cage her in;" | | 13 | "He watched her hands rather" | | 14 | "His hand came up." | | 15 | "Her left wrist turned to" | | 16 | "His knuckle stopped a breath" | | 17 | "He answered by leaning in," | | 18 | "He smelled of rain and" | | 19 | "She put her palm flat" |
| | ratio | 0.473 | |
| 23.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 55 | | matches | | 0 | "Rory opened the door the" | | 1 | "The other stayed black as" | | 2 | "Lucien Moreau lifted a paperback" | | 3 | "Water ticked off the hem" | | 4 | "A Dylan Thomas, spine mended" | | 5 | "Her hand went to the" | | 6 | "The door swung wide, and" | | 7 | "She could shut him out" | | 8 | "Ptolemy deserted the windowsill and" | | 9 | "She threw all three deadbolts" | | 10 | "The flat held four metres" | | 11 | "Lucien leaned his cane against" | | 12 | "A laugh escaped before she" | | 13 | "He turned the rain-dark collar" | | 14 | "She took the paperback from" | | 15 | "He stopped, chose again" | | 16 | "He didn't check his watch" | | 17 | "The book trembled until she" | | 18 | "Rain slid from his hairline" | | 19 | "Her spine went rigid." |
| | ratio | 0.873 | |
| 90.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 55 | | matches | | 0 | "Even at that angle she" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |