| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 66 | | tagDensity | 0.242 | | leniency | 0.485 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1878 | | totalAiIsmAdverbs | 2 | | 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) | |
| 81.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1878 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "measured" | | 2 | "tension" | | 3 | "flicked" | | 4 | "silence" | | 5 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 2 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 119 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 119 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 169 | | 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 | 1879 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 79 | | wordCount | 1489 | | uniqueNames | 10 | | maxNameDensity | 2.22 | | worstName | "Rory" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | London | 1 | | Rory | 33 | | Silas | 6 | | Cardiff | 3 | | Queen | 1 | | Street | 1 | | Eva | 31 | | Evan | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Silas" | | 4 | "Queen" | | 5 | "Eva" | | 6 | "Evan" |
| | places | | 0 | "London" | | 1 | "Cardiff" | | 2 | "Street" |
| | globalScore | 0.392 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 1 | | matches | | 0 | "breath that seemed to thin all the tension from her shoulders and put it straight into her face" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1879 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 169 | | matches | | 0 | "announcing that he" | | 1 | "let that sit" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 88 | | mean | 21.35 | | std | 24.13 | | cv | 1.13 | | sampleLengths | | 0 | 110 | | 1 | 61 | | 2 | 15 | | 3 | 33 | | 4 | 6 | | 5 | 22 | | 6 | 122 | | 7 | 4 | | 8 | 9 | | 9 | 43 | | 10 | 1 | | 11 | 19 | | 12 | 7 | | 13 | 1 | | 14 | 44 | | 15 | 28 | | 16 | 2 | | 17 | 3 | | 18 | 20 | | 19 | 93 | | 20 | 9 | | 21 | 15 | | 22 | 9 | | 23 | 43 | | 24 | 6 | | 25 | 15 | | 26 | 5 | | 27 | 98 | | 28 | 28 | | 29 | 26 | | 30 | 32 | | 31 | 5 | | 32 | 6 | | 33 | 20 | | 34 | 3 | | 35 | 28 | | 36 | 11 | | 37 | 35 | | 38 | 11 | | 39 | 16 | | 40 | 5 | | 41 | 20 | | 42 | 21 | | 43 | 11 | | 44 | 27 | | 45 | 7 | | 46 | 7 | | 47 | 13 | | 48 | 4 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 232 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 169 | | ratio | 0.012 | | matches | | 0 | "The maps, the smoke, the hum of the neon, Silas at the end of the counter with his glass in hand — all of it folded down to a single, impossible shape standing in the doorway." | | 1 | "Rory saw it then, all at once: the train platform in Cardiff, the small suitcase with one broken wheel, her phone buzzing in the pocket of her coat and her hand too busy shaking to pull it out; Eva’s name lighting the screen over and over while Evan’s voice still lived in the flat walls behind her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1494 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.03279785809906292 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002677376171352075 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 169 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 169 | | mean | 11.12 | | std | 9.28 | | cv | 0.835 | | sampleLengths | | 0 | 29 | | 1 | 21 | | 2 | 20 | | 3 | 5 | | 4 | 35 | | 5 | 21 | | 6 | 22 | | 7 | 18 | | 8 | 15 | | 9 | 17 | | 10 | 16 | | 11 | 3 | | 12 | 3 | | 13 | 22 | | 14 | 13 | | 15 | 35 | | 16 | 17 | | 17 | 2 | | 18 | 2 | | 19 | 32 | | 20 | 4 | | 21 | 4 | | 22 | 13 | | 23 | 4 | | 24 | 9 | | 25 | 7 | | 26 | 36 | | 27 | 1 | | 28 | 19 | | 29 | 7 | | 30 | 1 | | 31 | 21 | | 32 | 23 | | 33 | 28 | | 34 | 2 | | 35 | 3 | | 36 | 15 | | 37 | 5 | | 38 | 12 | | 39 | 2 | | 40 | 8 | | 41 | 7 | | 42 | 23 | | 43 | 6 | | 44 | 3 | | 45 | 3 | | 46 | 29 | | 47 | 9 | | 48 | 6 | | 49 | 9 |
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| 42.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.24260355029585798 | | totalSentences | 169 | | uniqueOpeners | 41 | |
| 31.45% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 106 | | matches | | | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 106 | | matches | | 0 | "His limp showed when he" | | 1 | "He wore his usual dark" | | 2 | "he muttered, eyeing the delivery" | | 3 | "She snorted, reached for the" | | 4 | "She paused just inside the" | | 5 | "Her coat looked expensive in" | | 6 | "Her face had thinned." | | 7 | "Her mouth had sharpened." | | 8 | "She carried herself as if" | | 9 | "He made for the other" | | 10 | "Her blunt laugh." | | 11 | "He filled them with a" | | 12 | "he said, then tipped his" | | 13 | "She had not answered then." | | 14 | "She had not answered later," | | 15 | "She tasted the old flat" | | 16 | "She heard herself speaking before" | | 17 | "Her thumb rubbed once over" | | 18 | "She looked down at her" |
| | ratio | 0.179 | |
| 2.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 97 | | totalSentences | 106 | | matches | | 0 | "The green neon over The" | | 1 | "Rory stood behind the bar" | | 2 | "The room smelled of old" | | 3 | "Maps stared from the walls." | | 4 | "Silas moved at the far" | | 5 | "His limp showed when he" | | 6 | "He wore his usual dark" | | 7 | "he muttered, eyeing the delivery" | | 8 | "Rory said, dropping the cloth" | | 9 | "Silas’s mouth twitched." | | 10 | "She snorted, reached for the" | | 11 | "A woman stepped in with" | | 12 | "She paused just inside the" | | 13 | "Her coat looked expensive in" | | 14 | "Hair cut to her jaw," | | 15 | "Her face had thinned." | | 16 | "Her mouth had sharpened." | | 17 | "She carried herself as if" | | 18 | "The woman's eyes locked on" | | 19 | "The maps, the smoke, the" |
| | ratio | 0.915 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 106 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 2 | | matches | | 0 | "She paused just inside the threshold, blinking through the low light, rain pearled on her shoulders, one hand on the door as if she had half a mind to turn arou…" | | 1 | "Rory saw it then, all at once: the train platform in Cardiff, the small suitcase with one broken wheel, her phone buzzing in the pocket of her coat and her hand…" |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva took, the bar, the maps, the old photographs" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "he muttered (mutter)" | | 1 | "Eva murmured (murmur)" |
| | dialogueSentences | 66 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.182 | | effectiveRatio | 0.061 | |