| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 90 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1746 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 79.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1746 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "measured" | | 1 | "charged" | | 2 | "weight" | | 3 | "silence" |
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| 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 | 135 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 135 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 220 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1746 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 76 | | wordCount | 1145 | | uniqueNames | 14 | | maxNameDensity | 2.62 | | worstName | "Eva" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 29 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Blackwood | 1 | | Cardiff | 1 | | Eva | 30 | | Controlled | 1 | | Tesco | 1 | | Daniel | 1 | | London | 1 | | Silas | 6 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Blackwood" | | 3 | "Eva" | | 4 | "Daniel" | | 5 | "Silas" |
| | places | | 0 | "Raven" | | 1 | "Cardiff" | | 2 | "Tesco" | | 3 | "London" |
| | globalScore | 0.19 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | 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 | 1746 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 220 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 143 | | mean | 12.21 | | std | 14.03 | | cv | 1.149 | | sampleLengths | | 0 | 66 | | 1 | 50 | | 2 | 29 | | 3 | 6 | | 4 | 55 | | 5 | 1 | | 6 | 6 | | 7 | 1 | | 8 | 16 | | 9 | 13 | | 10 | 10 | | 11 | 6 | | 12 | 19 | | 13 | 60 | | 14 | 26 | | 15 | 15 | | 16 | 5 | | 17 | 52 | | 18 | 9 | | 19 | 18 | | 20 | 3 | | 21 | 8 | | 22 | 1 | | 23 | 3 | | 24 | 11 | | 25 | 3 | | 26 | 5 | | 27 | 9 | | 28 | 4 | | 29 | 1 | | 30 | 16 | | 31 | 25 | | 32 | 6 | | 33 | 11 | | 34 | 6 | | 35 | 1 | | 36 | 9 | | 37 | 2 | | 38 | 3 | | 39 | 8 | | 40 | 5 | | 41 | 21 | | 42 | 5 | | 43 | 3 | | 44 | 10 | | 45 | 2 | | 46 | 6 | | 47 | 5 | | 48 | 14 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 135 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 186 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 220 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1148 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.008710801393728223 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 220 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 220 | | mean | 7.94 | | std | 6.61 | | cv | 0.832 | | sampleLengths | | 0 | 26 | | 1 | 14 | | 2 | 12 | | 3 | 14 | | 4 | 12 | | 5 | 27 | | 6 | 11 | | 7 | 13 | | 8 | 2 | | 9 | 7 | | 10 | 7 | | 11 | 6 | | 12 | 3 | | 13 | 37 | | 14 | 3 | | 15 | 3 | | 16 | 9 | | 17 | 1 | | 18 | 6 | | 19 | 1 | | 20 | 6 | | 21 | 10 | | 22 | 13 | | 23 | 10 | | 24 | 6 | | 25 | 16 | | 26 | 3 | | 27 | 12 | | 28 | 12 | | 29 | 28 | | 30 | 8 | | 31 | 15 | | 32 | 1 | | 33 | 1 | | 34 | 9 | | 35 | 13 | | 36 | 2 | | 37 | 5 | | 38 | 15 | | 39 | 5 | | 40 | 24 | | 41 | 8 | | 42 | 9 | | 43 | 7 | | 44 | 11 | | 45 | 3 | | 46 | 8 | | 47 | 1 | | 48 | 3 | | 49 | 11 |
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| 46.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.21818181818181817 | | totalSentences | 220 | | uniqueOpeners | 48 | |
| 26.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 128 | | matches | | 0 | "Instead she took a small" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 128 | | matches | | 0 | "She wanted the back stairs," | | 1 | "His grey-streaked auburn hair caught" | | 2 | "He lifted his chin toward" | | 3 | "Her face kept the sharp" | | 4 | "His hazel eyes moved from" | | 5 | "Its edge held the faint" | | 6 | "She had delivered food through" | | 7 | "She had never brought the" | | 8 | "He left them and let" | | 9 | "She remained standing until Aurora" | | 10 | "Her fingers touched the edge" | | 11 | "She left it bare." | | 12 | "It had rebuilt her." | | 13 | "Her hands flattened on the" | | 14 | "Her movements stayed measured." | | 15 | "She drew out a phone," | | 16 | "She remembered the messages." | | 17 | "It had travelled with Eva" | | 18 | "He stood in the gap" | | 19 | "His left leg took less" |
| | ratio | 0.211 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 118 | | totalSentences | 128 | | matches | | 0 | "The green neon sign above" | | 1 | "Rain ran from her straight" | | 2 | "The Golden Empress bag knocked" | | 3 | "She wanted the back stairs," | | 4 | "Silas Blackwood stood behind the" | | 5 | "His grey-streaked auburn hair caught" | | 6 | "He lifted his chin toward" | | 7 | "A woman sat there with" | | 8 | "Hands folded around a tumbler" | | 9 | "A leather briefcase upright beside" | | 10 | "Aurora’s grip tightened on the" | | 11 | "The woman turned." | | 12 | "Her face kept the sharp" | | 13 | "A thin gold band sat" | | 14 | "The glass lowered in Eva’s" | | 15 | "Silas set the polished glass" | | 16 | "His hazel eyes moved from" | | 17 | "Eva’s mouth formed a smile" | | 18 | "Silas wiped a circle on" | | 19 | "Aurora looked at the bookshelf" |
| | ratio | 0.922 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 128 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 1 | | matches | | 0 | "Eva’s mouth formed a smile that stopped at her lips." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |