| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 935 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 46.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 935 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "echoed" | | 1 | "fascinating" | | 2 | "traced" | | 3 | "silk" | | 4 | "stomach" | | 5 | "glint" | | 6 | "trembled" | | 7 | "dance" | | 8 | "scanning" | | 9 | "flickered" |
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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 | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 98 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 149 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 935 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 591 | | uniqueNames | 13 | | maxNameDensity | 0.68 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 4 | | Dawes | 1 | | Evening | 1 | | Wednesday | 1 | | Have | 1 | | Convenient | 1 | | Eva | 4 | | Three | 1 | | Morris | 1 | | Air | 1 | | Shade | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Wednesday" | | 2 | "Have" | | 3 | "Eva" | | 4 | "Morris" | | 5 | "Air" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 935 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 20.78 | | std | 15.33 | | cv | 0.738 | | sampleLengths | | 0 | 7 | | 1 | 50 | | 2 | 16 | | 3 | 56 | | 4 | 12 | | 5 | 6 | | 6 | 10 | | 7 | 45 | | 8 | 11 | | 9 | 2 | | 10 | 11 | | 11 | 4 | | 12 | 43 | | 13 | 12 | | 14 | 37 | | 15 | 2 | | 16 | 27 | | 17 | 44 | | 18 | 2 | | 19 | 35 | | 20 | 12 | | 21 | 45 | | 22 | 34 | | 23 | 44 | | 24 | 36 | | 25 | 19 | | 26 | 3 | | 27 | 33 | | 28 | 37 | | 29 | 28 | | 30 | 6 | | 31 | 12 | | 32 | 18 | | 33 | 16 | | 34 | 4 | | 35 | 30 | | 36 | 5 | | 37 | 26 | | 38 | 24 | | 39 | 13 | | 40 | 6 | | 41 | 19 | | 42 | 4 | | 43 | 22 | | 44 | 7 |
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| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 98 | | matches | | 0 | "been found" | | 1 | "collected" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 104 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 149 | | ratio | 0 | | matches | (empty) | |
| 89.74% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 58 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.05172413793103448 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.017241379310344827 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 149 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 149 | | mean | 6.28 | | std | 4.21 | | cv | 0.671 | | sampleLengths | | 0 | 7 | | 1 | 14 | | 2 | 13 | | 3 | 2 | | 4 | 3 | | 5 | 18 | | 6 | 11 | | 7 | 2 | | 8 | 1 | | 9 | 1 | | 10 | 1 | | 11 | 13 | | 12 | 7 | | 13 | 15 | | 14 | 18 | | 15 | 3 | | 16 | 4 | | 17 | 8 | | 18 | 6 | | 19 | 1 | | 20 | 6 | | 21 | 3 | | 22 | 5 | | 23 | 14 | | 24 | 3 | | 25 | 11 | | 26 | 9 | | 27 | 3 | | 28 | 2 | | 29 | 1 | | 30 | 4 | | 31 | 4 | | 32 | 2 | | 33 | 5 | | 34 | 4 | | 35 | 2 | | 36 | 1 | | 37 | 3 | | 38 | 9 | | 39 | 15 | | 40 | 6 | | 41 | 3 | | 42 | 6 | | 43 | 4 | | 44 | 8 | | 45 | 4 | | 46 | 11 | | 47 | 3 | | 48 | 14 | | 49 | 1 |
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| 83.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5167785234899329 | | totalSentences | 149 | | uniqueOpeners | 77 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 76 | | matches | | 0 | "Just a young woman in" | | 1 | "Instead, her gaze traced the" | | 2 | "Somewhere in that darkness, something" |
| | ratio | 0.039 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 76 | | matches | | 0 | "She descended the iron steps" | | 1 | "She didn't touch." | | 2 | "They feed on life force" | | 3 | "She was prepared." | | 4 | "She crouched again, angling the" | | 5 | "They're not common." | | 6 | "It should be pointing to" | | 7 | "It's pointing to something" | | 8 | "She looked up, the beam" | | 9 | "She knelt, carefully using her" | | 10 | "They don't stay with the" | | 11 | "She stood, scanning the darkness." |
| | ratio | 0.158 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 76 | | matches | | 0 | "Bodies don't just appear in" | | 1 | "The torch beam carved through" | | 2 | "Harlow Quinn crouched, her knees" | | 3 | "The dust layers told a" | | 4 | "Drag marks, recent, leading from" | | 5 | "Quinn leaned closer." | | 6 | "The skin had a waxy" | | 7 | "She descended the iron steps" | | 8 | "Victorian brickwork with modern mortar" | | 9 | "Someone's been doing" | | 10 | "Eva knelt beside the body," | | 11 | "She didn't touch." | | 12 | "They feed on life force" | | 13 | "Leaves almost no trace" | | 14 | "The coroner ruled it a" | | 15 | "Someone who's been in this" | | 16 | "She was prepared." | | 17 | "Eva leaned in, her expression" | | 18 | "The victim's clothing was expensive." | | 19 | "A silk blouse, tailored trousers," |
| | ratio | 0.605 | |
| 65.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 76 | | matches | | 0 | "If someone wanted to send" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 1 | | matches | | 0 | "Harlow Quinn crouched, her knees protesting after eighteen years of doing exactly this." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |