| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.72% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 973 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 43.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 973 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "depths" | | 1 | "scanned" | | 2 | "silence" | | 3 | "weight" | | 4 | "gleaming" | | 5 | "lurched" | | 6 | "flicked" | | 7 | "could feel" | | 8 | "looming" | | 9 | "stomach" |
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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 | 1 | | narrationSentences | 68 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 68 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 979 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.81% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 926 | | uniqueNames | 12 | | maxNameDensity | 1.4 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 13 | | Soho | 3 | | Herrera | 2 | | Raven | 3 | | Nest | 3 | | Silas | 4 | | Veil | 2 | | London | 1 | | Market | 1 | | God | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Silas" |
| | places | | | globalScore | 0.798 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | 0 | "as if reading her thoughts" |
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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 | 979 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 73 | | matches | (empty) | |
| 82.70% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 39.16 | | std | 17.21 | | cv | 0.439 | | sampleLengths | | 0 | 60 | | 1 | 58 | | 2 | 20 | | 3 | 42 | | 4 | 81 | | 5 | 48 | | 6 | 41 | | 7 | 47 | | 8 | 44 | | 9 | 55 | | 10 | 53 | | 11 | 21 | | 12 | 1 | | 13 | 40 | | 14 | 23 | | 15 | 42 | | 16 | 17 | | 17 | 22 | | 18 | 53 | | 19 | 15 | | 20 | 36 | | 21 | 36 | | 22 | 32 | | 23 | 39 | | 24 | 53 |
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| 89.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 68 | | matches | | 0 | "were plastered" | | 1 | "was known" | | 2 | "being cocked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 148 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 73 | | ratio | 0.068 | | matches | | 0 | "The young man - Herrera, she remembered the name from her case files - glanced back at her over his shoulder." | | 1 | "Her heart lurched into her throat as she recognized the symbol - an arcane mark of-the-etheropaths, the bone-crafters." | | 2 | "They were dealers in the forbidden - unregistered magic, unexplained phenomena, items of unknown origin." | | 3 | "The owner of the voice stepped from the shadows - a man with cold eyes and a hard mouth." | | 4 | "But one thing was certain - she would be back." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 930 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.021505376344086023 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008602150537634409 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 13.41 | | std | 6.48 | | cv | 0.483 | | sampleLengths | | 0 | 26 | | 1 | 15 | | 2 | 6 | | 3 | 13 | | 4 | 21 | | 5 | 13 | | 6 | 24 | | 7 | 18 | | 8 | 2 | | 9 | 21 | | 10 | 21 | | 11 | 25 | | 12 | 14 | | 13 | 16 | | 14 | 26 | | 15 | 12 | | 16 | 17 | | 17 | 10 | | 18 | 9 | | 19 | 18 | | 20 | 6 | | 21 | 17 | | 22 | 13 | | 23 | 12 | | 24 | 14 | | 25 | 8 | | 26 | 20 | | 27 | 24 | | 28 | 20 | | 29 | 18 | | 30 | 17 | | 31 | 8 | | 32 | 18 | | 33 | 15 | | 34 | 12 | | 35 | 11 | | 36 | 10 | | 37 | 1 | | 38 | 19 | | 39 | 21 | | 40 | 17 | | 41 | 6 | | 42 | 8 | | 43 | 18 | | 44 | 16 | | 45 | 11 | | 46 | 6 | | 47 | 22 | | 48 | 4 | | 49 | 4 |
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| 65.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.410958904109589 | | totalSentences | 73 | | uniqueOpeners | 30 | |
| 49.02% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 68 | | matches | | 0 | "Slowly, she backed away, keeping" |
| | ratio | 0.015 | |
| 78.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 68 | | matches | | 0 | "Her salt-and-pepper hair, cropped short" | | 1 | "Her gaze was laser-focused on" | | 2 | "She caught a glimpse of" | | 3 | "she shouted, her voice nearly" | | 4 | "His eyes, warm brown and" | | 5 | "She paused, catching her breath," | | 6 | "It was the only exit" | | 7 | "She shoved her way through" | | 8 | "It took a moment for" | | 9 | "Her heart lurched into her" | | 10 | "They were dealers in the" | | 11 | "She took a step forward," | | 12 | "He was known as Silas," | | 13 | "he said, his voice raspy" | | 14 | "His eyes flicked to the" | | 15 | "She didn't need to." | | 16 | "She could feel the threat" | | 17 | "She could kill them all," | | 18 | "She had to live to" | | 19 | "His eyes bore into her," |
| | ratio | 0.353 | |
| 99.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 68 | | matches | | 0 | "The rain pelted Detective Harlow" | | 1 | "Her salt-and-pepper hair, cropped short" | | 2 | "Her gaze was laser-focused on" | | 3 | "The fugitive, a young man" | | 4 | "Quinn put on a burst" | | 5 | "She caught a glimpse of" | | 6 | "she shouted, her voice nearly" | | 7 | "The young man - Herrera," | | 8 | "His eyes, warm brown and" | | 9 | "Quinn reached the doorway,, a" | | 10 | "She paused, catching her breath," | | 11 | "The bar was dimly lit," | | 12 | "Eyes turned to the bedraggled" | | 13 | "Quinn scanned the room, her" | | 14 | "It was the only exit" | | 15 | "She shoved her way through" | | 16 | "Another door, this one tucked" | | 17 | "The room beyond was even" | | 18 | "It took a moment for" | | 19 | "Quinn froze, her gaze locked" |
| | ratio | 0.721 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "Silas said, as if reading her thoughts" | | 1 | "Silas said, his tone hard as flint" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.25 | | effectiveRatio | 0.222 | |