| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1531 | | 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) | |
| 60.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1531 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "measured" | | 1 | "scanned" | | 2 | "chaotic" | | 3 | "standard" | | 4 | "perfect" | | 5 | "weight" | | 6 | "traced" | | 7 | "etched" | | 8 | "magnetic" | | 9 | "warmth" | | 10 | "trembled" |
| |
| 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 | 264 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 264 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 264 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 20 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1531 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1531 | | uniqueNames | 6 | | maxNameDensity | 0.98 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 15 | | Hayes | 9 | | Camden | 1 | | Shoreditch | 1 | | Old | 3 | | You | 6 |
| | persons | | | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 126 | | 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 | 1531 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 264 | | matches | (empty) | |
| 68.01% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 66.57 | | std | 25.83 | | cv | 0.388 | | sampleLengths | | 0 | 58 | | 1 | 56 | | 2 | 63 | | 3 | 53 | | 4 | 81 | | 5 | 58 | | 6 | 58 | | 7 | 75 | | 8 | 42 | | 9 | 14 | | 10 | 70 | | 11 | 89 | | 12 | 73 | | 13 | 88 | | 14 | 93 | | 15 | 84 | | 16 | 83 | | 17 | 69 | | 18 | 38 | | 19 | 146 | | 20 | 66 | | 21 | 37 | | 22 | 37 |
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| 98.62% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 264 | | matches | | 0 | "was pulled" | | 1 | "are held" | | 2 | "is swept" | | 3 | "was lowered" | | 4 | "was placed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 284 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 264 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1535 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, pressing against her" |
| | adverbCount | 28 | | adverbRatio | 0.018241042345276872 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005211726384364821 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 264 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 264 | | mean | 5.8 | | std | 3.26 | | cv | 0.563 | | sampleLengths | | 0 | 9 | | 1 | 9 | | 2 | 13 | | 3 | 19 | | 4 | 1 | | 5 | 7 | | 6 | 14 | | 7 | 11 | | 8 | 9 | | 9 | 8 | | 10 | 14 | | 11 | 9 | | 12 | 7 | | 13 | 4 | | 14 | 10 | | 15 | 11 | | 16 | 12 | | 17 | 4 | | 18 | 6 | | 19 | 3 | | 20 | 9 | | 21 | 14 | | 22 | 4 | | 23 | 6 | | 24 | 4 | | 25 | 11 | | 26 | 2 | | 27 | 4 | | 28 | 4 | | 29 | 6 | | 30 | 7 | | 31 | 5 | | 32 | 2 | | 33 | 7 | | 34 | 2 | | 35 | 6 | | 36 | 15 | | 37 | 8 | | 38 | 15 | | 39 | 5 | | 40 | 7 | | 41 | 7 | | 42 | 6 | | 43 | 6 | | 44 | 8 | | 45 | 3 | | 46 | 9 | | 47 | 4 | | 48 | 3 | | 49 | 4 |
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| 52.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3560606060606061 | | totalSentences | 264 | | uniqueOpeners | 94 | |
| 14.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 229 | | matches | | 0 | "Just the powder dragging across" |
| | ratio | 0.004 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 66 | | totalSentences | 229 | | matches | | 0 | "She stepped past the yellow" | | 1 | "He knelt beside a body" | | 2 | "His penlight cut a narrow" | | 3 | "He did not look up" | | 4 | "He measured the distance between" | | 5 | "She scanned the platform." | | 6 | "She circled the body." | | 7 | "His shoes had scuffed white" | | 8 | "Her knees pressed into the" | | 9 | "She reached out and hovered" | | 10 | "You said he fell three" | | 11 | "He unclipped his radio and" | | 12 | "I am reading impact trauma" | | 13 | "I found three loose luggage" | | 14 | "He held tight." | | 15 | "She walked to the base" | | 16 | "She traced the vertical lines" | | 17 | "She bent and picked up" | | 18 | "She rotated her wrist." | | 19 | "His shadow stretched across the" |
| | ratio | 0.288 | |
| 69.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 179 | | totalSentences | 229 | | matches | | 0 | "The iron gate groaned as" | | 1 | "She stepped past the yellow" | | 2 | "The air grew heavy, pressing" | | 3 | "PC Hayes waited at the" | | 4 | "He knelt beside a body" | | 5 | "His penlight cut a narrow" | | 6 | "He did not look up" | | 7 | "He measured the distance between" | | 8 | "Quinn holstered her sidearm and" | | 9 | "The worn strap creaked against" | | 10 | "She scanned the platform." | | 11 | "Graffiti carved over brick arches" | | 12 | "The work lamp hummed." | | 13 | "Dust motes swirled in the" | | 14 | "Robbery gone sideways." | | 15 | "Hayes stood and wiped his" | | 16 | "Head hit the pier." | | 17 | "Blood pooled before he stopped" | | 18 | "Quinn walked past him." | | 19 | "She circled the body." |
| | ratio | 0.782 | |
| 21.83% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 229 | | matches | | 0 | "If he pulled a weapon," |
| | ratio | 0.004 | |
| 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 | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |