| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 70 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2170 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 93.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2170 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "footsteps" | | 1 | "velvet" | | 2 | "measured" |
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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 | 200 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 200 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 260 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2170 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 73 | | wordCount | 1795 | | uniqueNames | 9 | | maxNameDensity | 1.78 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 3 | | Harlow | 1 | | Quinn | 32 | | Herrera | 29 | | Saint | 1 | | Christopher | 1 | | Seven | 1 | | Rain | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Rain" |
| | places | | | globalScore | 0.609 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 145 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a coin until he brought it in" |
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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 | 2170 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 260 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 127 | | mean | 17.09 | | std | 15.36 | | cv | 0.899 | | sampleLengths | | 0 | 54 | | 1 | 2 | | 2 | 35 | | 3 | 43 | | 4 | 41 | | 5 | 9 | | 6 | 11 | | 7 | 3 | | 8 | 24 | | 9 | 20 | | 10 | 49 | | 11 | 9 | | 12 | 14 | | 13 | 36 | | 14 | 1 | | 15 | 8 | | 16 | 4 | | 17 | 8 | | 18 | 10 | | 19 | 43 | | 20 | 38 | | 21 | 15 | | 22 | 8 | | 23 | 13 | | 24 | 4 | | 25 | 15 | | 26 | 3 | | 27 | 3 | | 28 | 5 | | 29 | 5 | | 30 | 24 | | 31 | 4 | | 32 | 3 | | 33 | 5 | | 34 | 3 | | 35 | 39 | | 36 | 11 | | 37 | 3 | | 38 | 2 | | 39 | 39 | | 40 | 18 | | 41 | 3 | | 42 | 4 | | 43 | 19 | | 44 | 47 | | 45 | 8 | | 46 | 20 | | 47 | 18 | | 48 | 4 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 200 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 299 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 260 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1798 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.016129032258064516 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0016685205784204673 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 260 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 260 | | mean | 8.35 | | std | 4.7 | | cv | 0.563 | | sampleLengths | | 0 | 22 | | 1 | 12 | | 2 | 20 | | 3 | 2 | | 4 | 3 | | 5 | 13 | | 6 | 5 | | 7 | 14 | | 8 | 5 | | 9 | 22 | | 10 | 5 | | 11 | 11 | | 12 | 4 | | 13 | 19 | | 14 | 18 | | 15 | 9 | | 16 | 11 | | 17 | 3 | | 18 | 12 | | 19 | 12 | | 20 | 20 | | 21 | 7 | | 22 | 20 | | 23 | 8 | | 24 | 8 | | 25 | 6 | | 26 | 9 | | 27 | 3 | | 28 | 5 | | 29 | 6 | | 30 | 4 | | 31 | 6 | | 32 | 12 | | 33 | 14 | | 34 | 1 | | 35 | 8 | | 36 | 4 | | 37 | 8 | | 38 | 10 | | 39 | 4 | | 40 | 6 | | 41 | 23 | | 42 | 10 | | 43 | 12 | | 44 | 12 | | 45 | 10 | | 46 | 4 | | 47 | 2 | | 48 | 13 | | 49 | 8 |
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| 46.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.28846153846153844 | | totalSentences | 260 | | uniqueOpeners | 75 | |
| 17.18% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 194 | | matches | | 0 | "Then a woman stepped between" |
| | ratio | 0.005 | |
| 94.23% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 194 | | matches | | 0 | "He looked back." | | 1 | "He put his shoulder into" | | 2 | "She cleared the bonnet and" | | 3 | "Her watch read 11:47." | | 4 | "She had seen him inside" | | 5 | "she had asked" | | 6 | "She turned after them." | | 7 | "He gripped it once, then" | | 8 | "He glanced back from beneath" | | 9 | "He swung the gate shut" | | 10 | "She went after him, scraping" | | 11 | "His hands were empty." | | 12 | "He raised them." | | 13 | "His eyes shifted to the" | | 14 | "He pulled a small object" | | 15 | "It looked like a coin" | | 16 | "Its surface bore flaking paint" | | 17 | "he told her" | | 18 | "He pressed the bone disc" | | 19 | "She reached for her cuffs." |
| | ratio | 0.314 | |
| 27.01% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 168 | | totalSentences | 194 | | matches | | 0 | "The man in the grey" | | 1 | "Detective Harlow Quinn came through" | | 2 | "He looked back." | | 3 | "Rain ran off his dark" | | 4 | "Tomás Herrera had heard her." | | 5 | "He put his shoulder into" | | 6 | "A bus swung between them." | | 7 | "Quinn slapped its flank as" | | 8 | "A taxi sounded its horn." | | 9 | "She cleared the bonnet and" | | 10 | "Her watch read 11:47." | | 11 | "She had seen him inside" | | 12 | "she had asked" | | 13 | "Herrera had looked at the" | | 14 | "Quinn keyed her radio as" | | 15 | "Water poured from a broken" | | 16 | "The reply dissolved beneath a" | | 17 | "Herrera vaulted a line of" | | 18 | "Quinn took the gap between" | | 19 | "The heel of her shoe" |
| | ratio | 0.866 | |
| 77.32% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 194 | | matches | | 0 | "Now he ran." | | 1 | "By the time she lifted" | | 2 | "Before she could reach him," |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 77 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 70 | | tagDensity | 0.086 | | leniency | 0.171 | | rawRatio | 0 | | effectiveRatio | 0 | |