| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.203 | | leniency | 0.407 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1797 | | 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) | |
| 88.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1797 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "electric" | | 1 | "scanned" | | 2 | "magnetic" | | 3 | "trembled" |
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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 | 161 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 161 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 208 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1797 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.29% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1363 | | uniqueNames | 11 | | maxNameDensity | 2.05 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Hale" | | discoveredNames | | Harlow | 1 | | Quinn | 28 | | Camden | 1 | | High | 1 | | Street | 1 | | Veil | 1 | | Market | 1 | | Sergeant | 1 | | Hale | 17 | | Kowalski | 1 | | Eva | 10 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Sergeant" | | 4 | "Hale" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.473 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | glossingSentenceCount | 1 | | matches | | 0 | "as if waiting for something to be placed into it" |
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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 | 1797 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 208 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 78 | | mean | 23.04 | | std | 19.08 | | cv | 0.828 | | sampleLengths | | 0 | 55 | | 1 | 31 | | 2 | 42 | | 3 | 9 | | 4 | 79 | | 5 | 24 | | 6 | 31 | | 7 | 52 | | 8 | 1 | | 9 | 14 | | 10 | 50 | | 11 | 3 | | 12 | 9 | | 13 | 57 | | 14 | 72 | | 15 | 2 | | 16 | 3 | | 17 | 21 | | 18 | 22 | | 19 | 3 | | 20 | 6 | | 21 | 19 | | 22 | 46 | | 23 | 5 | | 24 | 12 | | 25 | 19 | | 26 | 45 | | 27 | 16 | | 28 | 8 | | 29 | 5 | | 30 | 26 | | 31 | 54 | | 32 | 4 | | 33 | 9 | | 34 | 16 | | 35 | 5 | | 36 | 21 | | 37 | 10 | | 38 | 18 | | 39 | 17 | | 40 | 7 | | 41 | 4 | | 42 | 50 | | 43 | 16 | | 44 | 20 | | 45 | 10 | | 46 | 27 | | 47 | 12 | | 48 | 26 | | 49 | 62 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 161 | | matches | | 0 | "was drawn" | | 1 | "been dragged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 226 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 208 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1368 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.00804093567251462 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0007309941520467836 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 208 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 208 | | mean | 8.64 | | std | 5.35 | | cv | 0.62 | | sampleLengths | | 0 | 14 | | 1 | 14 | | 2 | 10 | | 3 | 17 | | 4 | 10 | | 5 | 1 | | 6 | 20 | | 7 | 9 | | 8 | 3 | | 9 | 8 | | 10 | 22 | | 11 | 9 | | 12 | 5 | | 13 | 6 | | 14 | 13 | | 15 | 6 | | 16 | 16 | | 17 | 10 | | 18 | 6 | | 19 | 3 | | 20 | 14 | | 21 | 10 | | 22 | 14 | | 23 | 13 | | 24 | 18 | | 25 | 16 | | 26 | 10 | | 27 | 8 | | 28 | 10 | | 29 | 8 | | 30 | 1 | | 31 | 14 | | 32 | 2 | | 33 | 17 | | 34 | 12 | | 35 | 3 | | 36 | 16 | | 37 | 3 | | 38 | 4 | | 39 | 5 | | 40 | 8 | | 41 | 14 | | 42 | 17 | | 43 | 2 | | 44 | 3 | | 45 | 8 | | 46 | 5 | | 47 | 12 | | 48 | 19 | | 49 | 17 |
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| 52.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.34615384615384615 | | totalSentences | 208 | | uniqueOpeners | 72 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 146 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 146 | | matches | | 0 | "She checked the worn leather" | | 1 | "Her jaw tightened." | | 2 | "He lifted the tape for" | | 3 | "His arms were spread wide." | | 4 | "He had a notebook in" | | 5 | "His lips had the blue-grey" | | 6 | "It stopped short of the" | | 7 | "She leaned closer." | | 8 | "She had tucked a strand" | | 9 | "She watched the body with" | | 10 | "Her shoes crunched on broken" | | 11 | "He dropped it into the" | | 12 | "She drew out a small" | | 13 | "Her fingers stayed a beat" | | 14 | "It did not waver." | | 15 | "She had seen tool marks" | | 16 | "She walked the length of" | | 17 | "It ended at the drainage" | | 18 | "She followed it." | | 19 | "His mouth thinned." |
| | ratio | 0.192 | |
| 11.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 131 | | totalSentences | 146 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "The stairs down from Camden" | | 2 | "Bass from the street above" | | 3 | "She checked the worn leather" | | 4 | "Uniforms had held the scene" | | 5 | "Lamp light caught the salt-and-pepper" | | 6 | "Her jaw tightened." | | 7 | "A uniformed constable stood beside" | | 8 | "He lifted the tape for" | | 9 | "There, under a collapsed canvas" | | 10 | "His arms were spread wide." | | 11 | "A dark coat pooled around" | | 12 | "The other rested palm up," | | 13 | "A line of black salt" | | 14 | "Glass vials rolled in the" | | 15 | "Detective Sergeant Hale waited at" | | 16 | "He had a notebook in" | | 17 | "Hale nodded at the stall" | | 18 | "Quinn moved around the body" | | 19 | "The man's hair was dark" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 146 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva pointed, not touching the white grains" | | 1 | "Quinn held, not too close" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.051 | | leniency | 0.102 | | rawRatio | 0 | | effectiveRatio | 0 | |