| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.265 | | leniency | 0.529 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1198 | | totalAiIsmAdverbs | 3 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "very" | | 1 | "deliberately" | | 2 | "slowly" |
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| 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) | |
| 91.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1198 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1197 | | ratio | 0.003 | | matches | | 0 | "Thirteen Lesser Instruments" |
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| 97.22% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 1 | | matches | | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 800 | | uniqueNames | 7 | | maxNameDensity | 1.38 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 11 | | Camden | 1 | | Town | 1 | | July | 1 | | Eva | 8 | | Kowalski | 1 | | Gregson | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Eva" | | 2 | "Kowalski" | | 3 | "Gregson" |
| | places | | | globalScore | 0.813 | | windowScore | 0.667 | |
| 33.72% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 2 | | matches | | 0 | "not quite" | | 1 | "looked like a man who had lain down in a" |
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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 | 1197 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 26.02 | | std | 23.62 | | cv | 0.908 | | sampleLengths | | 0 | 7 | | 1 | 70 | | 2 | 18 | | 3 | 29 | | 4 | 4 | | 5 | 1 | | 6 | 4 | | 7 | 26 | | 8 | 9 | | 9 | 73 | | 10 | 12 | | 11 | 91 | | 12 | 21 | | 13 | 44 | | 14 | 41 | | 15 | 14 | | 16 | 45 | | 17 | 17 | | 18 | 5 | | 19 | 2 | | 20 | 52 | | 21 | 42 | | 22 | 75 | | 23 | 5 | | 24 | 29 | | 25 | 7 | | 26 | 3 | | 27 | 38 | | 28 | 60 | | 29 | 16 | | 30 | 4 | | 31 | 35 | | 32 | 9 | | 33 | 42 | | 34 | 7 | | 35 | 75 | | 36 | 30 | | 37 | 22 | | 38 | 11 | | 39 | 4 | | 40 | 1 | | 41 | 16 | | 42 | 30 | | 43 | 42 | | 44 | 4 | | 45 | 5 |
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| 96.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 77 | | matches | | 0 | "were folded" | | 1 | "being walked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 134 | | matches | | 0 | "was lifting" | | 1 | "was striking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 102 | | ratio | 0.01 | | matches | | 0 | "The temperature dropped another few degrees as she rounded the bend, and a thin reek met her—not rot, not quite." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 808 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.03217821782178218 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007425742574257425 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 11.74 | | std | 10.08 | | cv | 0.859 | | sampleLengths | | 0 | 7 | | 1 | 26 | | 2 | 28 | | 3 | 8 | | 4 | 3 | | 5 | 5 | | 6 | 6 | | 7 | 4 | | 8 | 8 | | 9 | 16 | | 10 | 13 | | 11 | 4 | | 12 | 1 | | 13 | 4 | | 14 | 26 | | 15 | 9 | | 16 | 27 | | 17 | 20 | | 18 | 20 | | 19 | 2 | | 20 | 2 | | 21 | 2 | | 22 | 12 | | 23 | 11 | | 24 | 35 | | 25 | 14 | | 26 | 2 | | 27 | 3 | | 28 | 5 | | 29 | 21 | | 30 | 16 | | 31 | 5 | | 32 | 10 | | 33 | 34 | | 34 | 27 | | 35 | 14 | | 36 | 4 | | 37 | 8 | | 38 | 2 | | 39 | 7 | | 40 | 38 | | 41 | 14 | | 42 | 3 | | 43 | 5 | | 44 | 2 | | 45 | 14 | | 46 | 24 | | 47 | 4 | | 48 | 10 | | 49 | 18 |
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| 88.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5784313725490197 | | totalSentences | 102 | | uniqueOpeners | 59 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 66 | | matches | | 0 | "Of course they had." | | 1 | "Still being walked on." | | 2 | "Somewhere far above her, on" |
| | ratio | 0.045 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 66 | | matches | | 0 | "She consulted her worn leather" | | 1 | "She stepped off the platform" | | 2 | "Her shoes found the rubble" | | 3 | "She found them in what" | | 4 | "He looked like a man" | | 5 | "She followed one set with" | | 6 | "She didn't touch it." | | 7 | "She studied it." | | 8 | "She put two fingers in" | | 9 | "She looked at Eva." |
| | ratio | 0.152 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 66 | | matches | | 0 | "The descent swallowed the daylight" | | 1 | "Harlow Quinn stood at the" | | 2 | "The station had been officially" | | 3 | "She consulted her worn leather" | | 4 | "The call had come through" | | 5 | "The constable, a young man" | | 6 | "She stepped off the platform" | | 7 | "The old tilework climbed the" | | 8 | "Her shoes found the rubble" | | 9 | "The temperature dropped another few" | | 10 | "Voices ahead, the flat vowels" | | 11 | "She found them in what" | | 12 | "A man, perhaps mid-thirties, on" | | 13 | "Eyes closed, the lashes undisturbed." | | 14 | "He looked like a man" | | 15 | "Quinn nodded at the figure" | | 16 | "Eva Kowalski glanced up, round" | | 17 | "A curl of red hair" | | 18 | "Eva rose, knees popping, and" | | 19 | "Quinn took the photograph." |
| | ratio | 0.712 | |
| 75.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 66 | | matches | | | ratio | 0.015 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "Behind her, a uniformed constable held a torch angled toward the throat of the tunnel, his breath misting in air that shouldn't have been this cold in July." | | 1 | "The old tilework climbed the curved walls in ranks of cream and oxblood, the patterns half-devoured by damp and a black lichen that smelled faintly of ink." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva had, her breath catching" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.088 | | leniency | 0.176 | | rawRatio | 0 | | effectiveRatio | 0 | |