| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said slowly [slowly]" |
| | dialogueSentences | 32 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.083 | | effectiveRatio | 0.063 | |
| 82.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1115 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "loosely" | | 1 | "slowly" | | 2 | "perfectly" | | 3 | "tightly" |
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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) | |
| 86.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1115 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "weight" | | 1 | "etched" | | 2 | "flickered" |
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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 | 61 | | matches | | |
| 72.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 81 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1115 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 727 | | uniqueNames | 5 | | maxNameDensity | 1.24 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Fenwick" | | discoveredNames | | Northern | 1 | | Camden | 1 | | Quinn | 9 | | Rhys | 1 | | Fenwick | 8 |
| | persons | | | places | (empty) | | globalScore | 0.881 | | windowScore | 1 | |
| 41.30% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 2 | | matches | | 0 | "as if reaching for a train that had left a century ago" | | 1 | "symbol that seemed to crawl when she looked at it directly" |
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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 | 1115 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 81 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 29.34 | | std | 21.76 | | cv | 0.742 | | sampleLengths | | 0 | 63 | | 1 | 8 | | 2 | 45 | | 3 | 8 | | 4 | 49 | | 5 | 59 | | 6 | 5 | | 7 | 54 | | 8 | 23 | | 9 | 11 | | 10 | 43 | | 11 | 4 | | 12 | 9 | | 13 | 11 | | 14 | 53 | | 15 | 12 | | 16 | 8 | | 17 | 66 | | 18 | 51 | | 19 | 3 | | 20 | 14 | | 21 | 51 | | 22 | 60 | | 23 | 23 | | 24 | 32 | | 25 | 8 | | 26 | 62 | | 27 | 47 | | 28 | 5 | | 29 | 14 | | 30 | 16 | | 31 | 29 | | 32 | 31 | | 33 | 4 | | 34 | 73 | | 35 | 27 | | 36 | 11 | | 37 | 23 |
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| 70.75% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 61 | | matches | | 0 | "been sealed" | | 1 | "was curled" | | 2 | "been worn" | | 3 | "been told" | | 4 | "was etched" | | 5 | "were stained" |
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| 34.71% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 121 | | matches | | 0 | "was going" | | 1 | "was looking" | | 2 | "was not pointing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 81 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 730 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint blue-black, like" |
| | adverbCount | 22 | | adverbRatio | 0.030136986301369864 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00821917808219178 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 81 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 81 | | mean | 13.77 | | std | 10.04 | | cv | 0.729 | | sampleLengths | | 0 | 23 | | 1 | 22 | | 2 | 18 | | 3 | 8 | | 4 | 21 | | 5 | 19 | | 6 | 5 | | 7 | 8 | | 8 | 27 | | 9 | 14 | | 10 | 4 | | 11 | 4 | | 12 | 7 | | 13 | 14 | | 14 | 38 | | 15 | 5 | | 16 | 11 | | 17 | 43 | | 18 | 23 | | 19 | 11 | | 20 | 4 | | 21 | 15 | | 22 | 24 | | 23 | 4 | | 24 | 9 | | 25 | 11 | | 26 | 29 | | 27 | 7 | | 28 | 17 | | 29 | 9 | | 30 | 3 | | 31 | 8 | | 32 | 8 | | 33 | 23 | | 34 | 5 | | 35 | 15 | | 36 | 11 | | 37 | 4 | | 38 | 8 | | 39 | 22 | | 40 | 4 | | 41 | 8 | | 42 | 9 | | 43 | 3 | | 44 | 14 | | 45 | 8 | | 46 | 12 | | 47 | 7 | | 48 | 14 | | 49 | 10 |
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| 81.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5308641975308642 | | totalSentences | 81 | | uniqueOpeners | 43 | |
| 57.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 58 | | matches | | 0 | "Somewhere in the tunnel a" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 58 | | matches | | 0 | "He was twenty-nine, eager, and" | | 1 | "His shoes were polished." | | 2 | "His hair was dry." | | 3 | "She was looking at the" | | 4 | "He held it up to" | | 5 | "They did not match the" | | 6 | "They were too large." | | 7 | "She rose, her left wrist" | | 8 | "She glanced at the dead" | | 9 | "His gold watch had stopped" | | 10 | "She eased it over with" | | 11 | "It was trembling, swinging in" | | 12 | "She turned the compass so" | | 13 | "He crouched, sweeping his torch" | | 14 | "he said slowly" | | 15 | "He stood, rubbing the back" | | 16 | "She pointed to the man's" |
| | ratio | 0.293 | |
| 3.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 58 | | matches | | 0 | "The Northern line platform at" | | 1 | "Harlow Quinn stepped down from" | | 2 | "The air smelled of wet" | | 3 | "DC Rhys Fenwick stood at" | | 4 | "He was twenty-nine, eager, and" | | 5 | "The pen was going now." | | 6 | "Quinn said, and stepped over" | | 7 | "The body lay near the" | | 8 | "The man wore a charcoal" | | 9 | "His shoes were polished." | | 10 | "His hair was dry." | | 11 | "That was the first thing" | | 12 | "Quinn crouched beside him, knees" | | 13 | "The floor was slick with" | | 14 | "Fenwick shifted his weight" | | 15 | "Quinn did not answer." | | 16 | "She was looking at the" | | 17 | "Fenwick pulled on a glove" | | 18 | "He held it up to" | | 19 | "The spiral seemed to catch" |
| | ratio | 0.914 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 4 | | matches | | 0 | "The body lay near the platform's edge, face up, one arm flung toward the tracks as if reaching for a train that had left a century ago." | | 1 | "The man wore a charcoal suit that had cost more than her monthly rent." | | 2 | "Between his fingers sat a small disc of bone, yellowed and smooth, carved with a spiral that had been worn almost flat by handling." | | 3 | "The nails were pale and perfectly clean, but the tips of the index and middle fingers were stained a faint blue-black, like ink that had dried and cracked." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0 | | effectiveRatio | 0 | |