| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "Patel stood too [too]" | | 1 | "Eva's words came fast [fast]" |
| | dialogueSentences | 47 | | tagDensity | 0.362 | | leniency | 0.723 | | rawRatio | 0.118 | | effectiveRatio | 0.085 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1348 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 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 | 1348 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "perfect" | | 1 | "echoed" | | 2 | "etched" |
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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 | 152 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 152 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 183 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1348 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 52.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 976 | | uniqueNames | 17 | | maxNameDensity | 1.95 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Patel" | | discoveredNames | | Camden | 1 | | Forensics | 2 | | Patel | 12 | | Quinn | 19 | | Eva | 7 | | Oxford | 1 | | Green | 1 | | Museum | 1 | | Maglite | 1 | | Met | 1 | | Veil | 1 | | Compass | 1 | | Milo | 1 | | Finch | 1 | | Matthew | 1 | | Morris | 1 | | Empty | 3 |
| | persons | | 0 | "Forensics" | | 1 | "Patel" | | 2 | "Quinn" | | 3 | "Eva" | | 4 | "Green" | | 5 | "Museum" | | 6 | "Compass" | | 7 | "Milo" | | 8 | "Finch" | | 9 | "Matthew" | | 10 | "Morris" |
| | places | | | globalScore | 0.527 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like a footnote" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.742 | | wordCount | 1348 | | matches | | 0 | "not north, but straight at the tiled wall behind the bench" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 183 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 69 | | mean | 19.54 | | std | 19.79 | | cv | 1.013 | | sampleLengths | | 0 | 8 | | 1 | 13 | | 2 | 25 | | 3 | 70 | | 4 | 6 | | 5 | 41 | | 6 | 3 | | 7 | 16 | | 8 | 11 | | 9 | 2 | | 10 | 12 | | 11 | 43 | | 12 | 51 | | 13 | 5 | | 14 | 8 | | 15 | 4 | | 16 | 4 | | 17 | 1 | | 18 | 3 | | 19 | 5 | | 20 | 25 | | 21 | 20 | | 22 | 17 | | 23 | 47 | | 24 | 5 | | 25 | 38 | | 26 | 6 | | 27 | 54 | | 28 | 6 | | 29 | 17 | | 30 | 2 | | 31 | 13 | | 32 | 9 | | 33 | 5 | | 34 | 48 | | 35 | 11 | | 36 | 13 | | 37 | 50 | | 38 | 20 | | 39 | 4 | | 40 | 64 | | 41 | 7 | | 42 | 6 | | 43 | 10 | | 44 | 32 | | 45 | 4 | | 46 | 29 | | 47 | 48 | | 48 | 10 | | 49 | 18 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 152 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 167 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 183 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 331 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 5 | | adverbRatio | 0.015105740181268883 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.006042296072507553 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 183 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 183 | | mean | 7.37 | | std | 6.91 | | cv | 0.938 | | sampleLengths | | 0 | 8 | | 1 | 2 | | 2 | 11 | | 3 | 10 | | 4 | 7 | | 5 | 8 | | 6 | 21 | | 7 | 1 | | 8 | 1 | | 9 | 1 | | 10 | 18 | | 11 | 9 | | 12 | 19 | | 13 | 2 | | 14 | 4 | | 15 | 13 | | 16 | 13 | | 17 | 3 | | 18 | 4 | | 19 | 7 | | 20 | 1 | | 21 | 3 | | 22 | 16 | | 23 | 11 | | 24 | 2 | | 25 | 12 | | 26 | 4 | | 27 | 10 | | 28 | 11 | | 29 | 18 | | 30 | 10 | | 31 | 3 | | 32 | 4 | | 33 | 15 | | 34 | 14 | | 35 | 5 | | 36 | 5 | | 37 | 3 | | 38 | 5 | | 39 | 4 | | 40 | 4 | | 41 | 1 | | 42 | 3 | | 43 | 2 | | 44 | 3 | | 45 | 25 | | 46 | 11 | | 47 | 4 | | 48 | 5 | | 49 | 7 |
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| 68.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.45604395604395603 | | totalSentences | 182 | | uniqueOpeners | 83 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 117 | | matches | | 0 | "Always with a book." | | 1 | "Always with an explanation that" | | 2 | "Instead of north and south," |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 117 | | matches | | 0 | "He held his notebook like" | | 1 | "She did not touch." | | 2 | "He had arranged himself." | | 3 | "His fingers were locked tight" | | 4 | "His lips were pale." | | 5 | "It was precise, a perfect" | | 6 | "She tilted her head." | | 7 | "He forced himself to look." | | 8 | "She checked it out of" | | 9 | "She wore round glasses and" | | 10 | "She tucked hair behind her" | | 11 | "Her mouth opened." | | 12 | "She pulled it out." | | 13 | "She did not ask permission." | | 14 | "Her fingers found it." | | 15 | "She held it up." | | 16 | "She stood in one fluid" | | 17 | "She walked the platform." | | 18 | "She counted tiles." | | 19 | "She followed it." |
| | ratio | 0.248 | |
| 71.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 117 | | matches | | 0 | "Quinn's boot slipped in water" | | 1 | "Patel stood too close to" | | 2 | "He held his notebook like" | | 3 | "The body lay centre stage" | | 4 | "This was the Camden spur," | | 5 | "Some kid with a crowbar" | | 6 | "She did not touch." | | 7 | "He had arranged himself." | | 8 | "His fingers were locked tight" | | 9 | "His lips were pale." | | 10 | "A faint frosting clung to" | | 11 | "The skin beneath was blue," | | 12 | "It was precise, a perfect" | | 13 | "Quinn pulled a pair of" | | 14 | "The snap echoed." | | 15 | "She tilted her head." | | 16 | "The platform lights, run off" | | 17 | "The station name was still" | | 18 | "Water damage blistered the letters." | | 19 | "Patel leaned in." |
| | ratio | 0.778 | |
| 42.74% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 117 | | matches | | | ratio | 0.009 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "The body lay centre stage on the northbound platform of a station that hadn't seen a northbound train in forty years." | | 1 | "It was precise, a perfect circle around his mouth, as if he had pressed his face to ice." | | 2 | "The air tore open with a wet, soft pop, and warm yellow light spilled out from a market that had not existed a second earlier, full of voices and bells and thin…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 47 | | tagDensity | 0.191 | | leniency | 0.383 | | rawRatio | 0.111 | | effectiveRatio | 0.043 | |