| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 84.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1327 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "perfectly" | | 1 | "suddenly" | | 2 | "very" | | 3 | "completely" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 66.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1327 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "measured" | | 1 | "silence" | | 2 | "traced" | | 3 | "weight" | | 4 | "unreadable" | | 5 | "flicked" |
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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 | 150 | | matches | (empty) | |
| 95.24% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 0 | | narrationSentences | 150 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 150 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1327 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1327 | | uniqueNames | 13 | | maxNameDensity | 0.83 | | worstName | "Owen" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Owen" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Empress | 1 | | Soho | 1 | | Chiswick | 2 | | Evan | 1 | | Silas | 5 | | Europe | 1 | | French | 1 | | Paris | 1 | | Owen | 11 | | Rory | 7 | | You | 6 |
| | persons | | 0 | "Nest" | | 1 | "Evan" | | 2 | "Silas" | | 3 | "Owen" | | 4 | "Rory" | | 5 | "You" |
| | places | | 0 | "Raven" | | 1 | "Soho" | | 2 | "Chiswick" | | 3 | "Europe" | | 4 | "Paris" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like one small bridge rebuilt acro" |
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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 | 1327 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 150 | | matches | (empty) | |
| 87.71% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 33.17 | | std | 15.16 | | cv | 0.457 | | sampleLengths | | 0 | 53 | | 1 | 48 | | 2 | 1 | | 3 | 25 | | 4 | 1 | | 5 | 61 | | 6 | 12 | | 7 | 26 | | 8 | 53 | | 9 | 28 | | 10 | 26 | | 11 | 48 | | 12 | 37 | | 13 | 54 | | 14 | 33 | | 15 | 35 | | 16 | 29 | | 17 | 48 | | 18 | 40 | | 19 | 43 | | 20 | 28 | | 21 | 49 | | 22 | 44 | | 23 | 27 | | 24 | 16 | | 25 | 57 | | 26 | 39 | | 27 | 26 | | 28 | 28 | | 29 | 40 | | 30 | 34 | | 31 | 28 | | 32 | 21 | | 33 | 21 | | 34 | 27 | | 35 | 25 | | 36 | 25 | | 37 | 63 | | 38 | 8 | | 39 | 20 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 150 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 233 | | matches | | 0 | "wasn't failing" | | 1 | "was waking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 150 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1333 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.035258814703675916 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.007501875468867217 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 150 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 150 | | mean | 8.85 | | std | 5.17 | | cv | 0.584 | | sampleLengths | | 0 | 12 | | 1 | 9 | | 2 | 9 | | 3 | 23 | | 4 | 8 | | 5 | 15 | | 6 | 11 | | 7 | 2 | | 8 | 12 | | 9 | 1 | | 10 | 14 | | 11 | 2 | | 12 | 9 | | 13 | 1 | | 14 | 5 | | 15 | 24 | | 16 | 11 | | 17 | 21 | | 18 | 2 | | 19 | 10 | | 20 | 6 | | 21 | 8 | | 22 | 12 | | 23 | 4 | | 24 | 5 | | 25 | 14 | | 26 | 13 | | 27 | 13 | | 28 | 4 | | 29 | 4 | | 30 | 5 | | 31 | 9 | | 32 | 2 | | 33 | 8 | | 34 | 2 | | 35 | 9 | | 36 | 11 | | 37 | 4 | | 38 | 6 | | 39 | 5 | | 40 | 2 | | 41 | 14 | | 42 | 10 | | 43 | 11 | | 44 | 4 | | 45 | 11 | | 46 | 6 | | 47 | 3 | | 48 | 2 | | 49 | 11 |
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| 58.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.36 | | totalSentences | 150 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 135 | | matches | | 0 | "Only on special occasions." | | 1 | "Always meant to send an" | | 2 | "Then another year disappeared." | | 3 | "Probably arrested for butchering the" | | 4 | "Instead she finished her drink" | | 5 | "Only when I think about" |
| | ratio | 0.044 | |
| 77.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 135 | | matches | | 0 | "She claimed a stool at" | | 1 | "Her fingers brushed the crescent" | | 2 | "He slid one stool closer." | | 3 | "His shoulders filled a tailored" | | 4 | "You still drink the cheap" | | 5 | "He tapped the laminated menu" | | 6 | "His voice carried a new" | | 7 | "She pushed the menu aside." | | 8 | "He poured two measures without" | | 9 | "His silence felt intentional." | | 10 | "I saw your name on" | | 11 | "She took a sip and" | | 12 | "You used to swear you'd" | | 13 | "His laugh came short and" | | 14 | "He rolled his shoulders as" | | 15 | "I burned the map." | | 16 | "She traced a water ring" | | 17 | "I thought about you." | | 18 | "His words dropped like pebbles" | | 19 | "She turned the glass in" |
| | ratio | 0.356 | |
| 45.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 112 | | totalSentences | 135 | | matches | | 0 | "Rory shoved the heavy wooden" | | 1 | "The bar smelled of damp" | | 2 | "Maps clung to the walls" | | 3 | "She claimed a stool at" | | 4 | "Her fingers brushed the crescent" | | 5 | "The movement drew a glance" | | 6 | "The name left his mouth" | | 7 | "Recognition slammed through her chest" | | 8 | "He slid one stool closer." | | 9 | "The years had carved new" | | 10 | "His shoulders filled a tailored" | | 11 | "The boy who once climbed" | | 12 | "The number hung between them" | | 13 | "You still drink the cheap" | | 14 | "He tapped the laminated menu" | | 15 | "His voice carried a new" | | 16 | "She pushed the menu aside." | | 17 | "Silas appeared from the back," | | 18 | "The silver ring on his" | | 19 | "He poured two measures without" |
| | ratio | 0.83 | |
| 37.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 135 | | matches | | 0 | "Because you stopped answering messages" |
| | ratio | 0.007 | |
| 96.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 4 | | matches | | 0 | "Maps clung to the walls like half-forgotten secrets, their ink faded under rows of black-and-white photographs that watched the room with indifferent eyes." | | 1 | "His shoulders filled a tailored coat that looked expensive and uncomfortable." | | 2 | "The girl who fled an abusive ex and an overbearing father now delivered curry and lived above a spy's bar." | | 3 | "The friendship that had once felt permanent now existed in fragments." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |