| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.30% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 637 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 5.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 637 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "measured" | | 3 | "efficient" | | 4 | "kaleidoscope" | | 5 | "pounding" | | 6 | "pawn" | | 7 | "jaw clenched" | | 8 | "scanning" | | 9 | "flickered" | | 10 | "dancing" | | 11 | "determined" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 50 | | matches | (empty) | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 50 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 50 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 640 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 634 | | uniqueNames | 12 | | maxNameDensity | 1.26 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 8 | | Quinn | 1 | | Soho | 1 | | Camden | 1 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | London | 1 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Veil" | | 5 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "London" |
| | globalScore | 0.869 | | windowScore | 0.667 | |
| 38.89% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like glass bottles" | | 1 | "as if making sure no one had followed" |
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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 | 640 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 50 | | matches | (empty) | |
| 74.03% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 45.71 | | std | 18.7 | | cv | 0.409 | | sampleLengths | | 0 | 49 | | 1 | 11 | | 2 | 59 | | 3 | 54 | | 4 | 49 | | 5 | 45 | | 6 | 31 | | 7 | 87 | | 8 | 48 | | 9 | 63 | | 10 | 50 | | 11 | 41 | | 12 | 41 | | 13 | 12 |
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| 98.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 50 | | matches | | |
| 11.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 106 | | matches | | 0 | "were heading" | | 1 | "weren't heading" | | 2 | "weren't looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 50 | | ratio | 0.06 | | matches | | 0 | "She recognized the sign from the files - the bone token requirement for entry." | | 1 | "The air grew cooler, the smell of wet pavement giving way to something else - ozone, metal, and a faint coppery scent." | | 2 | "She reached into her jacket pocket, her fingers closing around the small, carved bone token she'd taken from a suspect earlier that day - evidence she'd kept for herself." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 637 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.026687598116169546 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.012558869701726845 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 50 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 50 | | mean | 12.8 | | std | 5.44 | | cv | 0.425 | | sampleLengths | | 0 | 15 | | 1 | 17 | | 2 | 17 | | 3 | 11 | | 4 | 10 | | 5 | 18 | | 6 | 10 | | 7 | 21 | | 8 | 18 | | 9 | 16 | | 10 | 20 | | 11 | 15 | | 12 | 18 | | 13 | 16 | | 14 | 8 | | 15 | 12 | | 16 | 9 | | 17 | 9 | | 18 | 7 | | 19 | 14 | | 20 | 9 | | 21 | 4 | | 22 | 4 | | 23 | 18 | | 24 | 21 | | 25 | 11 | | 26 | 16 | | 27 | 13 | | 28 | 8 | | 29 | 10 | | 30 | 22 | | 31 | 16 | | 32 | 4 | | 33 | 6 | | 34 | 18 | | 35 | 29 | | 36 | 6 | | 37 | 12 | | 38 | 17 | | 39 | 11 | | 40 | 10 | | 41 | 6 | | 42 | 15 | | 43 | 3 | | 44 | 12 | | 45 | 5 | | 46 | 15 | | 47 | 13 | | 48 | 13 | | 49 | 12 |
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| 65.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.44 | | totalSentences | 50 | | uniqueOpeners | 22 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 50 | | matches | | 0 | "Instead, they leaped over a" | | 1 | "Instead, they turned down an" |
| | ratio | 0.04 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 50 | | matches | | 0 | "Her lungs burned, but years" | | 1 | "Her shoulder slammed against the" | | 2 | "They were heading toward the" | | 3 | "She recognized the sign from" | | 4 | "Her training screamed caution." | | 5 | "Her instincts screamed pursuit." | | 6 | "She wiped her face with" | | 7 | "She'd dismissed them as grief-induced" | | 8 | "Her sharp jaw clenched." | | 9 | "She reached into her jacket" | | 10 | "They weren't looking at her," | | 11 | "She straightened her soaked coat," | | 12 | "she muttered to herself, stepping" |
| | ratio | 0.26 | |
| 40.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 50 | | matches | | 0 | "Rain lashed against Detective Harlow" | | 1 | "The thunderous footsteps of her" | | 2 | "Her lungs burned, but years" | | 3 | "Harlow shouted, her voice cutting" | | 4 | "The figure in the dark" | | 5 | "Harlow followed without hesitation, pushing" | | 6 | "Her shoulder slammed against the" | | 7 | "The suspect was faster than" | | 8 | "Neon signs from nearby bars" | | 9 | "Harlow shielded her eyes with" | | 10 | "They were heading toward the" | | 11 | "The distinctive green neon sign" | | 12 | "Heart pounding against her ribs," | | 13 | "The suspect suddenly vanished through" | | 14 | "Harlow skidded to a halt," | | 15 | "The doorway was recessed, almost" | | 16 | "A faint green glow emanated" | | 17 | "She recognized the sign from" | | 18 | "This was The Veil Market," | | 19 | "Her training screamed caution." |
| | ratio | 0.84 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 1 | | matches | | 0 | "The suspect was faster than expected, already cutting through the next alley, which opened onto a busier street." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow shouted, her voice cutting through the storm" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Harlow shouted (shout)" | | 1 | "she muttered (mutter)" |
| | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |