| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" |
| | dialogueSentences | 28 | | tagDensity | 0.179 | | leniency | 0.357 | | rawRatio | 0.2 | | effectiveRatio | 0.071 | |
| 88.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1304 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "really" |
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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) | |
| 57.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1304 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "kaleidoscope" | | 1 | "predictable" | | 2 | "etched" | | 3 | "echoed" | | 4 | "constructed" | | 5 | "pulse" | | 6 | "shattered" | | 7 | "velvet" | | 8 | "pounding" | | 9 | "normalcy" |
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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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 151 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 174 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 6 | | totalWords | 1301 | | ratio | 0.005 | | matches | | 0 | "Abandoned Tube station." | | 1 | "The Veil Market." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1116 | | uniqueNames | 10 | | maxNameDensity | 1.43 | | worstName | "Quinn" | | maxWindowNameDensity | 6 | | worstWindowName | "Herrera" | | discoveredNames | | Quinn | 16 | | Camden | 1 | | London | 1 | | Tube | 2 | | Veil | 1 | | Stalls | 1 | | Victorian | 2 | | Glock | 1 | | Christopher | 1 | | Herrera | 13 |
| | persons | | 0 | "Quinn" | | 1 | "Stalls" | | 2 | "Christopher" | | 3 | "Herrera" |
| | places | | 0 | "Camden" | | 1 | "London" | | 2 | "Victorian" |
| | globalScore | 0.783 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a seven-foot lizard in a tren" |
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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 | 1301 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 174 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 80 | | mean | 16.26 | | std | 11.59 | | cv | 0.713 | | sampleLengths | | 0 | 31 | | 1 | 3 | | 2 | 43 | | 3 | 10 | | 4 | 36 | | 5 | 15 | | 6 | 30 | | 7 | 10 | | 8 | 14 | | 9 | 4 | | 10 | 7 | | 11 | 25 | | 12 | 21 | | 13 | 9 | | 14 | 32 | | 15 | 3 | | 16 | 34 | | 17 | 7 | | 18 | 21 | | 19 | 20 | | 20 | 7 | | 21 | 16 | | 22 | 3 | | 23 | 53 | | 24 | 18 | | 25 | 26 | | 26 | 23 | | 27 | 4 | | 28 | 27 | | 29 | 1 | | 30 | 11 | | 31 | 18 | | 32 | 6 | | 33 | 5 | | 34 | 30 | | 35 | 6 | | 36 | 10 | | 37 | 5 | | 38 | 20 | | 39 | 8 | | 40 | 35 | | 41 | 23 | | 42 | 9 | | 43 | 36 | | 44 | 19 | | 45 | 25 | | 46 | 7 | | 47 | 11 | | 48 | 29 | | 49 | 5 |
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| 98.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 151 | | matches | | 0 | "was panicked" | | 1 | "was known" | | 2 | "was gone" |
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| 10.43% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 211 | | matches | | 0 | "was heading" | | 1 | "was teeming" | | 2 | "was bargaining" | | 3 | "was looking" | | 4 | "was disappearing" | | 5 | "was pounding" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 174 | | ratio | 0.006 | | matches | | 0 | "The kid—maybe twenty, hoodie pulled low—scrambled over the opposite side." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1119 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.021447721179624665 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004468275245755138 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 174 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 174 | | mean | 7.48 | | std | 4.76 | | cv | 0.636 | | sampleLengths | | 0 | 11 | | 1 | 20 | | 2 | 3 | | 3 | 7 | | 4 | 14 | | 5 | 18 | | 6 | 4 | | 7 | 10 | | 8 | 11 | | 9 | 14 | | 10 | 11 | | 11 | 7 | | 12 | 4 | | 13 | 4 | | 14 | 17 | | 15 | 13 | | 16 | 10 | | 17 | 8 | | 18 | 6 | | 19 | 4 | | 20 | 3 | | 21 | 2 | | 22 | 2 | | 23 | 15 | | 24 | 3 | | 25 | 7 | | 26 | 6 | | 27 | 12 | | 28 | 3 | | 29 | 9 | | 30 | 6 | | 31 | 10 | | 32 | 5 | | 33 | 11 | | 34 | 3 | | 35 | 4 | | 36 | 9 | | 37 | 10 | | 38 | 11 | | 39 | 7 | | 40 | 8 | | 41 | 9 | | 42 | 2 | | 43 | 2 | | 44 | 13 | | 45 | 7 | | 46 | 7 | | 47 | 10 | | 48 | 3 | | 49 | 3 |
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| 48.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3563218390804598 | | totalSentences | 174 | | uniqueOpeners | 62 | |
| 75.19% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 133 | | matches | | 0 | "Too loose for a council" | | 1 | "Just a smooth, grey face" | | 2 | "Too many limbs." |
| | ratio | 0.023 | |
| 69.62% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 133 | | matches | | 0 | "She hit the skip with" | | 1 | "Her worn leather watch caught" | | 2 | "She didn't look down." | | 3 | "She landed in a puddle," | | 4 | "He was fast, but he" | | 5 | "He skidded to a halt," | | 6 | "He looked past her, then" | | 7 | "He didn't surrender." | | 8 | "It was loose." | | 9 | "She stared at the dark" | | 10 | "She holstered her weapon and" | | 11 | "She landed in a crouch," | | 12 | "She recognised the architecture." | | 13 | "She moved forward, hugging the" | | 14 | "She rounded the corner and" | | 15 | "It wasn't abandoned." | | 16 | "It was teeming." | | 17 | "She gripped the handle of" | | 18 | "He slapped it against the" | | 19 | "They were too busy trading." |
| | ratio | 0.376 | |
| 12.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 133 | | matches | | 0 | "Mud splashed against the back" | | 1 | "The suspect vaulted a rusty" | | 2 | "Quinn ignored the burn in" | | 3 | "She hit the skip with" | | 4 | "Her worn leather watch caught" | | 5 | "She didn't look down." | | 6 | "The kid—maybe twenty, hoodie pulled" | | 7 | "She landed in a puddle," | | 8 | "He was fast, but he" | | 9 | "Panic makes you sloppy." | | 10 | "Panic makes you predictable." | | 11 | "Quinn cut left at the" | | 12 | "He skidded to a halt," | | 13 | "He looked past her, then" | | 14 | "A grim smile split his" | | 15 | "He didn't surrender." | | 16 | "Quinn lunged, her fingers brushing" | | 17 | "It was loose." | | 18 | "She stared at the dark" | | 19 | "A draft carrying the scent" |
| | ratio | 0.895 | |
| 37.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 133 | | matches | | | ratio | 0.008 | |
| 89.29% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 3 | | matches | | 0 | "The suspect vaulted a rusty skip in the Camden alleyway, his trainers slipping on the wet cobblestones before finding purchase." | | 1 | "Symbols that made her eyes water if she stared too long." | | 2 | "A woman in a Victorian gown was bargaining with a creature that looked like a seven-foot lizard in a trench coat." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Herrera whispered (whisper)" |
| | dialogueSentences | 28 | | tagDensity | 0.036 | | leniency | 0.071 | | rawRatio | 1 | | effectiveRatio | 0.071 | |