| 70.97% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 4 | | adverbTags | | 0 | "Eva’s voice echoed slightly [slightly]" | | 1 | "Eva continued quietly [quietly]" | | 2 | "Eva said softly [softly]" | | 3 | "she said quietly [quietly]" |
| | dialogueSentences | 62 | | tagDensity | 0.403 | | leniency | 0.806 | | rawRatio | 0.16 | | effectiveRatio | 0.129 | |
| 90.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1501 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "softly" | | 2 | "gently" |
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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) | |
| 63.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1501 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "grave" | | 1 | "gloom" | | 2 | "footsteps" | | 3 | "echoing" | | 4 | "standard" | | 5 | "echoed" | | 6 | "stomach" | | 7 | "intricate" | | 8 | "etched" | | 9 | "tracing" | | 10 | "weight" |
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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 | 114 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1492 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1100 | | uniqueNames | 12 | | maxNameDensity | 2.73 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 30 | | Metropolitan | 1 | | Police | 1 | | Kowalski | 1 | | Eva | 17 | | Morris | 3 | | One | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Morris" | | 5 | "One" |
| | places | (empty) | | globalScore | 0.136 | | windowScore | 0 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like museum-issue trousers, and Qu" | | 1 | "something like approval in her green eyes" |
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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 | 1492 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 151 | | matches | | 0 | "caught that scent" | | 1 | "had that look" | | 2 | "seen that shade" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 22.61 | | std | 16.77 | | cv | 0.742 | | sampleLengths | | 0 | 45 | | 1 | 42 | | 2 | 31 | | 3 | 71 | | 4 | 10 | | 5 | 59 | | 6 | 9 | | 7 | 24 | | 8 | 15 | | 9 | 4 | | 10 | 25 | | 11 | 9 | | 12 | 4 | | 13 | 52 | | 14 | 28 | | 15 | 12 | | 16 | 22 | | 17 | 27 | | 18 | 19 | | 19 | 41 | | 20 | 8 | | 21 | 21 | | 22 | 7 | | 23 | 13 | | 24 | 5 | | 25 | 23 | | 26 | 2 | | 27 | 11 | | 28 | 29 | | 29 | 7 | | 30 | 42 | | 31 | 4 | | 32 | 56 | | 33 | 8 | | 34 | 7 | | 35 | 52 | | 36 | 39 | | 37 | 9 | | 38 | 15 | | 39 | 6 | | 40 | 7 | | 41 | 45 | | 42 | 33 | | 43 | 24 | | 44 | 17 | | 45 | 30 | | 46 | 4 | | 47 | 14 | | 48 | 35 | | 49 | 43 |
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| 92.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 114 | | matches | | 0 | "was turned" | | 1 | "was tucked" | | 2 | "was gone" | | 3 | "been scrubbed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 172 | | matches | | 0 | "was standing" | | 1 | "was watching" |
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| 48.25% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 151 | | ratio | 0.033 | | matches | | 0 | "And the smell—that coppery tang wasn’t just blood." | | 1 | "Then Quinn saw it—faint green staining around the cuff, a patina like weathered copper." | | 2 | "Something glinted among the debris—a sliver of brass." | | 3 | "She saw what the others had missed—a story written in brass and blood and broken needles." | | 4 | "But beneath it, something else—the first thread of a truth she’d been chasing for three years, shimmering like a compass needle pointing into the dark." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1107 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.031616982836495035 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.01084010840108401 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 9.88 | | std | 6.58 | | cv | 0.666 | | sampleLengths | | 0 | 14 | | 1 | 18 | | 2 | 13 | | 3 | 14 | | 4 | 17 | | 5 | 11 | | 6 | 18 | | 7 | 13 | | 8 | 23 | | 9 | 11 | | 10 | 12 | | 11 | 11 | | 12 | 14 | | 13 | 10 | | 14 | 16 | | 15 | 16 | | 16 | 27 | | 17 | 9 | | 18 | 5 | | 19 | 13 | | 20 | 6 | | 21 | 14 | | 22 | 1 | | 23 | 4 | | 24 | 25 | | 25 | 5 | | 26 | 4 | | 27 | 4 | | 28 | 7 | | 29 | 11 | | 30 | 30 | | 31 | 4 | | 32 | 8 | | 33 | 20 | | 34 | 12 | | 35 | 2 | | 36 | 10 | | 37 | 10 | | 38 | 24 | | 39 | 3 | | 40 | 9 | | 41 | 10 | | 42 | 6 | | 43 | 5 | | 44 | 3 | | 45 | 6 | | 46 | 7 | | 47 | 12 | | 48 | 1 | | 49 | 1 |
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| 62.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3973509933774834 | | totalSentences | 151 | | uniqueOpeners | 60 | |
| 64.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 104 | | matches | | 0 | "Then Quinn saw it—faint green" | | 1 | "Gently, she turned the dead" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 104 | | matches | | 0 | "She approached with military precision," | | 1 | "His face was turned away," | | 2 | "Her curly red hair was" | | 3 | "She wore a heavy wool" | | 4 | "She kept her hands in" | | 5 | "She glanced at the uniformed" | | 6 | "Her stomach tightened." | | 7 | "She examined the victim’s hands." | | 8 | "His knuckles were clean, nails" | | 9 | "She leaned closer, ignoring the" | | 10 | "She had that look academics" | | 11 | "She’d seen that shade before" | | 12 | "His palm was clean, but" | | 13 | "She flipped pages, showed Quinn" | | 14 | "She walked a slow circuit" | | 15 | "She stopped near the edge" | | 16 | "She looked back at the" | | 17 | "She stood, facing Eva" | | 18 | "She walked to where the" | | 19 | "She moved to where the" |
| | ratio | 0.25 | |
| 56.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 104 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn stepped through" | | 2 | "The place reeked of damp" | | 3 | "Crime scene lights threw harsh" | | 4 | "She approached with military precision," | | 5 | "The sharp line of her" | | 6 | "A man lay sprawled near" | | 7 | "His face was turned away," | | 8 | "A spreading halo of blood" | | 9 | "Eva Kowalski looked up as" | | 10 | "Her curly red hair was" | | 11 | "She wore a heavy wool" | | 12 | "Eva’s voice echoed slightly in" | | 13 | "Quinn didn’t crouch" | | 14 | "She kept her hands in" | | 15 | "Eva gestured toward the dead" | | 16 | "Quinn’s gaze swept the platform." | | 17 | "That’s when Quinn noticed the" | | 18 | "The blood pattern on the" | | 19 | "The spatter arced too high," |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 104 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 65.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn asked, her voice flat" | | 1 | "Eva repeated, her voice low" | | 2 | "Quinn said, the words coming out harder than she intended" |
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| 53.23% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 6 | | fancyTags | | 0 | "Eva continued quietly (continue)" | | 1 | "Quinn murmured (murmur)" | | 2 | "Quinn spoke (speak)" | | 3 | "she guessed (guess)" | | 4 | "Eva repeated (repeat)" | | 5 | "Quinn guessed (guess)" |
| | dialogueSentences | 62 | | tagDensity | 0.194 | | leniency | 0.387 | | rawRatio | 0.5 | | effectiveRatio | 0.194 | |