| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 836 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 64.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 836 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "shimmered" | | 2 | "traced" | | 3 | "reminder" | | 4 | "familiar" |
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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 | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 86 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | 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 | 2 | | markdownWords | 8 | | totalWords | 829 | | ratio | 0.01 | | matches | | 0 | "The Veil Market. Entry requirement: bone token." | | 1 | "other" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 820 | | uniqueNames | 18 | | maxNameDensity | 0.98 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 8 | | Dean | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Morris | 3 | | Wapping | 1 | | Hendon | 1 | | Camden | 2 | | Veil | 2 | | Market | 2 | | Met | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Estates | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Morris" | | 5 | "Camden" | | 6 | "Market" | | 7 | "Herrera" | | 8 | "Saint" | | 9 | "Christopher" |
| | places | | 0 | "London" | | 1 | "Dean" | | 2 | "Street" | | 3 | "Wapping" | | 4 | "Hendon" | | 5 | "Met" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like this—wrong, off-kilter, *othe" |
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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 | 829 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 39.48 | | std | 20.65 | | cv | 0.523 | | sampleLengths | | 0 | 77 | | 1 | 67 | | 2 | 46 | | 3 | 44 | | 4 | 35 | | 5 | 61 | | 6 | 31 | | 7 | 27 | | 8 | 22 | | 9 | 3 | | 10 | 71 | | 11 | 34 | | 12 | 47 | | 13 | 68 | | 14 | 15 | | 15 | 31 | | 16 | 36 | | 17 | 4 | | 18 | 25 | | 19 | 55 | | 20 | 30 |
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| 93.02% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 86 | | matches | | 0 | "was connected" | | 1 | "was plastered" | | 2 | "was lost" |
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| 94.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 126 | | matches | | 0 | "was running" | | 1 | "was gaining" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 86 | | ratio | 0.07 | | matches | | 0 | "Ahead, a dark figure—a man in a soaked leather jacket—darted around the corner onto Dean Street." | | 1 | "Fear there, yes, but something else—a calculation." | | 2 | "Even from a distance, Quinn recognized the shape—a human knuckle bone, drilled and threaded on a cord." | | 3 | "She examined the crevice—a narrow, almost invisible slot." | | 4 | "Morris had vanished chasing a lead that smelled like this—wrong, off-kilter, *other*." | | 5 | "The brick wall before her promised the opposite—chaos, truth, maybe death." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 831 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.019253910950661854 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0024067388688327317 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 9.64 | | std | 5.91 | | cv | 0.613 | | sampleLengths | | 0 | 19 | | 1 | 16 | | 2 | 16 | | 3 | 26 | | 4 | 4 | | 5 | 16 | | 6 | 6 | | 7 | 17 | | 8 | 12 | | 9 | 12 | | 10 | 6 | | 11 | 11 | | 12 | 15 | | 13 | 14 | | 14 | 12 | | 15 | 7 | | 16 | 11 | | 17 | 14 | | 18 | 8 | | 19 | 8 | | 20 | 2 | | 21 | 6 | | 22 | 3 | | 23 | 8 | | 24 | 16 | | 25 | 12 | | 26 | 17 | | 27 | 2 | | 28 | 7 | | 29 | 3 | | 30 | 4 | | 31 | 10 | | 32 | 16 | | 33 | 5 | | 34 | 7 | | 35 | 9 | | 36 | 1 | | 37 | 1 | | 38 | 8 | | 39 | 1 | | 40 | 12 | | 41 | 10 | | 42 | 3 | | 43 | 4 | | 44 | 17 | | 45 | 12 | | 46 | 7 | | 47 | 5 | | 48 | 26 | | 49 | 4 |
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| 48.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.37209302325581395 | | totalSentences | 86 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 71.85% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 81 | | matches | | 0 | "She didn’t break stride." | | 1 | "She’d seen him outside the" | | 2 | "He’d been talking to a" | | 3 | "Her breath came in sharp" | | 4 | "She pushed harder, her movements" | | 5 | "He cut left into a" | | 6 | "She was gaining." | | 7 | "He fumbled in his jacket" | | 8 | "He shoved the bone into" | | 9 | "She placed a hand against" | | 10 | "She examined the crevice—a narrow," | | 11 | "She stood there, rain coursing" | | 12 | "She had no token." | | 13 | "She didn’t know the rules," | | 14 | "She still woke some nights" | | 15 | "She could turn back." | | 16 | "Her fingers traced the scar" | | 17 | "He’d patched up a kid" | | 18 | "He knew things" | | 19 | "He might even be down" |
| | ratio | 0.37 | |
| 40.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 81 | | matches | | 0 | "The rain fell in relentless" | | 1 | "Detective Harlow Quinn’s boots slapped" | | 2 | "The green neon sign of" | | 3 | "She didn’t break stride." | | 4 | "This one, this runner, was" | | 5 | "She’d seen him outside the" | | 6 | "He’d been talking to a" | | 7 | "Her breath came in sharp" | | 8 | "The salt-and-pepper crop of her" | | 9 | "The worn leather band of" | | 10 | "She pushed harder, her movements" | | 11 | "The man glanced back, his" | | 12 | "He cut left into a" | | 13 | "Quinn followed, the smell of" | | 14 | "The alley opened onto a" | | 15 | "The runner was already halfway" | | 16 | "Quinn dug deep, her thighs" | | 17 | "She was gaining." | | 18 | "The gap closed to twenty" | | 19 | "The man skidded to a" |
| | ratio | 0.84 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 81 | | matches | | 0 | "Now he was running, and" | | 1 | "Even from a distance, Quinn" |
| | ratio | 0.025 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 1 | | matches | | 0 | "Morris had vanished chasing a lead that smelled like this—wrong, off-kilter, *other*." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |