| 46.15% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 1 | | adverbTags | | 0 | "He glanced around [around]" |
| | dialogueSentences | 13 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 1 | | effectiveRatio | 0.154 | |
| 85.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 685 | | 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) | |
| 48.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 685 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "perfect" | | 1 | "echoed" | | 2 | "wavering" | | 3 | "pulsed" | | 4 | "measured" | | 5 | "flicked" | | 6 | "shattered" |
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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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | 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 | 0 | | markdownWords | 0 | | totalWords | 677 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.65% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 622 | | uniqueNames | 10 | | maxNameDensity | 1.45 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 1 | | Quinn | 9 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" | | 7 | "Morris" |
| | places | (empty) | | globalScore | 0.777 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 677 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 18.81 | | std | 17.41 | | cv | 0.926 | | sampleLengths | | 0 | 58 | | 1 | 36 | | 2 | 32 | | 3 | 46 | | 4 | 37 | | 5 | 8 | | 6 | 51 | | 7 | 21 | | 8 | 12 | | 9 | 11 | | 10 | 75 | | 11 | 12 | | 12 | 23 | | 13 | 5 | | 14 | 7 | | 15 | 34 | | 16 | 8 | | 17 | 13 | | 18 | 2 | | 19 | 2 | | 20 | 4 | | 21 | 23 | | 22 | 3 | | 23 | 9 | | 24 | 31 | | 25 | 5 | | 26 | 13 | | 27 | 9 | | 28 | 16 | | 29 | 26 | | 30 | 12 | | 31 | 18 | | 32 | 4 | | 33 | 3 | | 34 | 4 | | 35 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 112 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 83 | | ratio | 0.06 | | matches | | 0 | "The suspect—hooded, fast—had been tailing her for blocks before she’d made him." | | 1 | "She slammed her shoulder into it, bursting into a dimly lit stairwell that reeked of mildew and something sharper—ozone, maybe, or old magic." | | 2 | "The sound of dripping water echoed off the curved brick walls—old Tube tunnels, maybe." | | 3 | "A muffled voice carried through the door—low, urgent." | | 4 | "The crowd was a shifting mass of shadows and silhouettes—some human, some not." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 630 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.0380952380952381 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009523809523809525 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 8.16 | | std | 4.86 | | cv | 0.596 | | sampleLengths | | 0 | 17 | | 1 | 17 | | 2 | 12 | | 3 | 12 | | 4 | 11 | | 5 | 13 | | 6 | 12 | | 7 | 13 | | 8 | 10 | | 9 | 9 | | 10 | 8 | | 11 | 12 | | 12 | 3 | | 13 | 23 | | 14 | 5 | | 15 | 6 | | 16 | 2 | | 17 | 6 | | 18 | 18 | | 19 | 8 | | 20 | 9 | | 21 | 14 | | 22 | 4 | | 23 | 14 | | 24 | 10 | | 25 | 5 | | 26 | 12 | | 27 | 4 | | 28 | 8 | | 29 | 4 | | 30 | 4 | | 31 | 7 | | 32 | 20 | | 33 | 11 | | 34 | 13 | | 35 | 15 | | 36 | 16 | | 37 | 10 | | 38 | 2 | | 39 | 10 | | 40 | 9 | | 41 | 4 | | 42 | 5 | | 43 | 7 | | 44 | 2 | | 45 | 14 | | 46 | 8 | | 47 | 10 | | 48 | 5 | | 49 | 3 |
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| 54.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3855421686746988 | | totalSentences | 83 | | uniqueOpeners | 32 | |
| 98.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 68 | | matches | | 0 | "Too late for witnesses, too" | | 1 | "Slowly, he turned." |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 68 | | matches | | 0 | "She skidded around the corner," | | 1 | "Her wristwatch, the worn leather" | | 2 | "She slammed her shoulder into" | | 3 | "She hesitated, just for a" | | 4 | "She took the stairs two" | | 5 | "She’d heard whispers about the" | | 6 | "She grabbed the token." | | 7 | "She followed, keeping her strides" | | 8 | "His Saint Christopher medallion glinted" | | 9 | "She pulled her arm free." | | 10 | "He glanced around, then leaned" | | 11 | "He studied her face, then" | | 12 | "They moved through the market," | | 13 | "She met his eyes." | | 14 | "He reached into his jacket" | | 15 | "He threw it against the" | | 16 | "She lunged for the alcove," | | 17 | "It wasn’t a suspect." | | 18 | "It was DS Morris." |
| | ratio | 0.279 | |
| 33.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 68 | | matches | | 0 | "The rain hammered down on" | | 1 | "Detective Harlow Quinn sprinted after" | | 2 | "The suspect—hooded, fast—had been tailing" | | 3 | "She skidded around the corner," | | 4 | "The street was narrow, lined" | | 5 | "The hooded figure darted into" | | 6 | "Her wristwatch, the worn leather" | | 7 | "The alley dead-ended at a" | | 8 | "The suspect was already through" | | 9 | "Quinn didn’t slow." | | 10 | "She slammed her shoulder into" | | 11 | "The steps descended into darkness." | | 12 | "She hesitated, just for a" | | 13 | "She took the stairs two" | | 14 | "The air grew thicker, warmer," | | 15 | "The sound of dripping water" | | 16 | "A single bone token hung" | | 17 | "Quinn’s fingers hovered over it." | | 18 | "She’d heard whispers about the" | | 19 | "A muffled voice carried through" |
| | ratio | 0.853 | |
| 73.53% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 68 | | matches | | 0 | "Now he was running, and" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "She slammed her shoulder into it, bursting into a dimly lit stairwell that reeked of mildew and something sharper—ozone, maybe, or old magic." |
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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 | |