| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.257 | | leniency | 0.514 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 733 | | 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) | |
| 31.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 733 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "quivered" | | 1 | "tracing" | | 2 | "flickered" | | 3 | "flicker" | | 4 | "vibrated" | | 5 | "pulsed" | | 6 | "shattered" | | 7 | "disrupt" | | 8 | "scanned" | | 9 | "traced" |
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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 | 84 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 4 | | totalWords | 727 | | ratio | 0.006 | | matches | | 0 | "Remember" | | 1 | "You’ll come back." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 516 | | uniqueNames | 7 | | maxNameDensity | 3.29 | | worstName | "Harlow" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Harlow | 17 | | Quinn | 1 | | Veil | 1 | | Compass | 3 | | Kowalski | 1 | | Eva | 17 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Compass" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 62.45% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.376 | | wordCount | 727 | | matches | | 0 | "not north but toward the far wall" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 22.72 | | std | 13.66 | | cv | 0.601 | | sampleLengths | | 0 | 73 | | 1 | 32 | | 2 | 11 | | 3 | 36 | | 4 | 33 | | 5 | 26 | | 6 | 28 | | 7 | 25 | | 8 | 25 | | 9 | 21 | | 10 | 13 | | 11 | 43 | | 12 | 32 | | 13 | 12 | | 14 | 27 | | 15 | 6 | | 16 | 20 | | 17 | 32 | | 18 | 18 | | 19 | 25 | | 20 | 4 | | 21 | 20 | | 22 | 11 | | 23 | 8 | | 24 | 23 | | 25 | 14 | | 26 | 38 | | 27 | 12 | | 28 | 10 | | 29 | 6 | | 30 | 31 | | 31 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 84 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 111 | | matches | (empty) | |
| 12.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 110 | | ratio | 0.045 | | matches | | 0 | "The air smelled of damp stone and something sharper—burnt copper." | | 1 | "A flicker passed over her face—something sharp in her gaze." | | 2 | "Her reflection in the mirror showed her older—older than forty-one." | | 3 | "“Wait.” She pulled a small device from her pocket—a signal jammer." | | 4 | "The reflection now showed a figure standing behind them—tall, faceless." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 524 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.019083969465648856 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0057251908396946565 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 6.61 | | std | 3.89 | | cv | 0.589 | | sampleLengths | | 0 | 11 | | 1 | 22 | | 2 | 10 | | 3 | 15 | | 4 | 15 | | 5 | 12 | | 6 | 10 | | 7 | 5 | | 8 | 5 | | 9 | 9 | | 10 | 2 | | 11 | 7 | | 12 | 11 | | 13 | 9 | | 14 | 9 | | 15 | 9 | | 16 | 16 | | 17 | 8 | | 18 | 13 | | 19 | 13 | | 20 | 3 | | 21 | 7 | | 22 | 10 | | 23 | 8 | | 24 | 6 | | 25 | 19 | | 26 | 7 | | 27 | 2 | | 28 | 7 | | 29 | 5 | | 30 | 4 | | 31 | 6 | | 32 | 15 | | 33 | 4 | | 34 | 9 | | 35 | 6 | | 36 | 11 | | 37 | 5 | | 38 | 11 | | 39 | 10 | | 40 | 5 | | 41 | 7 | | 42 | 10 | | 43 | 5 | | 44 | 5 | | 45 | 3 | | 46 | 9 | | 47 | 4 | | 48 | 4 | | 49 | 10 |
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| 56.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.36363636363636365 | | totalSentences | 110 | | uniqueOpeners | 40 | |
| 88.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 75 | | matches | | 0 | "Somewhere, a clock struck midnight." | | 1 | "Somewhere, another rift stirred." |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 75 | | matches | | 0 | "She adjusted her grip on" | | 1 | "Her green eyes flickered between" | | 2 | "She tapped a spot near" | | 3 | "She held the vial to" | | 4 | "They moved deeper into the" | | 5 | "she said, pointing to a" | | 6 | "Her reflection in the mirror" | | 7 | "She pulled a small device" | | 8 | "She traced the symbols" | | 9 | "She turned to Eva." | | 10 | "She didn’t turn." | | 11 | "They sprinted as stones rained" | | 12 | "It glinted like the compass." |
| | ratio | 0.173 | |
| 26.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 75 | | matches | | 0 | "Rain lashed against the rusted" | | 1 | "Detective Harlow Quinn stood at" | | 2 | "The air smelled of damp" | | 3 | "She adjusted her grip on" | | 4 | "The needle quivered, pointing not" | | 5 | "Eva Kowalski crouched nearby, fingers" | | 6 | "Her green eyes flickered between" | | 7 | "Harlow knelt beside her, boots" | | 8 | "Eva spread the parchment on" | | 9 | "A circle marked with chalk," | | 10 | "She tapped a spot near" | | 11 | "Harlow nodded, pulling a small" | | 12 | "She held the vial to" | | 13 | "Eva countered, brushing dust from" | | 14 | "Harlow leaned closer." | | 15 | "The mirror’s surface rippled, distorting" | | 16 | "A flicker passed over her" | | 17 | "A low hum vibrated through" | | 18 | "The Compass spun wildly, needle" | | 19 | "Eva’s hand hovered over her" |
| | ratio | 0.867 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 92.86% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 35 | | tagDensity | 0.114 | | leniency | 0.229 | | rawRatio | 0.5 | | effectiveRatio | 0.114 | |