| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 86.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1115 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 19.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1115 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "solitary" | | 3 | "gloom" | | 4 | "stark" | | 5 | "navigated" | | 6 | "echoing" | | 7 | "pulse" | | 8 | "scanning" | | 9 | "ensure" | | 10 | "warmth" | | 11 | "rhythmic" | | 12 | "mechanical" | | 13 | "silence" |
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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 | 36 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 36 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 56 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1115 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 95.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 638 | | uniqueNames | 6 | | maxNameDensity | 1.1 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Evan" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Evan | 4 | | Aurora | 7 | | Silas | 4 |
| | persons | | 0 | "Raven" | | 1 | "Evan" | | 2 | "Aurora" | | 3 | "Silas" |
| | places | | | globalScore | 0.951 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 35 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.897 | | wordCount | 1115 | | matches | | 0 | "not as places she had been, but as walls she had eventually walked through" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 56 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 34.84 | | std | 19.26 | | cv | 0.553 | | sampleLengths | | 0 | 68 | | 1 | 77 | | 2 | 53 | | 3 | 10 | | 4 | 32 | | 5 | 16 | | 6 | 39 | | 7 | 20 | | 8 | 15 | | 9 | 44 | | 10 | 10 | | 11 | 44 | | 12 | 28 | | 13 | 40 | | 14 | 53 | | 15 | 43 | | 16 | 34 | | 17 | 16 | | 18 | 14 | | 19 | 43 | | 20 | 27 | | 21 | 34 | | 22 | 30 | | 23 | 46 | | 24 | 13 | | 25 | 21 | | 26 | 49 | | 27 | 43 | | 28 | 25 | | 29 | 21 | | 30 | 16 | | 31 | 91 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 36 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 103 | | matches | (empty) | |
| 91.84% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 56 | | ratio | 0.018 | | matches | | 0 | "He didn't offer comfort or platitudes; he simply slid a fresh glass toward her, filled with clear water." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 642 | | adjectiveStacks | 1 | | stackExamples | | 0 | "white, crescent-shaped scar" |
| | adverbCount | 12 | | adverbRatio | 0.018691588785046728 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.01557632398753894 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 56 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 56 | | mean | 19.91 | | std | 8.74 | | cv | 0.439 | | sampleLengths | | 0 | 27 | | 1 | 21 | | 2 | 20 | | 3 | 20 | | 4 | 15 | | 5 | 18 | | 6 | 24 | | 7 | 15 | | 8 | 15 | | 9 | 23 | | 10 | 10 | | 11 | 16 | | 12 | 16 | | 13 | 16 | | 14 | 13 | | 15 | 11 | | 16 | 15 | | 17 | 20 | | 18 | 15 | | 19 | 44 | | 20 | 10 | | 21 | 17 | | 22 | 17 | | 23 | 10 | | 24 | 28 | | 25 | 40 | | 26 | 8 | | 27 | 33 | | 28 | 12 | | 29 | 43 | | 30 | 34 | | 31 | 16 | | 32 | 14 | | 33 | 25 | | 34 | 18 | | 35 | 27 | | 36 | 34 | | 37 | 30 | | 38 | 24 | | 39 | 22 | | 40 | 13 | | 41 | 21 | | 42 | 17 | | 43 | 18 | | 44 | 4 | | 45 | 10 | | 46 | 16 | | 47 | 9 | | 48 | 18 | | 49 | 25 |
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| 45.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3392857142857143 | | totalSentences | 56 | | uniqueOpeners | 19 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 36 | | matches | (empty) | | ratio | 0 | |
| 8.89% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 36 | | matches | | 0 | "She pushed the heavy oak" | | 1 | "He caught her eye, a" | | 2 | "His face, once defined by" | | 3 | "He pushed his chair back," | | 4 | "She didn't move toward him," | | 5 | "She rubbed the crescent-shaped scar" | | 6 | "He leaned forward, the slight" | | 7 | "He didn't interrupt, simply watched" | | 8 | "He took a single, deliberate" | | 9 | "He looked back at her," | | 10 | "He looked at Evan with" | | 11 | "He smoothed his tie, pulled" | | 12 | "He paused at the door," | | 13 | "He didn't look back." | | 14 | "She looked at the abandoned" | | 15 | "He didn't offer comfort or" | | 16 | "She stared at her wrist," | | 17 | "It was a map of" | | 18 | "She set the glass down," |
| | ratio | 0.528 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 35 | | totalSentences | 36 | | matches | | 0 | "Rain drummed a frantic rhythm" | | 1 | "Aurora wiped a bead of" | | 2 | "She pushed the heavy oak" | | 3 | "Silas stood behind the mahogany" | | 4 | "He caught her eye, a" | | 5 | "The bar was mostly empty," | | 6 | "The man wore a tailored" | | 7 | "Aurora navigated past a wobbly" | | 8 | "His face, once defined by" | | 9 | "The man’s voice rasped, carrying" | | 10 | "He pushed his chair back," | | 11 | "Aurora let her delivery bag" | | 12 | "She didn't move toward him," | | 13 | "She rubbed the crescent-shaped scar" | | 14 | "Silas ceased his polishing and" | | 15 | "He leaned forward, the slight" | | 16 | "He didn't interrupt, simply watched" | | 17 | "Evan stood, smoothing the front" | | 18 | "He took a single, deliberate" | | 19 | "He looked back at her," |
| | ratio | 0.972 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 36 | | matches | (empty) | | ratio | 0 | |
| 56.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 4 | | matches | | 0 | "The man wore a tailored charcoal wool coat that looked entirely out of place in this gloom, a stark contrast to the dust-moted atmosphere." | | 1 | "He took a single, deliberate step forward, his eyes scanning the walls of the bar, lingering on the faded maps and sepia-toned photos of men who had long since …" | | 2 | "He looked at Evan with a detached, professional scrutiny that made the man’s stiff shoulders drop an inch." | | 3 | "Evan looked at Aurora, his gaze softening into something that might have been regret, though it held all the warmth of a morgue slab." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |