| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "She stepped back [back]" | | 1 | "she said quietly [quietly]" |
| | dialogueSentences | 53 | | tagDensity | 0.302 | | leniency | 0.604 | | rawRatio | 0.125 | | effectiveRatio | 0.075 | |
| 94.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 907 | | totalAiIsmAdverbs | 1 | | 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) | |
| 50.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 907 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "familiar" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "weight" | | 4 | "traced" | | 5 | "tinged" | | 6 | "glinting" | | 7 | "fractured" |
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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 | 55 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 55 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 904 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 636 | | uniqueNames | 10 | | maxNameDensity | 0.63 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Silas | 4 | | Blackwood | 1 | | Empty | 1 | | Soho | 1 |
| | persons | | 0 | "Nest" | | 1 | "Aurora" | | 2 | "Carter" | | 3 | "Silas" | | 4 | "Blackwood" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 1 | | matches | | 0 | "something like gravel and whiskey" |
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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 | 904 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 92 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 18.83 | | std | 17.24 | | cv | 0.915 | | sampleLengths | | 0 | 87 | | 1 | 44 | | 2 | 1 | | 3 | 66 | | 4 | 8 | | 5 | 23 | | 6 | 27 | | 7 | 11 | | 8 | 11 | | 9 | 40 | | 10 | 13 | | 11 | 5 | | 12 | 4 | | 13 | 27 | | 14 | 49 | | 15 | 2 | | 16 | 10 | | 17 | 11 | | 18 | 17 | | 19 | 21 | | 20 | 35 | | 21 | 11 | | 22 | 5 | | 23 | 10 | | 24 | 7 | | 25 | 26 | | 26 | 6 | | 27 | 25 | | 28 | 7 | | 29 | 18 | | 30 | 16 | | 31 | 23 | | 32 | 3 | | 33 | 18 | | 34 | 18 | | 35 | 4 | | 36 | 4 | | 37 | 22 | | 38 | 35 | | 39 | 5 | | 40 | 2 | | 41 | 5 | | 42 | 20 | | 43 | 40 | | 44 | 5 | | 45 | 3 | | 46 | 25 | | 47 | 29 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 55 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 103 | | matches | (empty) | |
| 49.69% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 92 | | ratio | 0.033 | | matches | | 0 | "She glanced at the clock—10:47 PM." | | 1 | "He hadn’t changed much—still tall, still carrying that quiet authority, but time had carved lines around his eyes and silvered his auburn hair further." | | 2 | "The moment fractured, and Silas straightened, his expression hardening into something she hadn’t seen in years—the mask of the operative, the man who’d carried secrets in his bones." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 641 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.029641185647425898 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0078003120124804995 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 9.83 | | std | 7.08 | | cv | 0.72 | | sampleLengths | | 0 | 33 | | 1 | 21 | | 2 | 6 | | 3 | 27 | | 4 | 20 | | 5 | 2 | | 6 | 15 | | 7 | 7 | | 8 | 1 | | 9 | 14 | | 10 | 20 | | 11 | 24 | | 12 | 8 | | 13 | 2 | | 14 | 6 | | 15 | 15 | | 16 | 8 | | 17 | 16 | | 18 | 11 | | 19 | 9 | | 20 | 2 | | 21 | 5 | | 22 | 6 | | 23 | 19 | | 24 | 16 | | 25 | 5 | | 26 | 10 | | 27 | 3 | | 28 | 5 | | 29 | 4 | | 30 | 17 | | 31 | 10 | | 32 | 7 | | 33 | 28 | | 34 | 14 | | 35 | 2 | | 36 | 9 | | 37 | 1 | | 38 | 5 | | 39 | 6 | | 40 | 10 | | 41 | 7 | | 42 | 12 | | 43 | 9 | | 44 | 23 | | 45 | 12 | | 46 | 7 | | 47 | 4 | | 48 | 5 | | 49 | 9 |
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| 63.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.40217391304347827 | | totalSentences | 92 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 50 | | matches | | 0 | "She glanced at the clock—10:47" | | 1 | "She was about to pull" | | 2 | "Her hand stilled on the" | | 3 | "She turned, her heart skipping," | | 4 | "He hadn’t changed much—still tall," | | 5 | "He pushed through the doorway," | | 6 | "She folded the trays into" | | 7 | "She stepped back, wary" | | 8 | "He moved into the main" | | 9 | "She followed him, her boots" | | 10 | "He stopped by the bar," | | 11 | "He turned, those hazel eyes" | | 12 | "She laughed, sharp and brittle." | | 13 | "He leaned closer" | | 14 | "She gestured to the bar," | | 15 | "He studied her face, the" | | 16 | "She shifted, uneasy" | | 17 | "She stared at the green" | | 18 | "she said quietly" | | 19 | "He set down a glass" |
| | ratio | 0.58 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 50 | | matches | | 0 | "The amber glow of the" | | 1 | "The scent of star anise" | | 2 | "She glanced at the clock—10:47" | | 3 | "The bar should have been" | | 4 | "She was about to pull" | | 5 | "The kind of steps that" | | 6 | "Her hand stilled on the" | | 7 | "The voice was rougher than" | | 8 | "She turned, her heart skipping," | | 9 | "He hadn’t changed much—still tall," | | 10 | "A new patch of gray" | | 11 | "He pushed through the doorway," | | 12 | "She folded the trays into" | | 13 | "A ghost of a smile" | | 14 | "She stepped back, wary" | | 15 | "He moved into the main" | | 16 | "She followed him, her boots" | | 17 | "He stopped by the bar," | | 18 | "The memory hit her like" | | 19 | "He turned, those hazel eyes" |
| | ratio | 0.9 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 50 | | matches | | 0 | "Before the scars, before the" |
| | ratio | 0.02 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 3 | | matches | | 0 | "Late nights in a cramped flat above a bar in Soho, him reading her law textbooks aloud when she’d fallen asleep, his hand brushing hers under the table." | | 1 | "The moment fractured, and Silas straightened, his expression hardening into something she hadn’t seen in years—the mask of the operative, the man who’d carried …" | | 2 | "Outside, the city hummed with its usual noise, oblivious to the two old ghosts standing in the half-light of a bar that had seen better days." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 6 | | matches | | 0 | "She stepped back, wary" | | 1 | "He moved, his gaze sweeping the room" | | 2 | "He turned, those hazel eyes locking onto hers" | | 3 | "She shifted, uneasy" | | 4 | "He set down, its contents long gone warm" | | 5 | "He crouched, his knee protesting as he shifted his weight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 53 | | tagDensity | 0.057 | | leniency | 0.113 | | rawRatio | 0.333 | | effectiveRatio | 0.038 | |