| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 1 | | adverbTags | | 0 | "The old nickname rang twice [twice]" |
| | dialogueSentences | 51 | | tagDensity | 0.431 | | leniency | 0.863 | | rawRatio | 0.045 | | effectiveRatio | 0.039 | |
| 79.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1204 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "completely" | | 1 | "slowly" | | 2 | "truly" | | 3 | "suddenly" | | 4 | "softly" |
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| 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) | |
| 79.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1204 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulse" | | 1 | "wavering" | | 2 | "flicked" | | 3 | "eyebrow" | | 4 | "flicker" |
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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 | 100 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 100 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1187 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 750 | | uniqueNames | 11 | | maxNameDensity | 3.73 | | worstName | "Aurora" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 1 | | Silas | 2 | | Blackwood | 1 | | Morgan | 1 | | Raven | 1 | | Nest | 1 | | Eva | 24 | | Soho | 1 | | Aurora | 28 | | Roxy | 1 | | Music | 1 |
| | persons | | 0 | "Carter" | | 1 | "Silas" | | 2 | "Blackwood" | | 3 | "Morgan" | | 4 | "Raven" | | 5 | "Eva" | | 6 | "Aurora" | | 7 | "Roxy" | | 8 | "Music" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 1187 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 23.27 | | std | 15.63 | | cv | 0.672 | | sampleLengths | | 0 | 4 | | 1 | 56 | | 2 | 12 | | 3 | 6 | | 4 | 15 | | 5 | 15 | | 6 | 51 | | 7 | 31 | | 8 | 30 | | 9 | 10 | | 10 | 49 | | 11 | 2 | | 12 | 9 | | 13 | 17 | | 14 | 37 | | 15 | 23 | | 16 | 37 | | 17 | 29 | | 18 | 13 | | 19 | 3 | | 20 | 21 | | 21 | 16 | | 22 | 39 | | 23 | 16 | | 24 | 60 | | 25 | 18 | | 26 | 7 | | 27 | 10 | | 28 | 38 | | 29 | 31 | | 30 | 48 | | 31 | 5 | | 32 | 28 | | 33 | 46 | | 34 | 5 | | 35 | 27 | | 36 | 42 | | 37 | 11 | | 38 | 46 | | 39 | 3 | | 40 | 12 | | 41 | 4 | | 42 | 17 | | 43 | 42 | | 44 | 10 | | 45 | 30 | | 46 | 32 | | 47 | 24 | | 48 | 21 | | 49 | 20 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 137 | | matches | (empty) | |
| 30.37% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 127 | | ratio | 0.039 | | matches | | 0 | "Aurora caught the edge of herself in the smeared bar mirror—old sweatshirt, mop of black hair in need of a cut, postage-stamp scar on her wrist." | | 1 | "The writing, unmistakable—Aurora’s old careful hand, clean loops, regret pressed into every line." | | 2 | "It landed heavily—a blade between old friends." | | 3 | "Eva’s face, half-lit, was suddenly a stranger’s—older, bitterly beautiful, already halfway gone." | | 4 | "She studied the note again—the edges soft with wear, fingerprints pressed into the paper." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 724 | | adjectiveStacks | 1 | | stackExamples | | 0 | "midnight-black, wide eyes" |
| | adverbCount | 25 | | adverbRatio | 0.034530386740331494 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006906077348066298 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 9.35 | | std | 6.92 | | cv | 0.74 | | sampleLengths | | 0 | 4 | | 1 | 6 | | 2 | 11 | | 3 | 20 | | 4 | 19 | | 5 | 5 | | 6 | 2 | | 7 | 5 | | 8 | 3 | | 9 | 3 | | 10 | 8 | | 11 | 7 | | 12 | 10 | | 13 | 5 | | 14 | 18 | | 15 | 2 | | 16 | 2 | | 17 | 12 | | 18 | 9 | | 19 | 8 | | 20 | 10 | | 21 | 7 | | 22 | 14 | | 23 | 26 | | 24 | 1 | | 25 | 3 | | 26 | 10 | | 27 | 6 | | 28 | 35 | | 29 | 8 | | 30 | 2 | | 31 | 9 | | 32 | 5 | | 33 | 4 | | 34 | 8 | | 35 | 9 | | 36 | 22 | | 37 | 6 | | 38 | 4 | | 39 | 14 | | 40 | 5 | | 41 | 8 | | 42 | 17 | | 43 | 12 | | 44 | 7 | | 45 | 5 | | 46 | 17 | | 47 | 3 | | 48 | 3 | | 49 | 7 |
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| 68.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4251968503937008 | | totalSentences | 127 | | uniqueOpeners | 54 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 92 | | matches | | 0 | "Instead, a woman stared at" | | 1 | "Then shouts split the hush" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 92 | | matches | | 0 | "She turned, half-expecting a city" | | 1 | "They clocked her, then her" | | 2 | "She slid onto the stool" | | 3 | "Her gaze travelled, inventory-style, down" | | 4 | "she added, voice all edges" | | 5 | "She set her glass down" | | 6 | "She had rehearsed a dozen" | | 7 | "They all shrank to nothing." | | 8 | "Her tone toyed, but her" | | 9 | "She ignored it." | | 10 | "Her throat clicked" | | 11 | "Her voice, raw" | | 12 | "She slid a creased slip" | | 13 | "She read the words and" | | 14 | "Her anger made her eyes" | | 15 | "Her hand skated to Aurora’s" | | 16 | "She cut herself off, fists" | | 17 | "Her voice shifted, thin and" | | 18 | "It landed heavily—a blade between" | | 19 | "Her gaze flicked up" |
| | ratio | 0.283 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 92 | | matches | | 0 | "The words snapped like a" | | 1 | "Aurora Carter blinked, fingers still" | | 2 | "She turned, half-expecting a city" | | 3 | "Recognition skittered in too late." | | 4 | "Aurora’s lips parted." | | 5 | "Eva laughed, sharp as coins" | | 6 | "Aurora shifted, shoulders catching a" | | 7 | "The gold neon of The" | | 8 | "Aurora watched the bartender’s raised" | | 9 | "They clocked her, then her" | | 10 | "Some stories hung invisible above" | | 11 | "Eva let her coat slip," | | 12 | "She slid onto the stool" | | 13 | "Aurora caught the edge of" | | 14 | "A ghost’s daughter." | | 15 | "Aurora said at last" | | 16 | "Eva sipped, throat moving" | | 17 | "Her gaze travelled, inventory-style, down" | | 18 | "she added, voice all edges" | | 19 | "The word curdled the air." |
| | ratio | 0.935 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 2 | | matches | | 0 | "she added, voice all edges" | | 1 | "Her tone toyed, but her hands shook when they reached for her drink" |
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| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "she added (add)" | | 1 | "she whispered (whisper)" | | 2 | "Aurora muttered (mutter)" |
| | dialogueSentences | 51 | | tagDensity | 0.118 | | leniency | 0.235 | | rawRatio | 0.5 | | effectiveRatio | 0.118 | |