| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.857 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1091 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "lightly" | | 1 | "carefully" | | 2 | "precisely" | | 3 | "slowly" |
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| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1091 | | totalAiIsms | 30 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | |
| | highlights | | 0 | "scanned" | | 1 | "loomed" | | 2 | "sentinels" | | 3 | "pulse" | | 4 | "warmth" | | 5 | "comforting" | | 6 | "oppressive" | | 7 | "silence" | | 8 | "pounding" | | 9 | "measured" | | 10 | "echoed" | | 11 | "scanning" | | 12 | "whisper" | | 13 | "wavered" | | 14 | "quickened" | | 15 | "pulsed" | | 16 | "traced" | | 17 | "cascading" | | 18 | "raced" | | 19 | "searing" | | 20 | "otherworldly" | | 21 | "racing" | | 22 | "echoing" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "knuckles turned white" | | count | 1 |
| | 2 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "knuckles turned white" | | 2 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 92 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 92 | | 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 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1087 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1064 | | uniqueNames | 5 | | maxNameDensity | 1.13 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Heartstone | 4 | | Pendant | 2 | | Grove | 8 | | Aurora | 12 | | Rory | 5 |
| | persons | | 0 | "Heartstone" | | 1 | "Pendant" | | 2 | "Aurora" | | 3 | "Rory" |
| | places | | | globalScore | 0.936 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like quicksand, pulling her down" |
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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 | 1087 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 65.18% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 40.26 | | std | 15.22 | | cv | 0.378 | | sampleLengths | | 0 | 87 | | 1 | 39 | | 2 | 73 | | 3 | 55 | | 4 | 34 | | 5 | 40 | | 6 | 53 | | 7 | 18 | | 8 | 38 | | 9 | 29 | | 10 | 50 | | 11 | 51 | | 12 | 35 | | 13 | 36 | | 14 | 40 | | 15 | 17 | | 16 | 45 | | 17 | 24 | | 18 | 27 | | 19 | 30 | | 20 | 24 | | 21 | 31 | | 22 | 37 | | 23 | 54 | | 24 | 43 | | 25 | 42 | | 26 | 35 |
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| 97.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 92 | | matches | | 0 | "was meant" | | 1 | "was supposed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 191 | | matches | (empty) | |
| 80.75% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 92 | | ratio | 0.022 | | matches | | 0 | "Her eyes narrowed as she took another step forward, expecting to catch a glimpse of who—or what—called out to her." | | 1 | "It was her – or, more precisely, someone who could have been her, years ago." |
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| 91.31% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1068 | | adjectiveStacks | 1 | | stackExamples | | 0 | "flowing over thin shoulders," |
| | adverbCount | 46 | | adverbRatio | 0.04307116104868914 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.016853932584269662 | |
| 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 | 11.82 | | std | 5.41 | | cv | 0.458 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 14 | | 3 | 24 | | 4 | 17 | | 5 | 17 | | 6 | 14 | | 7 | 8 | | 8 | 9 | | 9 | 21 | | 10 | 12 | | 11 | 14 | | 12 | 17 | | 13 | 10 | | 14 | 9 | | 15 | 6 | | 16 | 15 | | 17 | 15 | | 18 | 12 | | 19 | 22 | | 20 | 15 | | 21 | 8 | | 22 | 1 | | 23 | 7 | | 24 | 9 | | 25 | 7 | | 26 | 9 | | 27 | 5 | | 28 | 10 | | 29 | 11 | | 30 | 11 | | 31 | 8 | | 32 | 10 | | 33 | 7 | | 34 | 1 | | 35 | 3 | | 36 | 11 | | 37 | 16 | | 38 | 3 | | 39 | 11 | | 40 | 10 | | 41 | 5 | | 42 | 12 | | 43 | 12 | | 44 | 26 | | 45 | 26 | | 46 | 14 | | 47 | 8 | | 48 | 3 | | 49 | 15 |
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| 55.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.34782608695652173 | | totalSentences | 92 | | uniqueOpeners | 32 | |
| 37.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 90 | | matches | | 0 | "Carefully, Aurora knelt and traced" |
| | ratio | 0.011 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 90 | | matches | | 0 | "She caught her balance just" | | 1 | "She clutched the Heartstone Pendant" | | 2 | "Her eyes darted from shadow" | | 3 | "she muttered to herself, nerves" | | 4 | "Her voice sounded hollow, absorbed" | | 5 | "She continued onward, each step" | | 6 | "She just hoped this time" | | 7 | "She turned back and nearly" | | 8 | "Her grip on the pendant" | | 9 | "It was barely a whisper," | | 10 | "Her voice wavered, lost in" | | 11 | "Her heart sank." | | 12 | "She had no interest in" | | 13 | "She had to keep moving." | | 14 | "She paused, swearing she saw" | | 15 | "She reached the centre of" | | 16 | "Her eyes narrowed as she" | | 17 | "Her voice did not tremble" | | 18 | "It cut through the silence" | | 19 | "It was her – or," |
| | ratio | 0.333 | |
| 32.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 90 | | matches | | 0 | "Aurora stumbled into the grove," | | 1 | "She caught her balance just" | | 2 | "The standing stones loomed like" | | 3 | "Time moved differently here, she" | | 4 | "She clutched the Heartstone Pendant" | | 5 | "The soft pulse of warmth" | | 6 | "Rory had come to the" | | 7 | "Eva, her childhood friend and" | | 8 | "An oppressive silence hung in" | | 9 | "A rustle behind her made" | | 10 | "The grove behind was empty," | | 11 | "A trick of the mind," | | 12 | "Her eyes darted from shadow" | | 13 | "she muttered to herself, nerves" | | 14 | "Her voice sounded hollow, absorbed" | | 15 | "She continued onward, each step" | | 16 | "Aurora knew she must be" | | 17 | "The Heartstone hadn't led her" | | 18 | "She just hoped this time" | | 19 | "A high-pitched giggle echoed through" |
| | ratio | 0.856 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 90 | | matches | | 0 | "If she sought them out," | | 1 | "Yet, the moment Aurora had" |
| | ratio | 0.022 | |
| 92.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 4 | | matches | | 0 | "The standing stones loomed like silent sentinels under the silver light of the moon, casting long shadows that danced and shifted with the wind." | | 1 | "Eva, her childhood friend and confidante who had been there through thick and thin, often spoke of the Grove's mystical properties." | | 2 | "The child version of herself dissolved into mist, her laugh trembling through the air before winking out." | | 3 | "In the stillness that followed, the wind returned, carrying with it the faint sounds of life beyond the ancient stones." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 2 | | matches | | 0 | "she muttered, nerves getting the better of her" | | 1 | "a whisper floated, her own name barely brushing against her consciousness" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "herself whispered (whisper)" |
| | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 1 | | effectiveRatio | 0.571 | |