| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "Her name had always [always]" |
| | dialogueSentences | 30 | | tagDensity | 0.267 | | leniency | 0.533 | | rawRatio | 0.125 | | effectiveRatio | 0.067 | |
| 91.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1721 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "tightly" | | 1 | "sharply" | | 2 | "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) | |
| 68.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1721 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "silk" | | 1 | "echo" | | 2 | "throbbed" | | 3 | "could feel" | | 4 | "pulse" | | 5 | "stomach" | | 6 | "flickered" | | 7 | "whisper" |
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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 | 198 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 198 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 222 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1721 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1599 | | uniqueNames | 15 | | maxNameDensity | 1.75 | | worstName | "Aurora" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Aurora" | | discoveredNames | | Heartstone | 4 | | Aurora | 28 | | Fae | 1 | | Grove | 1 | | Park | 1 | | London | 2 | | Putney | 1 | | Golden | 1 | | Empress | 1 | | Cheung | 1 | | Jennifer | 1 | | Carter | 1 | | Eva | 3 | | Britain | 1 | | Yu-Fei | 2 |
| | persons | | 0 | "Heartstone" | | 1 | "Aurora" | | 2 | "Cheung" | | 3 | "Jennifer" | | 4 | "Carter" | | 5 | "Eva" | | 6 | "Britain" | | 7 | "Yu-Fei" |
| | places | | 0 | "Fae" | | 1 | "Grove" | | 2 | "Park" | | 3 | "London" | | 4 | "Putney" | | 5 | "Golden" |
| | globalScore | 0.624 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 117 | | glossingSentenceCount | 1 | | matches | | 0 | "Not quite laughter, not on the second hearing" |
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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 | 1721 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 222 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 130 | | mean | 13.24 | | std | 14.78 | | cv | 1.116 | | sampleLengths | | 0 | 15 | | 1 | 18 | | 2 | 25 | | 3 | 8 | | 4 | 78 | | 5 | 5 | | 6 | 6 | | 7 | 40 | | 8 | 1 | | 9 | 21 | | 10 | 1 | | 11 | 9 | | 12 | 3 | | 13 | 25 | | 14 | 36 | | 15 | 7 | | 16 | 29 | | 17 | 70 | | 18 | 17 | | 19 | 5 | | 20 | 7 | | 21 | 16 | | 22 | 34 | | 23 | 8 | | 24 | 3 | | 25 | 15 | | 26 | 35 | | 27 | 1 | | 28 | 2 | | 29 | 7 | | 30 | 3 | | 31 | 53 | | 32 | 3 | | 33 | 3 | | 34 | 4 | | 35 | 50 | | 36 | 4 | | 37 | 8 | | 38 | 44 | | 39 | 4 | | 40 | 11 | | 41 | 11 | | 42 | 21 | | 43 | 6 | | 44 | 3 | | 45 | 7 | | 46 | 11 | | 47 | 2 | | 48 | 21 | | 49 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 198 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 257 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 222 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 942 | | adjectiveStacks | 1 | | stackExamples | | 0 | "neat, old-fashioned hand." |
| | adverbCount | 32 | | adverbRatio | 0.03397027600849257 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0074309978768577496 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 222 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 222 | | mean | 7.75 | | std | 5.61 | | cv | 0.724 | | sampleLengths | | 0 | 15 | | 1 | 13 | | 2 | 5 | | 3 | 6 | | 4 | 19 | | 5 | 8 | | 6 | 26 | | 7 | 15 | | 8 | 16 | | 9 | 21 | | 10 | 5 | | 11 | 6 | | 12 | 2 | | 13 | 20 | | 14 | 2 | | 15 | 2 | | 16 | 14 | | 17 | 1 | | 18 | 8 | | 19 | 13 | | 20 | 1 | | 21 | 9 | | 22 | 3 | | 23 | 11 | | 24 | 14 | | 25 | 12 | | 26 | 2 | | 27 | 2 | | 28 | 20 | | 29 | 7 | | 30 | 7 | | 31 | 22 | | 32 | 20 | | 33 | 22 | | 34 | 28 | | 35 | 8 | | 36 | 9 | | 37 | 5 | | 38 | 7 | | 39 | 16 | | 40 | 16 | | 41 | 2 | | 42 | 3 | | 43 | 13 | | 44 | 8 | | 45 | 2 | | 46 | 1 | | 47 | 15 | | 48 | 12 | | 49 | 12 |
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| 42.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.2972972972972973 | | totalSentences | 222 | | uniqueOpeners | 66 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 178 | | matches | | 0 | "Only a deep crimson stone" | | 1 | "Then she had spent two" | | 2 | "Then he had sent her" | | 3 | "Only black branches tangled against" | | 4 | "Only Eva had believed her." | | 5 | "Then every flower spoke at" | | 6 | "Of course it did not." | | 7 | "Then a pale hand pushed" |
| | ratio | 0.045 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 178 | | matches | | 0 | "she muttered, catching the silver" | | 1 | "She tore free, leaving a" | | 2 | "It did not hold a" | | 3 | "It did not hold grass" | | 4 | "It certainly did not hold" | | 5 | "She stopped between two stones." | | 6 | "Her voice travelled farther than" | | 7 | "It bounced from the standing" | | 8 | "Its crimson glow seeped through" | | 9 | "She had received the pendant" | | 10 | "She had laughed when she" | | 11 | "he had told her" | | 12 | "Her father used to notice" | | 13 | "Her keyring held a small" | | 14 | "It drifted from one branch" | | 15 | "Her stomach clenched." | | 16 | "She began to walk." | | 17 | "She could see every landmark" | | 18 | "She counted twelve standing stones" | | 19 | "It was narrow, grey, and" |
| | ratio | 0.287 | |
| 47.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 147 | | totalSentences | 178 | | matches | | 0 | "The Heartstone burned against Aurora’s" | | 1 | "she muttered, catching the silver" | | 2 | "A branch hooked her coat" | | 3 | "She tore free, leaving a" | | 4 | "The Fae Grove had no" | | 5 | "Richmond Park held deer, dog" | | 6 | "It did not hold a" | | 7 | "It did not hold grass" | | 8 | "It certainly did not hold" | | 9 | "She stopped between two stones." | | 10 | "The brambles behind her knitted" | | 11 | "The hedge had grown thick" | | 12 | "Her voice travelled farther than" | | 13 | "It bounced from the standing" | | 14 | "The second word had not" | | 15 | "Aurora held still." | | 16 | "The Heartstone throbbed once, a" | | 17 | "Its crimson glow seeped through" | | 18 | "She had received the pendant" | | 19 | "She had laughed when she" |
| | ratio | 0.826 | |
| 28.09% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 178 | | matches | | | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 2 | | matches | | 0 | "It certainly did not hold wildflowers blooming in the dark, their white faces tilted towards Aurora as if they recognised her." | | 1 | "The blades leaned aside in a neat line, as though something invisible had crossed them." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 30 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0.333 | | effectiveRatio | 0.067 | |