| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1276 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 60.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1276 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "trembled" | | 1 | "warmth" | | 2 | "footfall" | | 3 | "pulse" | | 4 | "lilt" | | 5 | "whisper" | | 6 | "etched" | | 7 | "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 | 149 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 149 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 158 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1274 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1223 | | uniqueNames | 8 | | maxNameDensity | 1.39 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Park | 1 | | Heartstone | 3 | | Eva | 1 | | December | 1 | | Aurora | 17 | | Grove | 2 | | Carter | 1 | | Welsh | 1 |
| | persons | | 0 | "Heartstone" | | 1 | "Eva" | | 2 | "Aurora" | | 3 | "Carter" |
| | places | | | globalScore | 0.805 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared beneath them in ink so fresh it shone" |
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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 | 1274 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 158 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 91 | | mean | 14 | | std | 13.59 | | cv | 0.971 | | sampleLengths | | 0 | 15 | | 1 | 50 | | 2 | 11 | | 3 | 11 | | 4 | 14 | | 5 | 43 | | 6 | 4 | | 7 | 34 | | 8 | 17 | | 9 | 4 | | 10 | 9 | | 11 | 61 | | 12 | 9 | | 13 | 8 | | 14 | 34 | | 15 | 5 | | 16 | 22 | | 17 | 2 | | 18 | 12 | | 19 | 13 | | 20 | 8 | | 21 | 5 | | 22 | 44 | | 23 | 2 | | 24 | 8 | | 25 | 4 | | 26 | 48 | | 27 | 44 | | 28 | 9 | | 29 | 6 | | 30 | 25 | | 31 | 6 | | 32 | 29 | | 33 | 10 | | 34 | 7 | | 35 | 29 | | 36 | 5 | | 37 | 5 | | 38 | 1 | | 39 | 2 | | 40 | 19 | | 41 | 29 | | 42 | 3 | | 43 | 9 | | 44 | 2 | | 45 | 11 | | 46 | 5 | | 47 | 43 | | 48 | 7 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 201 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 158 | | ratio | 0.006 | | matches | | 0 | "The park had a particular smell after rain—mud, leaf rot, damp stone—but the air here held something sweeter." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1228 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.02768729641693811 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 158 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 158 | | mean | 8.06 | | std | 5.72 | | cv | 0.709 | | sampleLengths | | 0 | 15 | | 1 | 25 | | 2 | 8 | | 3 | 17 | | 4 | 11 | | 5 | 9 | | 6 | 2 | | 7 | 2 | | 8 | 2 | | 9 | 10 | | 10 | 8 | | 11 | 4 | | 12 | 2 | | 13 | 29 | | 14 | 4 | | 15 | 11 | | 16 | 8 | | 17 | 15 | | 18 | 3 | | 19 | 14 | | 20 | 4 | | 21 | 9 | | 22 | 31 | | 23 | 18 | | 24 | 12 | | 25 | 1 | | 26 | 1 | | 27 | 7 | | 28 | 8 | | 29 | 10 | | 30 | 6 | | 31 | 18 | | 32 | 5 | | 33 | 10 | | 34 | 12 | | 35 | 2 | | 36 | 6 | | 37 | 6 | | 38 | 3 | | 39 | 10 | | 40 | 8 | | 41 | 5 | | 42 | 23 | | 43 | 6 | | 44 | 15 | | 45 | 2 | | 46 | 3 | | 47 | 2 | | 48 | 3 | | 49 | 4 |
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| 49.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.33544303797468356 | | totalSentences | 158 | | uniqueOpeners | 53 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 129 | | matches | | 0 | "Somewhere beyond the dark, a" | | 1 | "Then the sound came again." | | 2 | "Then huge, each print pressed" |
| | ratio | 0.023 | |
| 92.87% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 129 | | matches | | 0 | "She checked the folded note" | | 1 | "She had not called." | | 2 | "Its crimson centre held a" | | 3 | "Her voice went nowhere." | | 4 | "She followed the narrow route" | | 5 | "They should not have been" | | 6 | "It twisted in one sharp" | | 7 | "Her hand went to the" | | 8 | "She swung the phone round." | | 9 | "It poured between the roots" | | 10 | "She forced herself onwards." | | 11 | "She knew them from walks" | | 12 | "Their bark looked black and" | | 13 | "She stopped at the first" | | 14 | "Its surface held shallow carvings." | | 15 | "she told the clearing" | | 16 | "It resembled wind travelling down" | | 17 | "Her hair did not move." | | 18 | "She turned so fast her" | | 19 | "Her phone beam swung across" |
| | ratio | 0.318 | |
| 41.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 129 | | matches | | 0 | "Aurora left the main path" | | 1 | "Richmond Park lay under a" | | 2 | "The sound carried too far," | | 3 | "She checked the folded note" | | 4 | "The handwriting had pressed hard" | | 5 | "Aurora tucked it back into" | | 6 | "The phone showed 12:17." | | 7 | "That had happened the moment" | | 8 | "She had not called." | | 9 | "The Heartstone hung against her" | | 10 | "Its crimson centre held a" | | 11 | "Each step she took made" | | 12 | "Her voice went nowhere." | | 13 | "The trees swallowed it before" | | 14 | "She followed the narrow route" | | 15 | "The park had a particular" | | 16 | "Wildflowers pushed through the frozen" | | 17 | "They should not have been" | | 18 | "Aurora crouched beside one, keeping" | | 19 | "The petals had a translucent" |
| | ratio | 0.837 | |
| 38.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 129 | | matches | | | ratio | 0.008 | |
| 93.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 3 | | matches | | 0 | "She followed the narrow route she remembered from daylight: past the fallen beech with its split trunk, over a shallow ditch, through a belt of yew that scratch…" | | 1 | "Jennifer Carter had a clear, dry Welsh lilt, the kind that made even a scolding sound neat and practical." | | 2 | "Then huge, each print pressed deep into the earth as though something heavy had crossed the clearing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" | | 2 | "the voice whispered (whisper)" |
| | dialogueSentences | 14 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0.6 | | effectiveRatio | 0.429 | |