| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1757 | | 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) | |
| 65.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1757 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "absolutely" | | 1 | "pulse" | | 2 | "warmth" | | 3 | "perfect" | | 4 | "fluttered" | | 5 | "standard" | | 6 | "silence" | | 7 | "could feel" | | 8 | "pulsed" |
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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 | 223 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 223 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 230 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1757 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.03% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1740 | | uniqueNames | 13 | | maxNameDensity | 1.38 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Heartstone | 2 | | Kingston | 1 | | November | 1 | | Golden | 1 | | Empress | 1 | | Silas | 2 | | Aurora | 24 | | Grove | 1 | | English | 1 | | Welsh | 1 | | Plink | 3 |
| | persons | | 0 | "Heartstone" | | 1 | "Empress" | | 2 | "Silas" | | 3 | "Aurora" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Kingston" | | 3 | "November" | | 4 | "Golden" |
| | globalScore | 0.81 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 128 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared beneath the door of her flat after dusk, dry despite the rain that had lashed the windows for most of the evening" |
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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 | 1757 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 230 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 138 | | mean | 12.73 | | std | 13.66 | | cv | 1.073 | | sampleLengths | | 0 | 15 | | 1 | 41 | | 2 | 10 | | 3 | 7 | | 4 | 1 | | 5 | 2 | | 6 | 42 | | 7 | 14 | | 8 | 6 | | 9 | 6 | | 10 | 21 | | 11 | 5 | | 12 | 41 | | 13 | 3 | | 14 | 23 | | 15 | 7 | | 16 | 2 | | 17 | 6 | | 18 | 76 | | 19 | 6 | | 20 | 16 | | 21 | 11 | | 22 | 31 | | 23 | 4 | | 24 | 2 | | 25 | 50 | | 26 | 10 | | 27 | 21 | | 28 | 5 | | 29 | 6 | | 30 | 45 | | 31 | 6 | | 32 | 3 | | 33 | 16 | | 34 | 3 | | 35 | 7 | | 36 | 54 | | 37 | 7 | | 38 | 9 | | 39 | 5 | | 40 | 26 | | 41 | 1 | | 42 | 1 | | 43 | 6 | | 44 | 6 | | 45 | 38 | | 46 | 6 | | 47 | 4 | | 48 | 13 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 223 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 264 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 230 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1745 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 57 | | adverbRatio | 0.032664756446991405 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0040114613180515755 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 230 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 230 | | mean | 7.64 | | std | 5.71 | | cv | 0.747 | | sampleLengths | | 0 | 15 | | 1 | 10 | | 2 | 14 | | 3 | 17 | | 4 | 10 | | 5 | 7 | | 6 | 1 | | 7 | 2 | | 8 | 2 | | 9 | 2 | | 10 | 26 | | 11 | 12 | | 12 | 14 | | 13 | 3 | | 14 | 3 | | 15 | 6 | | 16 | 11 | | 17 | 7 | | 18 | 1 | | 19 | 1 | | 20 | 1 | | 21 | 5 | | 22 | 16 | | 23 | 9 | | 24 | 16 | | 25 | 3 | | 26 | 7 | | 27 | 2 | | 28 | 1 | | 29 | 13 | | 30 | 7 | | 31 | 2 | | 32 | 6 | | 33 | 22 | | 34 | 9 | | 35 | 22 | | 36 | 23 | | 37 | 6 | | 38 | 16 | | 39 | 11 | | 40 | 10 | | 41 | 8 | | 42 | 13 | | 43 | 4 | | 44 | 2 | | 45 | 10 | | 46 | 8 | | 47 | 8 | | 48 | 12 | | 49 | 12 |
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| 43.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2608695652173913 | | totalSentences | 230 | | uniqueOpeners | 60 | |
| 88.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 188 | | matches | | 0 | "At least, that was what" | | 1 | "Then he had walked away" | | 2 | "Just a gap between stones." | | 3 | "Then a voice whispered in" | | 4 | "Then, beneath the loose earth," |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 188 | | matches | | 0 | "She checked the folded note" | | 1 | "Her breath clouded before her" | | 2 | "It struck the path in" | | 3 | "She stepped through the gate." | | 4 | "It had done nothing on" | | 5 | "She had only found the" | | 6 | "She had followed a narrow" | | 7 | "She had stepped over the" | | 8 | "Her manager at Golden Empress" | | 9 | "She had not returned." | | 10 | "She pushed them back and" | | 11 | "She found the oak." | | 12 | "Its trunk rose from the" | | 13 | "Her thumb hovered over the" | | 14 | "She could turn the light" | | 15 | "She swung the torch towards" | | 16 | "Its thin tip dragged back" | | 17 | "She saw it in the" | | 18 | "It stood between two oaks," | | 19 | "She aimed the light at" |
| | ratio | 0.277 | |
| 55.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 152 | | totalSentences | 188 | | matches | | 0 | "The gate on the Richmond" | | 1 | "Aurora stopped beneath it, her" | | 2 | "The road behind her carried" | | 3 | "She checked the folded note" | | 4 | "The paper had appeared beneath" | | 5 | "The handwriting pressed deep into" | | 6 | "Aurora folded it along the" | | 7 | "Her breath clouded before her" | | 8 | "The rain had stopped, but" | | 9 | "It struck the path in" | | 10 | "She stepped through the gate." | | 11 | "The Heartstone lay under her" | | 12 | "It had done nothing on" | | 13 | "Nothing as she had crossed" | | 14 | "A small pulse pressed against" | | 15 | "The sort of warmth that" | | 16 | "Aurora caught the pendant through" | | 17 | "The warmth answered with another" | | 18 | "She had only found the" | | 19 | "She had followed a narrow" |
| | ratio | 0.809 | |
| 26.60% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 188 | | matches | | | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 1 | | matches | | 0 | "The sort of standard vibration that belonged on a crowded train or beneath a table in a pub, not here." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 10 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 1 | | effectiveRatio | 0.6 | |