| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 161 | | tagDensity | 0.168 | | leniency | 0.335 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2764 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "quickly" | | 1 | "very" | | 2 | "slowly" | | 3 | "carefully" | | 4 | "happily" | | 5 | "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) | |
| 85.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2764 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "gleaming" | | 1 | "warmth" | | 2 | "silence" | | 3 | "racing" | | 4 | "perfect" | | 5 | "traced" | | 6 | "whisper" | | 7 | "vibrated" |
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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 | 1 | | narrationSentences | 224 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 224 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 358 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2762 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 40 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 78 | | wordCount | 1984 | | uniqueNames | 16 | | maxNameDensity | 1.56 | | worstName | "Daniel" | | maxWindowNameDensity | 3 | | worstWindowName | "Daniel" | | discoveredNames | | Wardour | 1 | | Street | 1 | | Aurora | 17 | | Cardiff | 2 | | Daniel | 31 | | Silas | 14 | | Barry | 1 | | London | 2 | | Evan | 2 | | Christmas | 1 | | Cork | 1 | | Victoria | 1 | | Prague | 1 | | Czech | 1 | | Megan | 1 | | Nia | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Daniel" | | 2 | "Silas" | | 3 | "Barry" | | 4 | "Evan" | | 5 | "Victoria" | | 6 | "Megan" | | 7 | "Nia" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Cardiff" | | 3 | "London" | | 4 | "Cork" | | 5 | "Prague" |
| | globalScore | 0.719 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 144 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed embarrassed by the gesture" | | 1 | "as if making an argument" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.362 | | wordCount | 2762 | | matches | | 0 | "not happily, but without blame" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 358 | | matches | | 0 | "learned that fear" | | 1 | "answered that this" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 232 | | mean | 11.91 | | std | 15.25 | | cv | 1.281 | | sampleLengths | | 0 | 22 | | 1 | 28 | | 2 | 53 | | 3 | 26 | | 4 | 11 | | 5 | 56 | | 6 | 3 | | 7 | 7 | | 8 | 5 | | 9 | 8 | | 10 | 13 | | 11 | 3 | | 12 | 5 | | 13 | 83 | | 14 | 6 | | 15 | 6 | | 16 | 33 | | 17 | 46 | | 18 | 4 | | 19 | 1 | | 20 | 9 | | 21 | 20 | | 22 | 2 | | 23 | 12 | | 24 | 9 | | 25 | 39 | | 26 | 16 | | 27 | 4 | | 28 | 5 | | 29 | 5 | | 30 | 18 | | 31 | 4 | | 32 | 8 | | 33 | 3 | | 34 | 12 | | 35 | 8 | | 36 | 78 | | 37 | 39 | | 38 | 4 | | 39 | 1 | | 40 | 1 | | 41 | 3 | | 42 | 11 | | 43 | 21 | | 44 | 11 | | 45 | 9 | | 46 | 6 | | 47 | 4 | | 48 | 4 | | 49 | 2 |
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| 99.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 224 | | matches | | 0 | "being listened" | | 1 | "were buffed" | | 2 | "was tired" | | 3 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 360 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 358 | | ratio | 0.008 | | matches | | 0 | "He had once bitten his fingernails bloody; now they were buffed and square." | | 1 | "It was her instinct now—to be brisk, clever, gone before anyone could close a hand around the vulnerable part." | | 2 | "Whatever he found there made him smile—not happily, but without blame." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1992 | | adjectiveStacks | 1 | | stackExamples | | 0 | "tall, grey-streaked auburn" |
| | adverbCount | 67 | | adverbRatio | 0.033634538152610444 | | lyAdverbCount | 20 | | lyAdverbRatio | 0.010040160642570281 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 358 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 358 | | mean | 7.72 | | std | 6.38 | | cv | 0.826 | | sampleLengths | | 0 | 14 | | 1 | 8 | | 2 | 28 | | 3 | 17 | | 4 | 18 | | 5 | 14 | | 6 | 3 | | 7 | 1 | | 8 | 13 | | 9 | 13 | | 10 | 11 | | 11 | 11 | | 12 | 14 | | 13 | 19 | | 14 | 12 | | 15 | 3 | | 16 | 7 | | 17 | 5 | | 18 | 8 | | 19 | 5 | | 20 | 8 | | 21 | 3 | | 22 | 5 | | 23 | 13 | | 24 | 25 | | 25 | 21 | | 26 | 24 | | 27 | 6 | | 28 | 5 | | 29 | 1 | | 30 | 6 | | 31 | 9 | | 32 | 18 | | 33 | 8 | | 34 | 3 | | 35 | 28 | | 36 | 7 | | 37 | 4 | | 38 | 1 | | 39 | 9 | | 40 | 4 | | 41 | 16 | | 42 | 2 | | 43 | 9 | | 44 | 3 | | 45 | 9 | | 46 | 11 | | 47 | 13 | | 48 | 15 | | 49 | 7 |
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| 44.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.25139664804469275 | | totalSentences | 358 | | uniqueOpeners | 90 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 198 | | matches | | 0 | "Sometimes it wore polished leather" | | 1 | "Then his mouth closed, and" | | 2 | "Perhaps from a song, perhaps" | | 3 | "Perhaps that was why she" | | 4 | "At least he had learned" | | 5 | "Then he went out into" |
| | ratio | 0.03 | |
| 68.48% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 75 | | totalSentences | 198 | | matches | | 0 | "He stood beneath the awning" | | 1 | "He had a narrow face," | | 2 | "She dragged the cloth across" | | 3 | "His reflection hovered over her" | | 4 | "He held a crate of" | | 5 | "She saw a school corridor" | | 6 | "His hand crushing hers at" | | 7 | "He crossed between two taxis," | | 8 | "It was early enough that" | | 9 | "She folded her arms." | | 10 | "His smile came quickly and" | | 11 | "It carried the old boy" | | 12 | "His hazel eyes missed very" | | 13 | "It sounded less like hospitality" | | 14 | "She poured herself soda water" | | 15 | "His caution annoyed her." | | 16 | "His dark hair had thinned" | | 17 | "He had once bitten his" | | 18 | "He glanced down at his" | | 19 | "He had a gift for" |
| | ratio | 0.379 | |
| 33.23% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 169 | | totalSentences | 198 | | matches | | 0 | "The green neon raven above" | | 1 | "Aurora noticed because she had" | | 2 | "He stood beneath the awning" | | 3 | "He had a narrow face," | | 4 | "Every few seconds his gaze" | | 5 | "Silas said behind her" | | 6 | "She dragged the cloth across" | | 7 | "His reflection hovered over her" | | 8 | "He held a crate of" | | 9 | "The old maps on the" | | 10 | "The man looked up again." | | 11 | "This time the light caught" | | 12 | "Aurora stopped moving." | | 13 | "The years slipped their knot." | | 14 | "She saw a school corridor" | | 15 | "A boy with a split" | | 16 | "A railway bridge in Cardiff," | | 17 | "His hand crushing hers at" | | 18 | "The cloth slid from her" | | 19 | "Silas set down the crate." |
| | ratio | 0.854 | |
| 25.25% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 198 | | matches | | | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 84 | | technicalSentenceCount | 1 | | matches | | 0 | "She had Daniel’s solemn brown eyes and one fist raised as if making an argument." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 161 | | tagDensity | 0.118 | | leniency | 0.236 | | rawRatio | 0 | | effectiveRatio | 0 | |