| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.421 | | leniency | 0.842 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 70.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 850 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "softly" | | 1 | "suddenly" | | 2 | "nervously" | | 3 | "very" |
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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) | |
| 70.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 850 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pounding" | | 1 | "stomach" | | 2 | "familiar" | | 3 | "dance" | | 4 | "comfortable" |
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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 | 37 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 37 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 47 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 845 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.71% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 553 | | uniqueNames | 12 | | maxNameDensity | 1.27 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 1 | | Silas | 5 | | Rory | 7 | | Hollywood | 1 | | Eva | 2 | | Dark | 1 | | Brummie | 1 | | Johnny | 3 | | Christmas | 1 | | Guinness | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Silas" | | 2 | "Rory" | | 3 | "Eva" | | 4 | "Brummie" | | 5 | "Johnny" |
| | places | | | globalScore | 0.867 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 845 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 47 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 38.41 | | std | 23.13 | | cv | 0.602 | | sampleLengths | | 0 | 22 | | 1 | 20 | | 2 | 20 | | 3 | 67 | | 4 | 41 | | 5 | 49 | | 6 | 48 | | 7 | 42 | | 8 | 41 | | 9 | 13 | | 10 | 1 | | 11 | 9 | | 12 | 63 | | 13 | 69 | | 14 | 39 | | 15 | 40 | | 16 | 41 | | 17 | 76 | | 18 | 10 | | 19 | 89 | | 20 | 26 | | 21 | 19 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 37 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 87 | | matches | (empty) | |
| 82.07% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 47 | | ratio | 0.021 | | matches | | 0 | "She knew well the cloak and dagger group Silas had gathered around him, knew they met in secret within that hidden back room Silas kept covered with one of his grandfather's musty old bookshelves—just like a glitzy Hollywood caper." |
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| 96.43% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 431 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.04408352668213457 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.013921113689095127 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 47 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 47 | | mean | 17.98 | | std | 9.6 | | cv | 0.534 | | sampleLengths | | 0 | 12 | | 1 | 10 | | 2 | 12 | | 3 | 8 | | 4 | 20 | | 5 | 8 | | 6 | 27 | | 7 | 11 | | 8 | 21 | | 9 | 33 | | 10 | 8 | | 11 | 14 | | 12 | 35 | | 13 | 19 | | 14 | 29 | | 15 | 42 | | 16 | 30 | | 17 | 11 | | 18 | 13 | | 19 | 1 | | 20 | 9 | | 21 | 24 | | 22 | 31 | | 23 | 8 | | 24 | 39 | | 25 | 9 | | 26 | 21 | | 27 | 22 | | 28 | 17 | | 29 | 22 | | 30 | 13 | | 31 | 5 | | 32 | 27 | | 33 | 14 | | 34 | 23 | | 35 | 7 | | 36 | 18 | | 37 | 18 | | 38 | 10 | | 39 | 10 | | 40 | 33 | | 41 | 20 | | 42 | 9 | | 43 | 7 | | 44 | 20 | | 45 | 26 | | 46 | 19 |
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| 97.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.6595744680851063 | | totalSentences | 47 | | uniqueOpeners | 31 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 36 | | matches | | 0 | "Quite the jolly retirement gig" | | 1 | "Once they were seated, his" | | 2 | "Just a bit more worn." |
| | ratio | 0.083 | |
| 75.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 36 | | matches | | 0 | "He rose from the booth" | | 1 | "he greeted her with a" | | 2 | "she said, her voice small" | | 3 | "he agreed, wincing as he" | | 4 | "She smiled softly, studying the" | | 5 | "She knew well the cloak" | | 6 | "He listened to gossip, secrets," | | 7 | "They looked a little worse" | | 8 | "She unceremoniously poured herself a" | | 9 | "He never had been the" | | 10 | "He'd always been partial to" | | 11 | "He seemed to be much" | | 12 | "He took a deep swig" |
| | ratio | 0.361 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 27 | | totalSentences | 36 | | matches | | 0 | "Rory stepped into The Raven's" | | 1 | "The dimly light soaked into" | | 2 | "Aurora slipped off her backpack," | | 3 | "The man who'd spoken had" | | 4 | "He rose from the booth" | | 5 | "he greeted her with a" | | 6 | "she said, her voice small" | | 7 | "he agreed, wincing as he" | | 8 | "Rory's stomach felt queasy, her" | | 9 | "She smiled softly, studying the" | | 10 | "Rory threw up her hands" | | 11 | "She knew well the cloak" | | 12 | "He listened to gossip, secrets," | | 13 | "They looked a little worse" | | 14 | "Here, was Eva," | | 15 | "Dutra followed, her usual smirk" | | 16 | "She unceremoniously poured herself a" | | 17 | "Johnny asked, his accent positively" | | 18 | "He never had been the" | | 19 | "Rory recalled a decade ago," |
| | ratio | 0.75 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 36 | | matches | | 0 | "Now, he shuffled in looking" |
| | ratio | 0.028 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 3 | | matches | | 0 | "Johnny asked, his accent positively drenched in thick, syrupy Brummie tones" | | 1 | "Rory asked, nervously" | | 2 | "Silas said, his gaze dropping to his pint" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "he agreed (agree)" | | 1 | "Eva joked (joke)" | | 2 | "Johnny retorted (retort)" |
| | dialogueSentences | 19 | | tagDensity | 0.368 | | leniency | 0.737 | | rawRatio | 0.429 | | effectiveRatio | 0.316 | |