| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Marcus turned back [back]" |
| | dialogueSentences | 76 | | tagDensity | 0.184 | | leniency | 0.368 | | rawRatio | 0.071 | | effectiveRatio | 0.026 | |
| 93.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1646 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 66.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1646 | | totalAiIsms | 11 | | found | | 0 | | | 1 | | | 2 | | word | "practiced ease" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | |
| | highlights | | 0 | "comfortable" | | 1 | "footsteps" | | 2 | "practiced ease" | | 3 | "traced" | | 4 | "flicker" | | 5 | "facade" | | 6 | "weight" | | 7 | "silence" | | 8 | "unreadable" |
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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 | 90 | | matches | (empty) | |
| 79.37% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 150 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 80 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1638 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.32% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 950 | | uniqueNames | 16 | | maxNameDensity | 1.47 | | worstName | "Marcus" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Marcus" | | discoveredNames | | Rory | 7 | | Silas | 7 | | Raven | 2 | | Nest | 2 | | November | 1 | | Webb | 2 | | Marcus | 14 | | Kierkegaard | 1 | | Welsh | 1 | | Tuesday | 1 | | Soho | 1 | | Laphroaig | 1 | | London | 1 | | Tom | 3 | | Waits | 3 | | Bitter | 1 |
| | persons | | 0 | "Rory" | | 1 | "Silas" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Webb" | | 5 | "Marcus" | | 6 | "Laphroaig" | | 7 | "Tom" | | 8 | "Waits" | | 9 | "Bitter" |
| | places | | | globalScore | 0.763 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 1638 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 150 | | matches | | 0 | "learned that space" | | 1 | "cultivated that atmosphere" | | 2 | "learned that much" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 87 | | mean | 18.83 | | std | 21.43 | | cv | 1.138 | | sampleLengths | | 0 | 6 | | 1 | 64 | | 2 | 106 | | 3 | 39 | | 4 | 4 | | 5 | 3 | | 6 | 55 | | 7 | 1 | | 8 | 46 | | 9 | 22 | | 10 | 7 | | 11 | 30 | | 12 | 2 | | 13 | 1 | | 14 | 32 | | 15 | 3 | | 16 | 23 | | 17 | 31 | | 18 | 1 | | 19 | 11 | | 20 | 1 | | 21 | 24 | | 22 | 9 | | 23 | 12 | | 24 | 33 | | 25 | 17 | | 26 | 4 | | 27 | 1 | | 28 | 28 | | 29 | 6 | | 30 | 2 | | 31 | 2 | | 32 | 35 | | 33 | 5 | | 34 | 21 | | 35 | 17 | | 36 | 5 | | 37 | 21 | | 38 | 4 | | 39 | 53 | | 40 | 5 | | 41 | 1 | | 42 | 34 | | 43 | 35 | | 44 | 4 | | 45 | 5 | | 46 | 33 | | 47 | 1 | | 48 | 5 | | 49 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 90 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 157 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 150 | | ratio | 0 | | matches | (empty) | |
| 92.35% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 955 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 40 | | adverbRatio | 0.041884816753926704 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008376963350785341 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 150 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 150 | | mean | 10.92 | | std | 11.41 | | cv | 1.045 | | sampleLengths | | 0 | 6 | | 1 | 31 | | 2 | 3 | | 3 | 30 | | 4 | 2 | | 5 | 7 | | 6 | 25 | | 7 | 17 | | 8 | 13 | | 9 | 20 | | 10 | 22 | | 11 | 5 | | 12 | 18 | | 13 | 16 | | 14 | 4 | | 15 | 3 | | 16 | 2 | | 17 | 53 | | 18 | 1 | | 19 | 7 | | 20 | 23 | | 21 | 6 | | 22 | 10 | | 23 | 12 | | 24 | 10 | | 25 | 7 | | 26 | 10 | | 27 | 8 | | 28 | 12 | | 29 | 2 | | 30 | 1 | | 31 | 18 | | 32 | 14 | | 33 | 3 | | 34 | 15 | | 35 | 8 | | 36 | 4 | | 37 | 27 | | 38 | 1 | | 39 | 11 | | 40 | 1 | | 41 | 9 | | 42 | 15 | | 43 | 9 | | 44 | 5 | | 45 | 7 | | 46 | 11 | | 47 | 4 | | 48 | 18 | | 49 | 6 |
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| 72.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.47333333333333333 | | totalSentences | 150 | | uniqueOpeners | 71 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 84 | | matches | | 0 | "It didn't shatter, just bounced" | | 1 | "She barely noticed." | | 2 | "Her eyes had locked onto" | | 3 | "His hair, once a perpetual" | | 4 | "He moved differently too, shoulders" | | 5 | "He hadn't seen her yet." | | 6 | "She could still disappear into" | | 7 | "She could claim a migraine," | | 8 | "His eyes found hers." | | 9 | "He crossed the room in" | | 10 | "His smile didn't reach his" | | 11 | "He set down a fresh" | | 12 | "Her hands were steady." | | 13 | "She'd learned that much in" | | 14 | "He looked up, and for" | | 15 | "It was enough." | | 16 | "It pressed down on them" | | 17 | "He gestured at his suit," | | 18 | "He laughed, and it was" | | 19 | "He held up a hand" |
| | ratio | 0.333 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 84 | | matches | | 0 | "The glass slipped from Rory's" | | 1 | "It didn't shatter, just bounced" | | 2 | "She barely noticed." | | 3 | "Her eyes had locked onto" | | 4 | "That Marcus had worn charity" | | 5 | "This man wore a charcoal" | | 6 | "His hair, once a perpetual" | | 7 | "He moved differently too, shoulders" | | 8 | "He hadn't seen her yet." | | 9 | "She could still disappear into" | | 10 | "She could claim a migraine," | | 11 | "His eyes found hers." | | 12 | "He crossed the room in" | | 13 | "The Raven's Nest wasn't crowded," | | 14 | "Silas had cultivated that atmosphere" | | 15 | "People came here to be" | | 16 | "Marcus settled onto a barstool," | | 17 | "His smile didn't reach his" | | 18 | "Nothing about him seemed to" | | 19 | "Silas appeared at her elbow," |
| | ratio | 0.857 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 4 | | matches | | 0 | "Her eyes had locked onto the figure pushing through the heavy oak door of The Raven's Nest, bringing with him a gust of November cold and something far more uns…" | | 1 | "Not the gangly philosophy student who'd once argued with her until three in the morning about Kierkegaard and whether free will was a comfortable lie." | | 2 | "Silas took it, held it a beat too long, studying the younger man with the quiet assessment of someone who'd spent decades reading people for a living." | | 3 | "A dawning horror that settled into something cold and hard." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "Marcus settled, his movements precise and controlled" | | 1 | "He gestured, his haircut, the polished veneer of professional success" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 76 | | tagDensity | 0.013 | | leniency | 0.026 | | rawRatio | 1 | | effectiveRatio | 0.026 | |