| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 992 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 64.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 992 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "warmth" | | 1 | "familiar" | | 2 | "solitary" | | 3 | "intensity" | | 4 | "methodical" | | 5 | "potential" | | 6 | "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 | 49 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 49 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 996 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 710 | | uniqueNames | 9 | | maxNameDensity | 0.99 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Thursday | 1 | | Aurora | 7 | | Evan | 2 | | Silas | 7 | | James | 7 | | Carter | 1 | | Laila | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Evan" | | 4 | "Silas" | | 5 | "James" | | 6 | "Carter" | | 7 | "Laila" |
| | places | (empty) | | globalScore | 1 | | windowScore | 0.833 | |
| 30.95% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like expensive scotch" | | 1 | "quite hide the way his shoulders slumped" |
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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 | 996 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 73 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 31.13 | | std | 19.35 | | cv | 0.622 | | sampleLengths | | 0 | 98 | | 1 | 58 | | 2 | 1 | | 3 | 78 | | 4 | 37 | | 5 | 36 | | 6 | 21 | | 7 | 30 | | 8 | 21 | | 9 | 1 | | 10 | 10 | | 11 | 37 | | 12 | 25 | | 13 | 31 | | 14 | 37 | | 15 | 22 | | 16 | 25 | | 17 | 24 | | 18 | 39 | | 19 | 24 | | 20 | 5 | | 21 | 26 | | 22 | 27 | | 23 | 33 | | 24 | 46 | | 25 | 18 | | 26 | 34 | | 27 | 33 | | 28 | 47 | | 29 | 22 | | 30 | 17 | | 31 | 33 |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 117 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 73 | | ratio | 0.055 | | matches | | 0 | "The usual Thursday crowd was thin - a couple of suits unwinding after work, a group of art students huddled in the corner booth, and a solitary figure at the bar who made Aurora's heart skip a beat." | | 1 | "But no - not Evan." | | 2 | "No one here called her that - she was Aurora to Silas, Carter to the regulars, Laila to the restaurant customers." | | 3 | "He smiled, and for a moment, she could see both versions of him at once - the carefree student and the careful professional, the friend she'd lost and the man he'd become." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 707 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.028288543140028287 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.011315417256011316 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 13.64 | | std | 9.21 | | cv | 0.675 | | sampleLengths | | 0 | 22 | | 1 | 11 | | 2 | 29 | | 3 | 36 | | 4 | 20 | | 5 | 38 | | 6 | 1 | | 7 | 20 | | 8 | 5 | | 9 | 14 | | 10 | 15 | | 11 | 14 | | 12 | 10 | | 13 | 16 | | 14 | 21 | | 15 | 15 | | 16 | 21 | | 17 | 10 | | 18 | 11 | | 19 | 18 | | 20 | 12 | | 21 | 7 | | 22 | 11 | | 23 | 3 | | 24 | 1 | | 25 | 6 | | 26 | 4 | | 27 | 33 | | 28 | 4 | | 29 | 3 | | 30 | 15 | | 31 | 7 | | 32 | 10 | | 33 | 16 | | 34 | 5 | | 35 | 8 | | 36 | 14 | | 37 | 15 | | 38 | 6 | | 39 | 16 | | 40 | 8 | | 41 | 17 | | 42 | 24 | | 43 | 33 | | 44 | 6 | | 45 | 17 | | 46 | 7 | | 47 | 5 | | 48 | 8 | | 49 | 18 |
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| 90.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5616438356164384 | | totalSentences | 73 | | uniqueOpeners | 41 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 46 | | matches | | 0 | "Maybe it was the way" | | 1 | "Finally, James pulled out his" |
| | ratio | 0.043 | |
| 37.39% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 46 | | matches | | 0 | "She pushed through the heavy" | | 1 | "Her fingers instinctively went to" | | 2 | "It was James, her best" | | 3 | "His voice carried across the" | | 4 | "She gave him a small" | | 5 | "she said, sliding onto the" | | 6 | "He studied her face with" | | 7 | "She accepted the gin and" | | 8 | "He stopped himself, took a" | | 9 | "She held up a hand" | | 10 | "He gestured to Silas for" | | 11 | "He loosened his tie, and" | | 12 | "She watched Silas arrange bottles" | | 13 | "She turned to face him" | | 14 | "His eyebrows shot up, but" | | 15 | "She hadn't thought about her" | | 16 | "He laughed, but it wasn't" | | 17 | "He stared into his scotch" | | 18 | "She watched one of the" | | 19 | "They sat in silence for" |
| | ratio | 0.457 | |
| 46.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 46 | | matches | | 0 | "The neon green sign of" | | 1 | "Aurora paused beneath the awning," | | 2 | "The last delivery of the" | | 3 | "She pushed through the heavy" | | 4 | "The usual Thursday crowd was" | | 5 | "Her fingers instinctively went to" | | 6 | "It was James, her best" | | 7 | "His voice carried across the" | | 8 | "Silas looked up from where" | | 9 | "She gave him a small" | | 10 | "she said, sliding onto the" | | 11 | "He studied her face with" | | 12 | "The words hung between them" | | 13 | "Aurora signaled Silas, who limped" | | 14 | "James watched the exchange with" | | 15 | "She accepted the gin and" | | 16 | "He stopped himself, took a" | | 17 | "Aurora ran a finger through" | | 18 | "The words came out sharper" | | 19 | "James turned to face her" |
| | ratio | 0.826 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "The usual Thursday crowd was thin - a couple of suits unwinding after work, a group of art students huddled in the corner booth, and a solitary figure at the ba…" | | 1 | "Aurora signaled Silas, who limped over with his characteristic unhurried grace." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |