| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 2 | | adverbTags | | 0 | "He nodded almost [almost]" | | 1 | "He nodded too [too]" |
| | dialogueSentences | 43 | | tagDensity | 0.465 | | leniency | 0.93 | | rawRatio | 0.1 | | effectiveRatio | 0.093 | |
| 83.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1205 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "really" | | 2 | "lightly" | | 3 | "quickly" |
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| 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) | |
| 95.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1205 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 66 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 66 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1194 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 906 | | uniqueNames | 14 | | maxNameDensity | 0.77 | | worstName | "Marcus" | | maxWindowNameDensity | 2 | | worstWindowName | "Marcus" | | discoveredNames | | Rory | 3 | | Raven | 1 | | Nest | 1 | | Marcus | 7 | | Hale | 1 | | Cardiff | 2 | | Crwys | 1 | | Road | 1 | | Silas | 3 | | Chinese | 1 | | Eastern | 1 | | Europe | 1 | | Evan | 1 | | Soho | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Marcus" | | 3 | "Hale" | | 4 | "Silas" | | 5 | "Evan" |
| | places | | 0 | "Cardiff" | | 1 | "Crwys" | | 2 | "Road" | | 3 | "Eastern" | | 4 | "Europe" | | 5 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.838 | | wordCount | 1194 | | matches | | 0 | "Not enough to ruin anything, but enough to remember" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 89 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 32.27 | | std | 26.62 | | cv | 0.825 | | sampleLengths | | 0 | 71 | | 1 | 11 | | 2 | 93 | | 3 | 13 | | 4 | 29 | | 5 | 3 | | 6 | 15 | | 7 | 14 | | 8 | 26 | | 9 | 20 | | 10 | 59 | | 11 | 3 | | 12 | 44 | | 13 | 18 | | 14 | 7 | | 15 | 64 | | 16 | 35 | | 17 | 76 | | 18 | 5 | | 19 | 28 | | 20 | 4 | | 21 | 46 | | 22 | 54 | | 23 | 33 | | 24 | 11 | | 25 | 2 | | 26 | 77 | | 27 | 46 | | 28 | 4 | | 29 | 26 | | 30 | 103 | | 31 | 27 | | 32 | 18 | | 33 | 23 | | 34 | 9 | | 35 | 54 | | 36 | 23 |
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| 89.31% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 66 | | matches | | 0 | "being ignored" | | 1 | "got renamed" | | 2 | "been sat" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 166 | | matches | | |
| 14.45% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 89 | | ratio | 0.045 | | matches | | 0 | "There was a moment—she watched it happen—when his face had no idea who she was." | | 1 | "\"I'll send some up.\" He nodded, almost imperceptibly, toward the empty stool beside Marcus, and then he drifted to the far end of the bar to give them the only privacy a place like this could offer—the privacy of being ignored by a professional." | | 2 | "Rory looked at one—somewhere in Eastern Europe, the ink gone sepia—and thought about how everything got renamed eventually." | | 3 | "It surprised her, how much it cost her to hear—the idea that her vanishing had cast a shadow she'd never accounted for, that she'd been so consumed with her own fleeing that she'd never imagined someone standing in the place she'd left, wondering." |
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| 95.71% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 913 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.044906900328587074 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.014238773274917854 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 13.42 | | std | 12.24 | | cv | 0.912 | | sampleLengths | | 0 | 25 | | 1 | 46 | | 2 | 11 | | 3 | 31 | | 4 | 2 | | 5 | 15 | | 6 | 45 | | 7 | 13 | | 8 | 2 | | 9 | 15 | | 10 | 8 | | 11 | 3 | | 12 | 1 | | 13 | 3 | | 14 | 11 | | 15 | 4 | | 16 | 12 | | 17 | 2 | | 18 | 19 | | 19 | 7 | | 20 | 15 | | 21 | 5 | | 22 | 13 | | 23 | 24 | | 24 | 14 | | 25 | 5 | | 26 | 3 | | 27 | 3 | | 28 | 44 | | 29 | 2 | | 30 | 4 | | 31 | 12 | | 32 | 5 | | 33 | 2 | | 34 | 53 | | 35 | 5 | | 36 | 6 | | 37 | 13 | | 38 | 22 | | 39 | 7 | | 40 | 8 | | 41 | 25 | | 42 | 21 | | 43 | 6 | | 44 | 9 | | 45 | 5 | | 46 | 4 | | 47 | 8 | | 48 | 4 | | 49 | 12 |
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| 65.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.449438202247191 | | totalSentences | 89 | | uniqueOpeners | 40 | |
| 65.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 51 | | matches | | 0 | "Then it arrived, recognition rolling" |
| | ratio | 0.02 | |
| 0.39% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 51 | | matches | | 0 | "She'd come down for ice," | | 1 | "She did not expect to" | | 2 | "He sat hunched over a" | | 3 | "She counted them the way" | | 4 | "she said, before she could" | | 5 | "He stood, knocked his knee" | | 6 | "She touched it without meaning" | | 7 | "He was still looking at" | | 8 | "She lifted the ice bucket" | | 9 | "He had a way of" | | 10 | "He nodded, almost imperceptibly, toward" | | 11 | "She didn't decide to." | | 12 | "Her body did it for" | | 13 | "She wasn't sure settled was" | | 14 | "He turned the tumbler in" | | 15 | "She could believe it and" | | 16 | "He'd had a novel he" | | 17 | "He used to read passages" | | 18 | "She'd loved him a little," | | 19 | "She thought of all the" |
| | ratio | 0.549 | |
| 58.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 51 | | matches | | 0 | "The green neon hummed its" | | 1 | "She'd come down for ice," | | 2 | "She did not expect to" | | 3 | "He sat hunched over a" | | 4 | "She counted them the way" | | 5 | "she said, before she could" | | 6 | "He stood, knocked his knee" | | 7 | "She touched it without meaning" | | 8 | "He was still looking at" | | 9 | "She lifted the ice bucket" | | 10 | "Silas, behind the counter polishing" | | 11 | "He had a way of" | | 12 | "The silver signet ring caught" | | 13 | "He nodded, almost imperceptibly, toward" | | 14 | "She didn't decide to." | | 15 | "Her body did it for" | | 16 | "She wasn't sure settled was" | | 17 | "He turned the tumbler in" | | 18 | "She could believe it and" | | 19 | "The Marcus she'd known had" |
| | ratio | 0.804 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "He sat hunched over a tumbler of something amber, his shoulders broader than she remembered, his hairline retreating in a way that made him look both older and …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 43 | | tagDensity | 0.209 | | leniency | 0.419 | | rawRatio | 0.111 | | effectiveRatio | 0.047 | |