| 29.79% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 4 | | adverbTags | | 0 | "The woman stopped just [just]" | | 1 | "she said finally [finally]" | | 2 | "Meg nodded slowly [slowly]" | | 3 | "she said eventually [eventually]" |
| | dialogueSentences | 47 | | tagDensity | 0.383 | | leniency | 0.766 | | rawRatio | 0.222 | | effectiveRatio | 0.17 | |
| 85.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1050 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 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) | |
| 80.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1050 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "silence" | | 1 | "familiar" | | 2 | "comforting" | | 3 | "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 | 1 | | narrationSentences | 40 | | matches | | |
| 71.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 40 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1046 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 26.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 650 | | uniqueNames | 5 | | maxNameDensity | 2.46 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Meg" | | discoveredNames | | October | 1 | | Rory | 16 | | Prosser | 1 | | Meg | 16 | | Silas | 1 |
| | persons | | 0 | "Rory" | | 1 | "Prosser" | | 2 | "Meg" | | 3 | "Silas" |
| | places | (empty) | | globalScore | 0.269 | | windowScore | 0.333 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | glossingSentenceCount | 2 | | matches | | 0 | "Older now, obviously, but it was the sti" | | 1 | "not quite a smile" |
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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 | 1046 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 68 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 25.51 | | std | 17.36 | | cv | 0.68 | | sampleLengths | | 0 | 57 | | 1 | 33 | | 2 | 48 | | 3 | 22 | | 4 | 28 | | 5 | 10 | | 6 | 38 | | 7 | 30 | | 8 | 2 | | 9 | 53 | | 10 | 9 | | 11 | 4 | | 12 | 39 | | 13 | 43 | | 14 | 6 | | 15 | 36 | | 16 | 69 | | 17 | 5 | | 18 | 13 | | 19 | 2 | | 20 | 26 | | 21 | 47 | | 22 | 12 | | 23 | 20 | | 24 | 10 | | 25 | 32 | | 26 | 18 | | 27 | 14 | | 28 | 25 | | 29 | 49 | | 30 | 17 | | 31 | 4 | | 32 | 51 | | 33 | 37 | | 34 | 27 | | 35 | 30 | | 36 | 7 | | 37 | 40 | | 38 | 6 | | 39 | 19 | | 40 | 8 |
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| 87.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 40 | | matches | | 0 | "was owed" | | 1 | "being asked" |
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| 80.95% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 112 | | matches | | 0 | "were recalculating" | | 1 | "was buying" |
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| 16.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 68 | | ratio | 0.044 | | matches | | 0 | "Older now, obviously, but it was the stillness that threw her—Meg had never been still in her life." | | 1 | "Rory poured two whiskies without being asked, because some things you remembered even after six years—Meg always drank whisky, neat, like it was a personality trait rather than a preference." | | 2 | "Meg's face did something complicated—concern warring with the careful blankness she'd probably practiced in a hundred client meetings." |
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| 85.63% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 653 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.05359877488514548 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.022970903522205207 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 15.38 | | std | 11.41 | | cv | 0.742 | | sampleLengths | | 0 | 20 | | 1 | 37 | | 2 | 28 | | 3 | 5 | | 4 | 7 | | 5 | 2 | | 6 | 18 | | 7 | 21 | | 8 | 14 | | 9 | 8 | | 10 | 20 | | 11 | 8 | | 12 | 10 | | 13 | 18 | | 14 | 20 | | 15 | 30 | | 16 | 2 | | 17 | 8 | | 18 | 45 | | 19 | 9 | | 20 | 4 | | 21 | 27 | | 22 | 12 | | 23 | 21 | | 24 | 22 | | 25 | 6 | | 26 | 8 | | 27 | 28 | | 28 | 42 | | 29 | 22 | | 30 | 3 | | 31 | 2 | | 32 | 5 | | 33 | 9 | | 34 | 4 | | 35 | 2 | | 36 | 26 | | 37 | 13 | | 38 | 34 | | 39 | 6 | | 40 | 6 | | 41 | 18 | | 42 | 2 | | 43 | 10 | | 44 | 23 | | 45 | 9 | | 46 | 18 | | 47 | 7 | | 48 | 7 | | 49 | 25 |
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| 82.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.5735294117647058 | | totalSentences | 68 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 36 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 36 | | matches | | 0 | "She'd been the girl who" | | 1 | "Her nails were done, Rory" | | 2 | "She didn't finish the sentence," | | 3 | "she said finally" | | 4 | "It came out sharper than" | | 5 | "she said eventually" | | 6 | "They sat with that for" |
| | ratio | 0.194 | |
| 1.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 33 | | totalSentences | 36 | | matches | | 0 | "The neon sign buzzed green" | | 1 | "Rory wiped down the counter" | | 2 | "The woman stopped just inside" | | 3 | "Rory's hand stilled on the" | | 4 | "She'd been the girl who" | | 5 | "Rory came around the bar," | | 6 | "Meg unbuttoned her coat slowly," | | 7 | "Meg took a stool, set" | | 8 | "Rory poured two whiskies without" | | 9 | "Meg turned the glass without" | | 10 | "Her nails were done, Rory" | | 11 | "Rory leaned against the bar," | | 12 | "Meg said it flat, no" | | 13 | "Meg finally drank, wincing slightly" | | 14 | "She didn't finish the sentence," | | 15 | "Rory studied her old friend's" | | 16 | "That girl's eyes were still" | | 17 | "Lawyer's eyes now." | | 18 | "Meg's gaze swept over her," | | 19 | "Rory picked up a glass" |
| | ratio | 0.917 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 36 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 2 | | matches | | 0 | "Older now, obviously, but it was the stillness that threw her—Meg had never been still in her life." | | 1 | "Rory studied her old friend's face, trying to find the girl who'd once climbed through her dorm window at 2am with a bottle of cheap cider because they'd had a …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.149 | | leniency | 0.298 | | rawRatio | 0 | | effectiveRatio | 0 | |