| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 54 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.91% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1146 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "quickly" | | 1 | "very" | | 2 | "really" |
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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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1146 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 46 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 46 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 88 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1146 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 734 | | uniqueNames | 10 | | maxNameDensity | 2.18 | | worstName | "Ffion" | | maxWindowNameDensity | 4 | | worstWindowName | "Ffion" | | discoveredNames | | Rory | 11 | | Raven | 1 | | Nest | 1 | | Miles | 1 | | Davis | 1 | | London | 1 | | Pryce | 1 | | Ffion | 16 | | Silas | 4 | | Malphora | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Davis" | | 3 | "Pryce" | | 4 | "Ffion" | | 5 | "Silas" |
| | places | | | globalScore | 0.41 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | 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 | 1146 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 88 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 19.1 | | std | 20.71 | | cv | 1.084 | | sampleLengths | | 0 | 18 | | 1 | 8 | | 2 | 47 | | 3 | 1 | | 4 | 85 | | 5 | 2 | | 6 | 21 | | 7 | 3 | | 8 | 30 | | 9 | 19 | | 10 | 2 | | 11 | 35 | | 12 | 4 | | 13 | 28 | | 14 | 4 | | 15 | 77 | | 16 | 7 | | 17 | 1 | | 18 | 1 | | 19 | 7 | | 20 | 4 | | 21 | 1 | | 22 | 50 | | 23 | 6 | | 24 | 33 | | 25 | 4 | | 26 | 1 | | 27 | 7 | | 28 | 32 | | 29 | 5 | | 30 | 21 | | 31 | 2 | | 32 | 28 | | 33 | 2 | | 34 | 6 | | 35 | 15 | | 36 | 11 | | 37 | 31 | | 38 | 5 | | 39 | 22 | | 40 | 62 | | 41 | 31 | | 42 | 4 | | 43 | 11 | | 44 | 68 | | 45 | 1 | | 46 | 5 | | 47 | 6 | | 48 | 22 | | 49 | 45 |
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| 97.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 46 | | matches | | |
| 98.22% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 131 | | matches | | 0 | "was testing" | | 1 | "was already coming" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 88 | | ratio | 0 | | matches | (empty) | |
| 93.22% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 712 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.047752808988764044 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.011235955056179775 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 88 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 88 | | mean | 13.02 | | std | 10.99 | | cv | 0.844 | | sampleLengths | | 0 | 18 | | 1 | 8 | | 2 | 25 | | 3 | 16 | | 4 | 6 | | 5 | 1 | | 6 | 23 | | 7 | 15 | | 8 | 6 | | 9 | 32 | | 10 | 9 | | 11 | 2 | | 12 | 20 | | 13 | 1 | | 14 | 3 | | 15 | 6 | | 16 | 24 | | 17 | 19 | | 18 | 2 | | 19 | 12 | | 20 | 23 | | 21 | 4 | | 22 | 16 | | 23 | 12 | | 24 | 4 | | 25 | 23 | | 26 | 38 | | 27 | 16 | | 28 | 7 | | 29 | 1 | | 30 | 1 | | 31 | 3 | | 32 | 4 | | 33 | 4 | | 34 | 1 | | 35 | 13 | | 36 | 29 | | 37 | 8 | | 38 | 6 | | 39 | 12 | | 40 | 21 | | 41 | 4 | | 42 | 1 | | 43 | 7 | | 44 | 13 | | 45 | 19 | | 46 | 5 | | 47 | 8 | | 48 | 13 | | 49 | 2 |
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| 65.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.45454545454545453 | | totalSentences | 88 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 45 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 45 | | matches | | 0 | "She didn't turn round straight" | | 1 | "She made air quotes with" | | 2 | "He caught the shift in" | | 3 | "He nodded once and drifted" | | 4 | "She shook her head" | | 5 | "She tugged it back down" | | 6 | "She thought, too, of things" | | 7 | "She stopped, tried again" | | 8 | "She didn't need to check" | | 9 | "She was already coming round" | | 10 | "She didn't look back at" | | 11 | "She couldn't afford to." |
| | ratio | 0.267 | |
| 26.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 45 | | matches | | 0 | "The glass slipped in Rory's" | | 1 | "The voice cut through the" | | 2 | "Rory set the glass down" | | 3 | "She didn't turn round straight" | | 4 | "Ffion Pryce dropped onto the" | | 5 | "She made air quotes with" | | 6 | "Ffion's eyes tracked over her," | | 7 | "Ffion frowned, searching" | | 8 | "Rory picked up a rag" | | 9 | "Ffion laughed, but it didn't" | | 10 | "Ffion repeated, like she was" | | 11 | "Silas emerged from the back" | | 12 | "He caught the shift in" | | 13 | "He nodded once and drifted" | | 14 | "Ffion watched him go, then" | | 15 | "The rag stopped moving." | | 16 | "Ffion said, too quickly, like" | | 17 | "She shook her head" | | 18 | "Ffion's jaw tightened" | | 19 | "The admission sat between them," |
| | ratio | 0.867 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 45 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 5 | | matches | | 0 | "Silas emerged from the back room, ducking beneath the low doorframe with the ease of a man who'd done it ten thousand times." | | 1 | "Rory thought of the wig she'd never worn, the pupillage she'd never started, the version of herself that existed only in old photographs and her mother's hopefu…" | | 2 | "Down the bar, Silas had already straightened, one hand resting near the hidden latch behind the gin bottles, eyes fixed on her with a question that wasn't reall…" | | 3 | "She was already coming round the bar, already shrugging off the rag apron, already feeling the old version of herself, the law student, the crier, the girl who …" | | 4 | "Not with Silas already pulling the bookshelf wide, not with the green glow of the neon sign outside flickering once, twice, in a pattern that meant trouble was …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 3 | | fancyTags | | 0 | "the Miles Davis record hissing (hiss)" | | 1 | "Ffion laughed (laugh)" | | 2 | "Ffion repeated (repeat)" |
| | dialogueSentences | 54 | | tagDensity | 0.074 | | leniency | 0.148 | | rawRatio | 0.75 | | effectiveRatio | 0.111 | |