| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 33 | | adverbTagCount | 3 | | adverbTags | | 0 | "the old laugh appeared then [then]" | | 1 | "Eva’s fingers tightened around [around]" | | 2 | "Eva’s answer came quickly [quickly]" |
| | dialogueSentences | 123 | | tagDensity | 0.268 | | leniency | 0.537 | | rawRatio | 0.091 | | effectiveRatio | 0.049 | |
| 95.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2038 | | totalAiIsmAdverbs | 2 | | 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) | |
| 95.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2038 | | totalAiIsms | 2 | | 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 | 1 | | narrationSentences | 155 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 155 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 244 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2037 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 41 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 87 | | wordCount | 1447 | | uniqueNames | 8 | | maxNameDensity | 2.9 | | worstName | "Rory" | | maxWindowNameDensity | 5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Eva | 33 | | Rory | 42 | | Silas | 7 | | London | 1 | | Evan | 1 | | You | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Silas" | | 5 | "Evan" | | 6 | "You" |
| | places | | | globalScore | 0.049 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.982 | | wordCount | 2037 | | matches | | 0 | "not the girl Rory remembered, but someone who understood" | | 1 | "not as a road but as a house with doors shut all along it" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 244 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 148 | | mean | 13.76 | | std | 14.5 | | cv | 1.053 | | sampleLengths | | 0 | 18 | | 1 | 79 | | 2 | 10 | | 3 | 21 | | 4 | 6 | | 5 | 55 | | 6 | 3 | | 7 | 4 | | 8 | 27 | | 9 | 1 | | 10 | 60 | | 11 | 41 | | 12 | 8 | | 13 | 20 | | 14 | 2 | | 15 | 22 | | 16 | 12 | | 17 | 8 | | 18 | 4 | | 19 | 16 | | 20 | 43 | | 21 | 5 | | 22 | 4 | | 23 | 12 | | 24 | 12 | | 25 | 3 | | 26 | 4 | | 27 | 2 | | 28 | 4 | | 29 | 3 | | 30 | 16 | | 31 | 52 | | 32 | 5 | | 33 | 15 | | 34 | 2 | | 35 | 4 | | 36 | 6 | | 37 | 12 | | 38 | 38 | | 39 | 7 | | 40 | 28 | | 41 | 4 | | 42 | 3 | | 43 | 5 | | 44 | 44 | | 45 | 13 | | 46 | 2 | | 47 | 3 | | 48 | 9 | | 49 | 28 |
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| 98.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 155 | | matches | | 0 | "been gone" | | 1 | "been asked" | | 2 | "been given" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 282 | | matches | | 0 | "was holding" | | 1 | "was looking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 244 | | ratio | 0.008 | | matches | | 0 | "His auburn hair was grey at the temples; his neatly trimmed beard caught the amber light." | | 1 | "Rory remembered those hands with chipped purple nail polish, always moving—plucking grass, turning a pen, stealing chips from someone else’s plate." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1453 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 48 | | adverbRatio | 0.03303509979353063 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004817618719889883 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 244 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 244 | | mean | 8.35 | | std | 6.56 | | cv | 0.786 | | sampleLengths | | 0 | 18 | | 1 | 5 | | 2 | 36 | | 3 | 15 | | 4 | 23 | | 5 | 10 | | 6 | 21 | | 7 | 6 | | 8 | 6 | | 9 | 26 | | 10 | 23 | | 11 | 3 | | 12 | 4 | | 13 | 7 | | 14 | 20 | | 15 | 1 | | 16 | 6 | | 17 | 24 | | 18 | 30 | | 19 | 20 | | 20 | 21 | | 21 | 8 | | 22 | 16 | | 23 | 4 | | 24 | 2 | | 25 | 13 | | 26 | 9 | | 27 | 9 | | 28 | 3 | | 29 | 8 | | 30 | 4 | | 31 | 9 | | 32 | 7 | | 33 | 12 | | 34 | 16 | | 35 | 15 | | 36 | 5 | | 37 | 4 | | 38 | 12 | | 39 | 7 | | 40 | 5 | | 41 | 3 | | 42 | 3 | | 43 | 1 | | 44 | 2 | | 45 | 4 | | 46 | 3 | | 47 | 13 | | 48 | 3 | | 49 | 5 |
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| 40.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.2540983606557377 | | totalSentences | 244 | | uniqueOpeners | 62 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 133 | | matches | | 0 | "Then Eva smiled, but carefully," | | 1 | "Just there, like a stone" | | 2 | "Somewhere behind them, a man" | | 3 | "Just the bare fact of" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 133 | | matches | | 0 | "Her delivery bag was still" | | 1 | "She was halfway to the" | | 2 | "It was not her face," | | 3 | "It was the way she" | | 4 | "She had cut her hair" | | 5 | "It was darker than Rory" | | 6 | "Her coat was good wool," | | 7 | "His auburn hair was grey" | | 8 | "He glanced from Eva to" | | 9 | "Her hands were bare." | | 10 | "It was a kindness, and" | | 11 | "She looked down at the" | | 12 | "She felt ridiculous in her" | | 13 | "His silver signet ring flashed" | | 14 | "He didn’t ask questions." | | 15 | "He never had to." | | 16 | "Her sleeve had ridden up," | | 17 | "She drew the cuff down" | | 18 | "It had been sitting somewhere" | | 19 | "She had found Silas, who" |
| | ratio | 0.293 | |
| 50.23% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 109 | | totalSentences | 133 | | matches | | 0 | "The green neon above the" | | 1 | "Rory should have gone upstairs." | | 2 | "Her delivery bag was still" | | 3 | "She was halfway to the" | | 4 | "A woman came in with" | | 5 | "Rory knew her before she" | | 6 | "It was not her face," | | 7 | "Faces changed in the ordinary" | | 8 | "It was the way she" | | 9 | "The woman looked up." | | 10 | "She had cut her hair" | | 11 | "It was darker than Rory" | | 12 | "Her coat was good wool," | | 13 | "Rory could almost see the" | | 14 | "The girl who had once" | | 15 | "That girl had been gone" | | 16 | "Eva glanced at the bar," | | 17 | "A thread of the old" | | 18 | "Rory’s fingers curled around the" | | 19 | "Eva nodded toward the far" |
| | ratio | 0.82 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 133 | | matches | | 0 | "Now they rested on the" | | 1 | "By the time Rory had" | | 2 | "Now the silence was crowded" |
| | ratio | 0.023 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 1 | | matches | | 0 | "In most versions they were useless, or too late, or delivered by someone who had no idea what she had been asked to forgive." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 33 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 23 | | fancyCount | 2 | | fancyTags | | 0 | "Eva breathed (breathe)" | | 1 | "He continued (continue)" |
| | dialogueSentences | 123 | | tagDensity | 0.187 | | leniency | 0.374 | | rawRatio | 0.087 | | effectiveRatio | 0.033 | |