| 64.41% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 4 | | adverbTags | | 0 | "He just said [just]" | | 1 | "the door opened again [again]" | | 2 | "Eva agreed gravely [gravely]" | | 3 | "Eva said again [again]" |
| | dialogueSentences | 59 | | tagDensity | 0.39 | | leniency | 0.78 | | rawRatio | 0.174 | | effectiveRatio | 0.136 | |
| 79.12% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1437 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slowly" | | 1 | "very" | | 2 | "really" | | 3 | "precisely" |
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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) | |
| 96.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1437 | | 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 | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 94 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 26 | | totalWords | 1447 | | ratio | 0.018 | | matches | | 0 | "you told me to come to London and then you weren't in London" | | 1 | "just jump, I'll catch you" | | 2 | "rang" | | 3 | "you're eating rice with a fork" | | 4 | "practical" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 877 | | uniqueNames | 12 | | maxNameDensity | 2.17 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Golden | 1 | | Empress | 1 | | Silas | 8 | | Prague | 2 | | October | 1 | | Rory | 19 | | Eva | 17 | | London | 2 | | Cardiff | 1 | | Twenty-one | 1 |
| | persons | | 0 | "Nest" | | 1 | "Empress" | | 2 | "Silas" | | 3 | "Rory" | | 4 | "Eva" |
| | places | | 0 | "Raven" | | 1 | "Golden" | | 2 | "Prague" | | 3 | "October" | | 4 | "London" | | 5 | "Cardiff" | | 6 | "Twenty-one" |
| | globalScore | 0.417 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | 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 | 1447 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 102 | | matches | | 0 | "out that all" | | 1 | "was that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 25.39 | | std | 29.89 | | cv | 1.177 | | sampleLengths | | 0 | 83 | | 1 | 12 | | 2 | 5 | | 3 | 4 | | 4 | 111 | | 5 | 18 | | 6 | 24 | | 7 | 67 | | 8 | 1 | | 9 | 6 | | 10 | 3 | | 11 | 55 | | 12 | 6 | | 13 | 12 | | 14 | 88 | | 15 | 6 | | 16 | 5 | | 17 | 4 | | 18 | 35 | | 19 | 3 | | 20 | 30 | | 21 | 20 | | 22 | 78 | | 23 | 7 | | 24 | 5 | | 25 | 74 | | 26 | 2 | | 27 | 2 | | 28 | 27 | | 29 | 82 | | 30 | 7 | | 31 | 2 | | 32 | 54 | | 33 | 5 | | 34 | 5 | | 35 | 1 | | 36 | 73 | | 37 | 4 | | 38 | 7 | | 39 | 39 | | 40 | 9 | | 41 | 2 | | 42 | 110 | | 43 | 3 | | 44 | 27 | | 45 | 4 | | 46 | 27 | | 47 | 8 | | 48 | 11 | | 49 | 54 |
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| 89.31% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 66 | | matches | | 0 | "was disappointed" | | 1 | "was, laid" | | 2 | "being asked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 152 | | matches | | 0 | "was polishing" | | 1 | "was looking" |
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| 2.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 102 | | ratio | 0.049 | | matches | | 0 | "The maps on the wall breathed in the damp — they always did, in October." | | 1 | "That was the first thing — that the rain had got her but hadn't ruined her, the coat shedding water in beads, the hair pinned up in something that had cost money to look careless." | | 2 | "Eva laughed — that same laugh, that was the awful thing, the laugh hadn't changed at all, it still went up at the end like a question." | | 3 | "Rory watched her drink and thought about the crescent scar on her own left wrist, which Eva had put there, sort of — the garden wall at the Ellises' house, the loose brick, Eva saying *just jump, I'll catch you*, and Eva had tried to catch her, that was the thing, she'd actually put her arms out, she'd just been eight years old and made of nothing." | | 4 | "Something in her chest gave way sideways — not forgiveness, nothing as clean as that, just the exhaustion of having carried a grudge up nine flights of stairs and discovering the lift had been working all along." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 991 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.026236125126135216 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008072653884964682 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 14.19 | | std | 15.56 | | cv | 1.097 | | sampleLengths | | 0 | 33 | | 1 | 35 | | 2 | 15 | | 3 | 5 | | 4 | 7 | | 5 | 5 | | 6 | 4 | | 7 | 36 | | 8 | 5 | | 9 | 21 | | 10 | 21 | | 11 | 15 | | 12 | 13 | | 13 | 18 | | 14 | 4 | | 15 | 11 | | 16 | 9 | | 17 | 9 | | 18 | 35 | | 19 | 23 | | 20 | 1 | | 21 | 6 | | 22 | 3 | | 23 | 27 | | 24 | 9 | | 25 | 19 | | 26 | 3 | | 27 | 3 | | 28 | 4 | | 29 | 8 | | 30 | 13 | | 31 | 11 | | 32 | 64 | | 33 | 6 | | 34 | 5 | | 35 | 4 | | 36 | 10 | | 37 | 14 | | 38 | 11 | | 39 | 3 | | 40 | 11 | | 41 | 12 | | 42 | 7 | | 43 | 20 | | 44 | 5 | | 45 | 27 | | 46 | 7 | | 47 | 28 | | 48 | 11 | | 49 | 7 |
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| 39.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.30392156862745096 | | totalSentences | 102 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 50 | | matches | | 0 | "He just said, and set" | | 1 | "She put the bag on" | | 2 | "She was five minutes into" | | 3 | "Her mother called her that," | | 4 | "She crossed the room and" | | 5 | "She put a hand on" | | 6 | "He set a glass in" | | 7 | "She left one stool between" | | 8 | "Her nails were done, a" | | 9 | "She had thought about it" | | 10 | "She looked down at it," | | 11 | "She put the fork down," | | 12 | "It just went tired." | | 13 | "She lifted the glass and" |
| | ratio | 0.28 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 50 | | matches | | 0 | "The rain had been coming" | | 1 | "Rory came in shaking water" | | 2 | "He just said, and set" | | 3 | "She put the bag on" | | 4 | "The Nest was almost empty." | | 5 | "A woman at the far" | | 6 | "The maps on the wall" | | 7 | "Rory had come to know" | | 8 | "She was five minutes into" | | 9 | "Nobody called her that." | | 10 | "Her mother called her that," | | 11 | "Rory turned with her fork" | | 12 | "The woman standing in the" | | 13 | "That was the first thing" | | 14 | "Rory's brain did the arithmetic" | | 15 | "Eva laughed — that same" | | 16 | "She crossed the room and" | | 17 | "She put a hand on" | | 18 | "He set a glass in" | | 19 | "Gin, no ice, a slice" |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "She had thought about it in the way you rehearse arguments in the shower, which is to say with the wrong words and far too much dignity." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 99.15% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 3 | | fancyTags | | 0 | "Rory agreed (agree)" | | 1 | "the door opened again (open)" | | 2 | "Eva agreed gravely (agree)" |
| | dialogueSentences | 59 | | tagDensity | 0.254 | | leniency | 0.508 | | rawRatio | 0.2 | | effectiveRatio | 0.102 | |