| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 193 | | tagDensity | 0.01 | | leniency | 0.021 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2336 | | totalAiIsmAdverbs | 1 | | 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) | |
| 97.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2336 | | 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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 151 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 342 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2336 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 101 | | wordCount | 1360 | | uniqueNames | 13 | | maxNameDensity | 3.46 | | worstName | "Aurora" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Aurora" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 2 | | Japanese | 1 | | Silas | 6 | | Aurora | 47 | | Welsh | 1 | | Evan | 1 | | Morgan | 1 | | Carys | 37 | | Yamazaki | 1 | | Prague | 1 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Silas" | | 2 | "Aurora" | | 3 | "Evan" | | 4 | "Morgan" | | 5 | "Carys" | | 6 | "Yamazaki" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "Cardiff" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed borrowed armour cut for a larger body" |
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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 | 2336 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 342 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 279 | | mean | 8.37 | | std | 10.05 | | cv | 1.2 | | sampleLengths | | 0 | 40 | | 1 | 71 | | 2 | 16 | | 3 | 6 | | 4 | 4 | | 5 | 6 | | 6 | 4 | | 7 | 15 | | 8 | 5 | | 9 | 5 | | 10 | 24 | | 11 | 62 | | 12 | 7 | | 13 | 12 | | 14 | 5 | | 15 | 1 | | 16 | 43 | | 17 | 6 | | 18 | 1 | | 19 | 21 | | 20 | 4 | | 21 | 24 | | 22 | 3 | | 23 | 5 | | 24 | 2 | | 25 | 20 | | 26 | 4 | | 27 | 6 | | 28 | 2 | | 29 | 39 | | 30 | 4 | | 31 | 2 | | 32 | 1 | | 33 | 3 | | 34 | 2 | | 35 | 2 | | 36 | 4 | | 37 | 23 | | 38 | 4 | | 39 | 9 | | 40 | 3 | | 41 | 5 | | 42 | 2 | | 43 | 1 | | 44 | 20 | | 45 | 4 | | 46 | 3 | | 47 | 4 | | 48 | 14 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 151 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 243 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 3 | | flaggedSentences | 3 | | totalSentences | 342 | | ratio | 0.009 | | matches | | 0 | "The navy suit had looked severe at the door; now it seemed borrowed, armour cut for a larger body." | | 1 | "The woman shook water from her sleeves; the man collected paper napkins and dabbed at her hair." | | 2 | "Water sloshed across her hand; ice struck her wrist, cold against the scar." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1364 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.020527859237536656 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001466275659824047 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 342 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 342 | | mean | 6.83 | | std | 5.74 | | cv | 0.84 | | sampleLengths | | 0 | 18 | | 1 | 22 | | 2 | 15 | | 3 | 23 | | 4 | 10 | | 5 | 23 | | 6 | 11 | | 7 | 5 | | 8 | 6 | | 9 | 4 | | 10 | 6 | | 11 | 4 | | 12 | 15 | | 13 | 5 | | 14 | 5 | | 15 | 3 | | 16 | 11 | | 17 | 10 | | 18 | 24 | | 19 | 8 | | 20 | 8 | | 21 | 22 | | 22 | 7 | | 23 | 3 | | 24 | 9 | | 25 | 5 | | 26 | 1 | | 27 | 4 | | 28 | 2 | | 29 | 2 | | 30 | 35 | | 31 | 6 | | 32 | 1 | | 33 | 4 | | 34 | 3 | | 35 | 14 | | 36 | 4 | | 37 | 24 | | 38 | 3 | | 39 | 5 | | 40 | 2 | | 41 | 4 | | 42 | 16 | | 43 | 4 | | 44 | 6 | | 45 | 2 | | 46 | 11 | | 47 | 8 | | 48 | 20 | | 49 | 4 |
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| 45.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.22807017543859648 | | totalSentences | 342 | | uniqueOpeners | 78 | |
| 23.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 142 | | matches | | 0 | "Then she looked at her" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 142 | | matches | | 0 | "His silver signet ring tapped" | | 1 | "She held herself upright, chin" | | 2 | "It belonged in lecture theatres" | | 3 | "She took two steps towards" | | 4 | "His gaze paused on Aurora," | | 5 | "He lifted the hinged section" | | 6 | "His limp showed more when" | | 7 | "She reached for a bottle." | | 8 | "She filled a tall glass" | | 9 | "He thanked her without breaking" | | 10 | "She took his empty glass," | | 11 | "She placed it near Carys." | | 12 | "She pressed until the old" | | 13 | "He would have heard enough" | | 14 | "He had given her privacy" | | 15 | "Their ease felt obscene." | | 16 | "She lifted her bag from" | | 17 | "Their fingers touched." | | 18 | "She placed a ten-pound note" | | 19 | "She stood with the counter" |
| | ratio | 0.141 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 132 | | totalSentences | 142 | | matches | | 0 | "Rain lacquered the Soho pavement" | | 1 | "Aurora watched it from behind" | | 2 | "The old maps on the" | | 3 | "Silas moved along the shelves" | | 4 | "Bottles clinked under his hands." | | 5 | "His silver signet ring tapped" | | 6 | "The door opened." | | 7 | "Aurora reached for a fresh" | | 8 | "A woman stood beneath the" | | 9 | "Water darkened the shoulders of" | | 10 | "A silver hoop gleamed in" | | 11 | "She held herself upright, chin" | | 12 | "Aurora knew the shape of" | | 13 | "The tumbler slipped." | | 14 | "Silas caught it against the" | | 15 | "The woman stared at Aurora." | | 16 | "The name landed wrong." | | 17 | "It belonged in lecture theatres" | | 18 | "Aurora set the tea towel" | | 19 | "Carys Morgan’s mouth opened." |
| | ratio | 0.93 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 142 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 1 | | matches | | 0 | "Aurora looked around the Nest: the green neon shivering on the glassware, old countries pinned to the walls, Silas’s scratched photograph, the narrow corridor t…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "Carys murmured (murmur)" |
| | dialogueSentences | 193 | | tagDensity | 0.01 | | leniency | 0.021 | | rawRatio | 0.5 | | effectiveRatio | 0.01 | |