| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 2 | | adverbTags | | 0 | "She gestured vaguely [vaguely]" | | 1 | "he said quietly [quietly]" |
| | dialogueSentences | 51 | | tagDensity | 0.471 | | leniency | 0.941 | | rawRatio | 0.083 | | effectiveRatio | 0.078 | |
| 79.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1202 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slightly" | | 1 | "quickly" | | 2 | "very" | | 3 | "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) | |
| 79.20% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1202 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "shattered" | | 1 | "weight" | | 2 | "trembled" | | 3 | "flickered" |
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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 | 0 | | narrationSentences | 61 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 61 | | filterMatches | (empty) | | hedgeMatches | | |
| 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 | 77 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1202 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 90.62% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 842 | | uniqueNames | 17 | | maxNameDensity | 1.19 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 10 | | God | 1 | | Raven | 1 | | Nest | 1 | | Silas | 9 | | Blackwood | 2 | | Evan | 1 | | Cardiff | 1 | | Drank | 1 | | London | 1 | | Eva | 1 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | Camden | 1 | | King | 1 | | Cross | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Blackwood" | | 4 | "Evan" | | 5 | "Eva" | | 6 | "Cheung" | | 7 | "King" | | 8 | "Cross" |
| | places | | | globalScore | 0.906 | | windowScore | 1 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like resentment" |
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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 | 1202 | | 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 | 48 | | mean | 25.04 | | std | 25.34 | | cv | 1.012 | | sampleLengths | | 0 | 37 | | 1 | 4 | | 2 | 29 | | 3 | 10 | | 4 | 89 | | 5 | 11 | | 6 | 27 | | 7 | 14 | | 8 | 61 | | 9 | 4 | | 10 | 1 | | 11 | 45 | | 12 | 6 | | 13 | 2 | | 14 | 10 | | 15 | 16 | | 16 | 4 | | 17 | 36 | | 18 | 37 | | 19 | 39 | | 20 | 6 | | 21 | 6 | | 22 | 48 | | 23 | 27 | | 24 | 9 | | 25 | 3 | | 26 | 65 | | 27 | 1 | | 28 | 107 | | 29 | 64 | | 30 | 5 | | 31 | 3 | | 32 | 52 | | 33 | 25 | | 34 | 30 | | 35 | 2 | | 36 | 3 | | 37 | 61 | | 38 | 15 | | 39 | 5 | | 40 | 6 | | 41 | 27 | | 42 | 8 | | 43 | 76 | | 44 | 15 | | 45 | 31 | | 46 | 4 | | 47 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 61 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 144 | | matches | | |
| 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) | |
| 91.76% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 850 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.04941176470588235 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.01647058823529412 | |
| 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 | 13.66 | | std | 13.57 | | cv | 0.994 | | sampleLengths | | 0 | 11 | | 1 | 26 | | 2 | 4 | | 3 | 4 | | 4 | 25 | | 5 | 9 | | 6 | 1 | | 7 | 33 | | 8 | 16 | | 9 | 40 | | 10 | 4 | | 11 | 7 | | 12 | 18 | | 13 | 9 | | 14 | 8 | | 15 | 6 | | 16 | 13 | | 17 | 23 | | 18 | 25 | | 19 | 4 | | 20 | 1 | | 21 | 4 | | 22 | 23 | | 23 | 18 | | 24 | 6 | | 25 | 2 | | 26 | 3 | | 27 | 1 | | 28 | 6 | | 29 | 3 | | 30 | 13 | | 31 | 4 | | 32 | 33 | | 33 | 3 | | 34 | 18 | | 35 | 19 | | 36 | 2 | | 37 | 2 | | 38 | 16 | | 39 | 6 | | 40 | 2 | | 41 | 11 | | 42 | 6 | | 43 | 6 | | 44 | 16 | | 45 | 30 | | 46 | 2 | | 47 | 6 | | 48 | 21 | | 49 | 9 |
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| 57.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4090909090909091 | | totalSentences | 88 | | uniqueOpeners | 36 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 46.09% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 46 | | matches | | 0 | "She was already reaching for" | | 1 | "She tore off a sheet" | | 2 | "He wore a charcoal coat" | | 3 | "He laughed, but it died" | | 4 | "She felt it then, the" | | 5 | "He ordered a whisky." | | 6 | "She poured it without asking," | | 7 | "She wiped down the same" | | 8 | "She'd told herself it was" | | 9 | "She gestured vaguely, at the" | | 10 | "She saw the knuckles whiten," | | 11 | "he said quietly" | | 12 | "He finally drank, the whisky" | | 13 | "He laughed, hollow" | | 14 | "He set the glass down" | | 15 | "He leaned closer, close enough" | | 16 | "He stopped, shook his head" | | 17 | "She saw a man who" | | 18 | "She met his eyes." | | 19 | "His signet ring was cold" |
| | ratio | 0.435 | |
| 14.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 46 | | matches | | 0 | "The glass slipped from Rory's" | | 1 | "Rory didn't look up." | | 2 | "She was already reaching for" | | 3 | "She tore off a sheet" | | 4 | "Silas Blackwood leaned against the" | | 5 | "The green neon from the" | | 6 | "He wore a charcoal coat" | | 7 | "Rory crumpled the towel, tossed" | | 8 | "He laughed, but it died" | | 9 | "She felt it then, the" | | 10 | "The flat above this very" | | 11 | "The way he'd looked at" | | 12 | "He ordered a whisky." | | 13 | "She poured it without asking," | | 14 | "The signet ring on his" | | 15 | "Rory's jaw tightened." | | 16 | "She wiped down the same" | | 17 | "Silas set the glass down," | | 18 | "Yu-Fei Cheung's Golden Empress, the" | | 19 | "She'd told herself it was" |
| | ratio | 0.891 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 6 | | matches | | 0 | "She was already reaching for the roll of paper towels behind the bar, her wrist turning to reveal the crescent scar, pale against her skin." | | 1 | "Silas Blackwood leaned against the bar's edge, all six feet of him folded into a posture that suggested he'd once been taller, or perhaps simply more certain of…" | | 2 | "He wore a charcoal coat that had seen better decades, and his hazel eyes, magnified slightly by wire-rimmed spectacles she didn't remember him owning, tracked h…" | | 3 | "She saw the knuckles whiten, the old injury in his knee shifting as he adjusted his weight, favouring his left leg." | | 4 | "Silas reached across the bar, his fingers brushing her scarred wrist with a gentleness that made her breath catch." | | 5 | "Outside, the green neon flickered, and for a moment the bar was dark, and Rory felt the weight of twenty-three years pressing down on her shoulders, and then th…" |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 2 | | matches | | 0 | "Silas set, untouched" | | 1 | "He laughed, hollow" |
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| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 3 | | fancyTags | | 0 | "He laughed (laugh)" | | 1 | "He laughed (laugh)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 51 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0.25 | | effectiveRatio | 0.118 | |