| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 79 | | tagDensity | 0.19 | | leniency | 0.38 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.54% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2031 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 77.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2031 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "porcelain" | | 1 | "navigated" | | 2 | "scanning" | | 3 | "silence" | | 4 | "framework" | | 5 | "pulsed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "without second thought" | | count | 1 |
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| | highlights | | 0 | "without a second thought" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 169 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 169 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 233 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 14 | | totalWords | 2031 | | ratio | 0.007 | | matches | | 0 | "I know you're home and I'll wait all night." | | 1 | "my accountant" | | 2 | "my plumber." | | 3 | "understated." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1445 | | uniqueNames | 17 | | maxNameDensity | 0.62 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 7 | | London | 1 | | Ptolemy | 4 | | Sumerian | 1 | | Moreau | 2 | | Brick | 1 | | Lane | 1 | | Wednesday | 1 | | Bermondsey | 2 | | Let | 1 | | Lucien | 6 | | Cold | 2 | | Flat | 1 | | Waterloo | 2 | | Bridge | 1 | | Rory | 9 | | One | 3 |
| | persons | | 0 | "Eva" | | 1 | "Ptolemy" | | 2 | "Moreau" | | 3 | "Lucien" | | 4 | "Cold" | | 5 | "Rory" | | 6 | "One" |
| | places | | 0 | "London" | | 1 | "Brick" | | 2 | "Lane" | | 3 | "Bermondsey" | | 4 | "Waterloo" | | 5 | "Bridge" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | 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 | 2031 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 233 | | matches | | 0 | "put that cane" | | 1 | "faint that she'd" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 117 | | mean | 17.36 | | std | 17.13 | | cv | 0.987 | | sampleLengths | | 0 | 7 | | 1 | 34 | | 2 | 22 | | 3 | 18 | | 4 | 5 | | 5 | 55 | | 6 | 10 | | 7 | 42 | | 8 | 2 | | 9 | 2 | | 10 | 11 | | 11 | 1 | | 12 | 5 | | 13 | 7 | | 14 | 35 | | 15 | 10 | | 16 | 53 | | 17 | 11 | | 18 | 8 | | 19 | 2 | | 20 | 16 | | 21 | 29 | | 22 | 3 | | 23 | 4 | | 24 | 3 | | 25 | 2 | | 26 | 11 | | 27 | 20 | | 28 | 77 | | 29 | 4 | | 30 | 55 | | 31 | 6 | | 32 | 2 | | 33 | 6 | | 34 | 56 | | 35 | 11 | | 36 | 3 | | 37 | 41 | | 38 | 51 | | 39 | 34 | | 40 | 16 | | 41 | 9 | | 42 | 22 | | 43 | 28 | | 44 | 3 | | 45 | 8 | | 46 | 15 | | 47 | 1 | | 48 | 9 | | 49 | 6 |
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| 99.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 169 | | matches | | 0 | "being breached" | | 1 | "been told" | | 2 | "was agitated" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 247 | | matches | | 0 | "was peeling" | | 1 | "was choosing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 233 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1454 | | adjectiveStacks | 1 | | stackExamples | | 0 | "bent, scratched behind his" |
| | adverbCount | 35 | | adverbRatio | 0.024071526822558458 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004814305364511692 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 233 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 233 | | mean | 8.72 | | std | 8.3 | | cv | 0.952 | | sampleLengths | | 0 | 2 | | 1 | 5 | | 2 | 26 | | 3 | 3 | | 4 | 4 | | 5 | 1 | | 6 | 5 | | 7 | 1 | | 8 | 1 | | 9 | 15 | | 10 | 15 | | 11 | 3 | | 12 | 3 | | 13 | 2 | | 14 | 23 | | 15 | 14 | | 16 | 3 | | 17 | 2 | | 18 | 13 | | 19 | 2 | | 20 | 3 | | 21 | 5 | | 22 | 7 | | 23 | 22 | | 24 | 9 | | 25 | 4 | | 26 | 2 | | 27 | 2 | | 28 | 11 | | 29 | 1 | | 30 | 5 | | 31 | 7 | | 32 | 31 | | 33 | 4 | | 34 | 10 | | 35 | 6 | | 36 | 7 | | 37 | 12 | | 38 | 16 | | 39 | 12 | | 40 | 11 | | 41 | 8 | | 42 | 2 | | 43 | 5 | | 44 | 5 | | 45 | 6 | | 46 | 13 | | 47 | 10 | | 48 | 6 | | 49 | 3 |
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| 59.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3905579399141631 | | totalSentences | 233 | | uniqueOpeners | 91 | |
| 95.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 139 | | matches | | 0 | "Somewhere on the street, a" | | 1 | "Always filing things away in" | | 2 | "More like the feeling before" | | 3 | "Just the ghost of it." |
| | ratio | 0.029 | |
| 44.46% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 139 | | matches | | 0 | "She knew because she'd counted" | | 1 | "His ears flattened." | | 2 | "She crossed the narrow hallway," | | 3 | "Her hand hovered over the" | | 4 | "She curled her fingers into" | | 5 | "She left the chain on" | | 6 | "His gaze dropped to the" | | 7 | "He leaned on the ivory-handled" | | 8 | "She'd watched him put that" | | 9 | "His suit was new, or" | | 10 | "She unhooked the chain." | | 11 | "He stepped inside and the" | | 12 | "It was already small, Eva's" | | 13 | "He stopped in the centre" | | 14 | "He didn't sit." | | 15 | "He never sat without invitation," | | 16 | "She'd offered out of reflex," | | 17 | "She turned toward the kitchen" | | 18 | "She filled the kettle, set" | | 19 | "She gave Lucien the chipped" |
| | ratio | 0.439 | |
| 75.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 107 | | totalSentences | 139 | | matches | | 0 | "Rory had locked all three." | | 1 | "She knew because she'd counted" | | 2 | "Muscle memory now." | | 3 | "The knock came in threes." | | 4 | "The kind of knock that" | | 5 | "Ptolemy lifted his head from" | | 6 | "His ears flattened." | | 7 | "She crossed the narrow hallway," | | 8 | "Both fixed on the door" | | 9 | "Her hand hovered over the" | | 10 | "The crescent scar on her" | | 11 | "She curled her fingers into" | | 12 | "She left the chain on" | | 13 | "His gaze dropped to the" | | 14 | "He leaned on the ivory-handled" | | 15 | "The lean was performance, a" | | 16 | "She'd watched him put that" | | 17 | "The memory sat between them" | | 18 | "Something shifted behind his expression." | | 19 | "A crack in the porcelain." |
| | ratio | 0.77 | |
| 35.97% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 139 | | matches | | 0 | "As though the demon blood" |
| | ratio | 0.007 | |
| 60.44% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 6 | | matches | | 0 | "The kind of knock that said *I know you're home and I'll wait all night.*" | | 1 | "It was already small, Eva's books colonising every surface, stacked on chairs and wedged into the gap between the fridge and the wall, scrolls unfurling from sh…" | | 2 | "Old-world manners wrapped around something that wasn't old-world at all." | | 3 | "As though the demon blood in his veins was a line item on a tax return." | | 4 | "He straightened, both hands on the cane, and for a moment he looked exactly like what he was: a man who was half of something else, caught perpetually between w…" | | 5 | "The knowledge sat between them alongside everything else, the Bermondsey night, the Waterloo cab, the phone calls that had stretched past three in the morning, …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 3 | | fancyTags | | 0 | "Rory muttered (mutter)" | | 1 | "he continued (continue)" | | 2 | "She laughed (laugh)" |
| | dialogueSentences | 79 | | tagDensity | 0.101 | | leniency | 0.203 | | rawRatio | 0.375 | | effectiveRatio | 0.076 | |