| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 83 | | tagDensity | 0.048 | | leniency | 0.096 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2271 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 84.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2271 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "weight" | | 1 | "pulse" | | 2 | "flickered" | | 3 | "trembled" | | 4 | "pulsed" | | 5 | "familiar" |
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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 | 212 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 212 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 290 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2271 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.79% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 107 | | wordCount | 1883 | | uniqueNames | 24 | | maxNameDensity | 2.12 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 40 | | Raven | 2 | | Nest | 3 | | Tomás | 1 | | Herrera | 32 | | Saint | 2 | | Christopher | 2 | | Wardour | 1 | | Street | 2 | | Oxford | 1 | | Clerkenwell | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Oyster | 1 | | Morris | 2 | | Boss | 1 | | Thames | 1 | | London | 2 | | Camden | 1 | | Ahead | 3 | | Three | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" | | 9 | "Ahead" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Oxford" | | 4 | "Clerkenwell" | | 5 | "Tottenham" | | 6 | "Court" | | 7 | "Road" | | 8 | "Thames" | | 9 | "London" |
| | globalScore | 0.438 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 143 | | 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 | 2271 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 290 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 205 | | mean | 11.08 | | std | 12.23 | | cv | 1.104 | | sampleLengths | | 0 | 9 | | 1 | 39 | | 2 | 4 | | 3 | 52 | | 4 | 12 | | 5 | 3 | | 6 | 7 | | 7 | 7 | | 8 | 2 | | 9 | 7 | | 10 | 29 | | 11 | 2 | | 12 | 17 | | 13 | 31 | | 14 | 8 | | 15 | 4 | | 16 | 2 | | 17 | 11 | | 18 | 7 | | 19 | 6 | | 20 | 2 | | 21 | 10 | | 22 | 46 | | 23 | 5 | | 24 | 11 | | 25 | 29 | | 26 | 4 | | 27 | 2 | | 28 | 41 | | 29 | 3 | | 30 | 28 | | 31 | 9 | | 32 | 39 | | 33 | 3 | | 34 | 26 | | 35 | 7 | | 36 | 15 | | 37 | 5 | | 38 | 2 | | 39 | 8 | | 40 | 1 | | 41 | 6 | | 42 | 32 | | 43 | 4 | | 44 | 4 | | 45 | 3 | | 46 | 17 | | 47 | 2 | | 48 | 2 | | 49 | 21 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 212 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 321 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 290 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1888 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.015889830508474576 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0015889830508474577 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 290 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 290 | | mean | 7.83 | | std | 5.32 | | cv | 0.679 | | sampleLengths | | 0 | 9 | | 1 | 18 | | 2 | 21 | | 3 | 4 | | 4 | 28 | | 5 | 13 | | 6 | 6 | | 7 | 5 | | 8 | 12 | | 9 | 3 | | 10 | 7 | | 11 | 7 | | 12 | 2 | | 13 | 7 | | 14 | 14 | | 15 | 15 | | 16 | 2 | | 17 | 6 | | 18 | 11 | | 19 | 18 | | 20 | 5 | | 21 | 8 | | 22 | 8 | | 23 | 4 | | 24 | 2 | | 25 | 11 | | 26 | 7 | | 27 | 6 | | 28 | 2 | | 29 | 5 | | 30 | 5 | | 31 | 6 | | 32 | 14 | | 33 | 26 | | 34 | 5 | | 35 | 11 | | 36 | 11 | | 37 | 18 | | 38 | 4 | | 39 | 2 | | 40 | 5 | | 41 | 16 | | 42 | 6 | | 43 | 14 | | 44 | 3 | | 45 | 10 | | 46 | 8 | | 47 | 10 | | 48 | 9 | | 49 | 6 |
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| 57.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3620689655172414 | | totalSentences | 290 | | uniqueOpeners | 105 | |
| 16.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 202 | | matches | | 0 | "Somewhere beyond the stalls, a" |
| | ratio | 0.005 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 202 | | matches | | 0 | "Her watch read 23:17." | | 1 | "He knew someone had followed" | | 2 | "He hadn’t spotted her yet." | | 3 | "Their eyes met through the" | | 4 | "His chair struck the floor" | | 5 | "He seized a brass raven" | | 6 | "He glanced over his shoulder," | | 7 | "He slipped through the opening." | | 8 | "She forced herself through the" | | 9 | "He was already at the" | | 10 | "He ducked beneath an umbrella," | | 11 | "His left hand slapped the" | | 12 | "His sleeve rode up, exposing" | | 13 | "Its wing mirror missed her" | | 14 | "She hammered her palm against" | | 15 | "Her lungs worked in clipped," | | 16 | "He moved like a man" | | 17 | "Her shoulder clipped his and" | | 18 | "His warm brown eyes caught" | | 19 | "He reached inside his coat." |
| | ratio | 0.213 | |
| 31.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 173 | | totalSentences | 202 | | matches | | 0 | "Rain glazed Soho in black" | | 1 | "Detective Harlow Quinn stood across" | | 2 | "The bar’s neon sign buzzed" | | 3 | "Her watch read 23:17." | | 4 | "Quinn had watched him study" | | 5 | "He knew someone had followed" | | 6 | "He hadn’t spotted her yet." | | 7 | "A bus dragged through a" | | 8 | "Herrera looked up." | | 9 | "Their eyes met through the" | | 10 | "His chair struck the floor" | | 11 | "Quinn left the doorway at" | | 12 | "Quinn burst through the door," | | 13 | "A woman in red stumbled" | | 14 | "A pint toppled from the" | | 15 | "The Nest’s walls flashed past" | | 16 | "Herrera reached the back wall." | | 17 | "He seized a brass raven" | | 18 | "The shelf swung inwards." | | 19 | "He glanced over his shoulder," |
| | ratio | 0.856 | |
| 24.75% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 202 | | matches | | 0 | "By the time she reached" |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 83 | | technicalSentenceCount | 3 | | matches | | 0 | "The bar’s neon sign buzzed above its entrance, staining every face that passed beneath it the colour of old hospital tiles." | | 1 | "The tunnel walls gave a low groan, as if the city had shifted its weight overhead." | | 2 | "A butcher in a waxed apron lifted a cleaver over something that knocked from inside a wooden crate." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "the coins whispered (whisper)" | | 1 | "the coins chimed (chime)" |
| | dialogueSentences | 83 | | tagDensity | 0.024 | | leniency | 0.048 | | rawRatio | 1 | | effectiveRatio | 0.048 | |