| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1661 | | 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) | |
| 78.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1661 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flicked" | | 1 | "calculating" | | 2 | "footsteps" | | 3 | "electric" | | 4 | "pulse" | | 5 | "warmth" | | 6 | "charm" |
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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 | 169 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 169 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 174 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1660 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.56% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1618 | | uniqueNames | 11 | | maxNameDensity | 1.67 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 2 | | Nest | 1 | | Quinn | 27 | | Camden | 1 | | High | 1 | | Street | 1 | | Underground | 1 | | Morris | 3 | | Don | 2 | | Rain | 3 |
| | persons | | | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Camden" | | 3 | "High" | | 4 | "Street" |
| | globalScore | 0.666 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 121 | | 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 | 1660 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 174 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 65 | | mean | 25.54 | | std | 22.29 | | cv | 0.873 | | sampleLengths | | 0 | 11 | | 1 | 41 | | 2 | 7 | | 3 | 15 | | 4 | 2 | | 5 | 90 | | 6 | 46 | | 7 | 15 | | 8 | 43 | | 9 | 16 | | 10 | 6 | | 11 | 29 | | 12 | 9 | | 13 | 27 | | 14 | 10 | | 15 | 25 | | 16 | 22 | | 17 | 61 | | 18 | 12 | | 19 | 38 | | 20 | 42 | | 21 | 4 | | 22 | 45 | | 23 | 42 | | 24 | 4 | | 25 | 4 | | 26 | 11 | | 27 | 73 | | 28 | 5 | | 29 | 2 | | 30 | 54 | | 31 | 14 | | 32 | 41 | | 33 | 9 | | 34 | 49 | | 35 | 7 | | 36 | 105 | | 37 | 8 | | 38 | 26 | | 39 | 35 | | 40 | 11 | | 41 | 32 | | 42 | 60 | | 43 | 34 | | 44 | 15 | | 45 | 13 | | 46 | 8 | | 47 | 6 | | 48 | 62 | | 49 | 14 |
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| 94.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 169 | | matches | | 0 | "been sealed" | | 1 | "been boarded" | | 2 | "were cracked" | | 3 | "been left" | | 4 | "been written" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 285 | | matches | | 0 | "was laundering" | | 1 | "was turning" | | 2 | "was trying" | | 3 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 174 | | ratio | 0.006 | | matches | | 0 | "The crowd’s noise rushed up around her, low and layered—haggling, murmured warnings, a child laughing somewhere out of sight." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1624 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 43 | | adverbRatio | 0.02647783251231527 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.003694581280788177 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 174 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 174 | | mean | 9.54 | | std | 5.75 | | cv | 0.603 | | sampleLengths | | 0 | 11 | | 1 | 12 | | 2 | 10 | | 3 | 19 | | 4 | 7 | | 5 | 14 | | 6 | 1 | | 7 | 2 | | 8 | 4 | | 9 | 11 | | 10 | 26 | | 11 | 5 | | 12 | 22 | | 13 | 22 | | 14 | 15 | | 15 | 10 | | 16 | 21 | | 17 | 6 | | 18 | 9 | | 19 | 6 | | 20 | 5 | | 21 | 18 | | 22 | 8 | | 23 | 6 | | 24 | 8 | | 25 | 4 | | 26 | 4 | | 27 | 6 | | 28 | 2 | | 29 | 15 | | 30 | 9 | | 31 | 2 | | 32 | 1 | | 33 | 9 | | 34 | 8 | | 35 | 10 | | 36 | 6 | | 37 | 3 | | 38 | 10 | | 39 | 11 | | 40 | 14 | | 41 | 4 | | 42 | 5 | | 43 | 13 | | 44 | 18 | | 45 | 7 | | 46 | 1 | | 47 | 17 | | 48 | 18 | | 49 | 12 |
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| 41.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2832369942196532 | | totalSentences | 173 | | uniqueOpeners | 49 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 157 | | matches | | 0 | "Somewhere behind her, a bus" | | 1 | "Then he threw a handful" | | 2 | "Somewhere beyond the tunnel, metal" | | 3 | "Just the wet scrape of" | | 4 | "Instead, she rolled the token" | | 5 | "Somewhere in the station, the" |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 157 | | matches | | 0 | "He slipped past a taxi," | | 1 | "she shouted, though she had" | | 2 | "Her boots struck water hard" | | 3 | "She had chased men through" | | 4 | "He’d checked both directions, crossed" | | 5 | "He had something under his" | | 6 | "She’d seen the hard outline" | | 7 | "He vaulted a low barrier" | | 8 | "His left leg buckled." | | 9 | "His eyes flicked past her" | | 10 | "She kept moving." | | 11 | "He burst out onto a" | | 12 | "She tried again and got" | | 13 | "Her worn leather watch sat" | | 14 | "She glanced at it without" | | 15 | "She had spent eighteen years" | | 16 | "She had also spent three" | | 17 | "She had no room for" | | 18 | "She didn’t slow." | | 19 | "She went after him." |
| | ratio | 0.287 | |
| 52.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 128 | | totalSentences | 157 | | matches | | 0 | "Quinn kept the man in" | | 1 | "A charcoal coat, one shoulder" | | 2 | "He slipped past a taxi," | | 3 | "That meant he knew she" | | 4 | "she shouted, though she had" | | 5 | "Quinn lengthened her stride." | | 6 | "Her boots struck water hard" | | 7 | "She had chased men through" | | 8 | "None of that mattered now." | | 9 | "The suspect had been at" | | 10 | "A woman with no job," | | 11 | "The green neon Raven sign" | | 12 | "Quinn had watched the man" | | 13 | "He’d checked both directions, crossed" | | 14 | "He had something under his" | | 15 | "She’d seen the hard outline" | | 16 | "The man darted down an" | | 17 | "Quinn followed, shoulder brushing brick." | | 18 | "The air smelled of wet" | | 19 | "He vaulted a low barrier" |
| | ratio | 0.815 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 157 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.333 | | effectiveRatio | 0.25 | |