| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1386 | | 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) | |
| 63.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1386 | | totalAiIsms | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "practiced ease" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | |
| | highlights | | 0 | "navigated" | | 1 | "measured" | | 2 | "quickened" | | 3 | "practiced ease" | | 4 | "streaming" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "silence" | | 8 | "weight" | | 9 | "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 | 132 | | matches | (empty) | |
| 88.74% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 132 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1386 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1379 | | uniqueNames | 21 | | maxNameDensity | 0.94 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 4 | | Harlow | 1 | | Quinn | 13 | | Raven | 1 | | Nest | 1 | | Herrera | 11 | | Old | 1 | | Compton | 1 | | Street | 1 | | Saint | 1 | | Christopher | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Camden | 1 | | Three | 5 | | London | 2 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Market" | | 6 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Old" | | 3 | "Compton" | | 4 | "Street" | | 5 | "Charing" | | 6 | "Cross" | | 7 | "Road" | | 8 | "Camden" | | 9 | "London" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 1 | | matches | | 0 | "vial that seemed to glow from within" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1386 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 88.41% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 39.6 | | std | 18.19 | | cv | 0.459 | | sampleLengths | | 0 | 63 | | 1 | 77 | | 2 | 55 | | 3 | 64 | | 4 | 14 | | 5 | 38 | | 6 | 41 | | 7 | 41 | | 8 | 64 | | 9 | 4 | | 10 | 45 | | 11 | 29 | | 12 | 28 | | 13 | 47 | | 14 | 48 | | 15 | 43 | | 16 | 26 | | 17 | 2 | | 18 | 39 | | 19 | 42 | | 20 | 8 | | 21 | 61 | | 22 | 42 | | 23 | 82 | | 24 | 44 | | 25 | 30 | | 26 | 41 | | 27 | 45 | | 28 | 37 | | 29 | 28 | | 30 | 29 | | 31 | 37 | | 32 | 25 | | 33 | 46 | | 34 | 21 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 132 | | matches | | 0 | "being followed" | | 1 | "been locked" | | 2 | "been painted" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 12 | | totalVerbs | 254 | | matches | | 0 | "was going" | | 1 | "was building" | | 2 | "wasn't wandering" | | 3 | "Wasn't behaving" | | 4 | "was passing" | | 5 | "was driving" | | 6 | "wasn't laughing" | | 7 | "was walking" | | 8 | "was making" | | 9 | "was exchanging" | | 10 | "was happening" | | 11 | "was conducting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 132 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1383 | | adjectiveStacks | 1 | | stackExamples | | 0 | "underground supernatural black market" |
| | adverbCount | 35 | | adverbRatio | 0.025307302964569775 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.0072306579898770785 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 10.5 | | std | 5.85 | | cv | 0.557 | | sampleLengths | | 0 | 15 | | 1 | 28 | | 2 | 16 | | 3 | 4 | | 4 | 7 | | 5 | 28 | | 6 | 21 | | 7 | 21 | | 8 | 8 | | 9 | 2 | | 10 | 17 | | 11 | 14 | | 12 | 14 | | 13 | 7 | | 14 | 17 | | 15 | 21 | | 16 | 19 | | 17 | 14 | | 18 | 3 | | 19 | 11 | | 20 | 12 | | 21 | 6 | | 22 | 6 | | 23 | 15 | | 24 | 11 | | 25 | 15 | | 26 | 14 | | 27 | 13 | | 28 | 10 | | 29 | 4 | | 30 | 11 | | 31 | 17 | | 32 | 16 | | 33 | 4 | | 34 | 2 | | 35 | 14 | | 36 | 4 | | 37 | 6 | | 38 | 13 | | 39 | 15 | | 40 | 11 | | 41 | 12 | | 42 | 7 | | 43 | 10 | | 44 | 2 | | 45 | 7 | | 46 | 19 | | 47 | 17 | | 48 | 4 | | 49 | 14 |
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| 60.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4090909090909091 | | totalSentences | 132 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 124 | | matches | | 0 | "Dark curly hair plastered to" | | 1 | "Then she saw it." | | 2 | "Originally a platform, now transformed" | | 3 | "Somewhere in that crowd, Herrera" |
| | ratio | 0.032 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 124 | | matches | | 0 | "She had spotted him outside" | | 1 | "He'd emerged from a side" | | 2 | "He turned left onto Old" | | 3 | "She matched his pace, staying" | | 4 | "she muttered to herself, wiping" | | 5 | "He wasn't wandering." | | 6 | "He suspected he was being" | | 7 | "They pushed north through Soho's" | | 8 | "She followed, dodging an umbrella" | | 9 | "She quickened her stride." | | 10 | "She checked her watch." | | 11 | "She entered the alley." | | 12 | "It dead-ended at a chain-link" | | 13 | "She hit the ground on" | | 14 | "They emerged onto a main" | | 15 | "She memorized the license plate," | | 16 | "She stood on the pavement," | | 17 | "Her training kept her silent" | | 18 | "She pulled out her phone" | | 19 | "Her footsteps echoed despite her" |
| | ratio | 0.274 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 124 | | matches | | 0 | "Detective Harlow Quinn kept her" | | 1 | "Tonight called for patience." | | 2 | "The man she followed moved" | | 3 | "She had spotted him outside" | | 4 | "He'd emerged from a side" | | 5 | "Quinn had memorized his face" | | 6 | "He turned left onto Old" | | 7 | "She matched his pace, staying" | | 8 | "A group of drunk tourists" | | 9 | "The Saint Christopher medallion around" | | 10 | "she muttered to herself, wiping" | | 11 | "He wasn't wandering." | | 12 | "Each turn was deliberate, each" | | 13 | "He suspected he was being" | | 14 | "They pushed north through Soho's" | | 15 | "The rain intensified, hammering against" | | 16 | "Quinn's boots squelched with each" | | 17 | "Herrera crossed Charing Cross Road" | | 18 | "She followed, dodging an umbrella" | | 19 | "The gap between them widened" |
| | ratio | 0.718 | |
| 40.32% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 124 | | matches | | 0 | "Now he ran medical care" |
| | ratio | 0.008 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 11 | | matches | | 0 | "Dark curly hair plastered to his skull, jacket collar turned up, he navigated the narrow streets with the confidence of someone who knew exactly where he was go…" | | 1 | "Former paramedic, lost his NHS license under circumstances that remained murky in the file." | | 2 | "Quinn climbed after him, her jacket catching on a loose wire, tearing at the shoulder." | | 3 | "An abandoned Tube station, one of the many that dotted London's underground network." | | 4 | "An underground supernatural black market that moved locations every full moon." | | 5 | "In all her years on the force, she'd seen things that defied explanation." | | 6 | "The case that killed her partner, DS Morris, three years ago." | | 7 | "The vendor, a man with burn scars covering half his face, watched her with eyes that caught the candlelight strangely." | | 8 | "Somewhere in that crowd, Herrera was conducting business that connected to the network she'd been investigating." | | 9 | "Touched the worn leather of her watch, a habit she'd developed when making decisions that scared her." | | 10 | "The candle flames flickered as she passed them, their colors shifting in ways that made her eyes ache." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |