| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1323 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sharply" | | 1 | "sweetly" | | 2 | "very" | | 3 | "gently" |
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| 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) | |
| 73.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1323 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "could feel" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "flickered" | | 4 | "vibrated" | | 5 | "velvet" |
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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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 15 | | totalWords | 1319 | | ratio | 0.011 | | matches | | 0 | "Some doors you don’t open, Harlow. Not even to see what’s behind them." | | 1 | "some doors" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.81% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1295 | | uniqueNames | 16 | | maxNameDensity | 1 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | High | 1 | | Street | 1 | | Harlow | 2 | | Quinn | 13 | | Raven | 1 | | Nest | 1 | | Kentish | 1 | | Town | 1 | | Morris | 4 | | Hackney | 1 | | Turkish | 1 | | London | 1 | | Underground | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Kentish" | | 4 | "Town" | | 5 | "Hackney" | | 6 | "London" | | 7 | "Veil" | | 8 | "Market" |
| | globalScore | 0.998 | | windowScore | 1 | |
| 58.54% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 3 | | matches | | 0 | "as if carrying something heavy on that side" | | 1 | "quite bone—too smooth, too warm" | | 2 | "as if tasting the word" |
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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 | 1319 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 52.76 | | std | 32.03 | | cv | 0.607 | | sampleLengths | | 0 | 120 | | 1 | 74 | | 2 | 97 | | 3 | 61 | | 4 | 98 | | 5 | 61 | | 6 | 72 | | 7 | 55 | | 8 | 76 | | 9 | 73 | | 10 | 52 | | 11 | 56 | | 12 | 9 | | 13 | 49 | | 14 | 3 | | 15 | 110 | | 16 | 15 | | 17 | 37 | | 18 | 50 | | 19 | 13 | | 20 | 23 | | 21 | 14 | | 22 | 52 | | 23 | 36 | | 24 | 13 |
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| 85.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 105 | | matches | | 0 | "being followed" | | 1 | "was supposed" | | 2 | "were tiled" | | 3 | "was littered" | | 4 | "was vaulted" | | 5 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 219 | | matches | | |
| 63.49% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 108 | | ratio | 0.028 | | matches | | 0 | "The alley, the rain, the city—they felt very far away." | | 1 | "It was lighter than it looked, carved from something that wasn’t quite bone—too smooth, too warm." | | 2 | "She straightened her coat, checked her watch—11:47—and walked deeper into the Veil Market." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1302 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.02534562211981567 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0030721966205837174 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 12.21 | | std | 8.13 | | cv | 0.665 | | sampleLengths | | 0 | 22 | | 1 | 24 | | 2 | 29 | | 3 | 29 | | 4 | 16 | | 5 | 8 | | 6 | 19 | | 7 | 4 | | 8 | 4 | | 9 | 18 | | 10 | 4 | | 11 | 17 | | 12 | 9 | | 13 | 19 | | 14 | 24 | | 15 | 6 | | 16 | 7 | | 17 | 32 | | 18 | 25 | | 19 | 2 | | 20 | 8 | | 21 | 5 | | 22 | 21 | | 23 | 10 | | 24 | 3 | | 25 | 36 | | 26 | 27 | | 27 | 3 | | 28 | 19 | | 29 | 5 | | 30 | 11 | | 31 | 2 | | 32 | 2 | | 33 | 10 | | 34 | 20 | | 35 | 11 | | 36 | 9 | | 37 | 28 | | 38 | 26 | | 39 | 9 | | 40 | 10 | | 41 | 5 | | 42 | 6 | | 43 | 7 | | 44 | 20 | | 45 | 2 | | 46 | 5 | | 47 | 14 | | 48 | 13 | | 49 | 20 |
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| 37.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.28703703703703703 | | totalSentences | 108 | | uniqueOpeners | 31 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 100 | | matches | | 0 | "Instead, the man disappeared into" | | 1 | "Then a click, a soft" | | 2 | "Somewhere above her, rain fell" | | 3 | "Somewhere below, the hum grew" |
| | ratio | 0.04 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 100 | | matches | | 0 | "She kept her eyes fixed" | | 1 | "He had come out of" | | 2 | "He checked shop windows." | | 3 | "He varied his pace." | | 4 | "He crossed when the light" | | 5 | "She chose the traffic." | | 6 | "Her left wrist ached under" | | 7 | "Her shoes were soaked through." | | 8 | "She could feel the cold" | | 9 | "He ducked into a alley" | | 10 | "She had walked these streets" | | 11 | "She had seen it on" | | 12 | "Her hand went to the" | | 13 | "She pulled out her torch," | | 14 | "Her torch beam caught graffiti," | | 15 | "She could hear him breathing." | | 16 | "He was opening a door." | | 17 | "She moved forward, careful, her" | | 18 | "It was open." | | 19 | "She reached the door and" |
| | ratio | 0.4 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 100 | | matches | | 0 | "Rain came down in sheets," | | 1 | "Detective Harlow Quinn moved through" | | 2 | "She kept her eyes fixed" | | 3 | "He had come out of" | | 4 | "Quinn had been tailing him" | | 5 | "He checked shop windows." | | 6 | "He varied his pace." | | 7 | "He crossed when the light" | | 8 | "She chose the traffic." | | 9 | "A black cab braked hard," | | 10 | "Her left wrist ached under" | | 11 | "The official report said cardiac" | | 12 | "The coroner’s face had said" | | 13 | "Quinn had stopped believing in" | | 14 | "The man in the anorak" | | 15 | "Rain drummed on the metal" | | 16 | "Her shoes were soaked through." | | 17 | "She could feel the cold" | | 18 | "He ducked into a alley" | | 19 | "Quinn knew Camden." |
| | ratio | 0.8 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 100 | | matches | | 0 | "Now he was moving fast," | | 1 | "If she called it in," | | 2 | "Now she had to see" |
| | ratio | 0.03 | |
| 50.26% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 7 | | matches | | 0 | "She kept her eyes fixed on the man forty yards ahead: olive anorak, hood up, left shoulder dipping every third step as if carrying something heavy on that side." | | 1 | "She could feel the cold creeping up her ankles, but she kept her breathing even, counting the man’s steps, matching them." | | 2 | "The man in the anorak was already through it, and beyond him Quinn saw a staircase spiraling down, lit by gas lamps that hissed and flickered." | | 3 | "The air that drifted up was warm and thick, carrying smells of incense, sweat, and something sweetly rotten." | | 4 | "She thought of Morris, of the way he had looked at her in the hospital corridor three years ago, before the case that killed him, and said, *Some doors you don’…" | | 5 | "The man who had stopped her was old, his face a map of scars, his eyes milky white." | | 6 | "She was alone, unarmed in a way that had nothing to do with her gun, standing in a market that moved every full moon and sold things that shouldn’t exist." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 2 | | matches | | 0 | "she said, her voice low" | | 1 | "he repeated, as if tasting the word" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 6 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.333 | | effectiveRatio | 0.333 | |