| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1344 | | 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) | |
| 40.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1344 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "streaming" | | 1 | "chilling" | | 2 | "pulsed" | | 3 | "rhythmic" | | 4 | "gloom" | | 5 | "velvet" | | 6 | "silence" | | 7 | "traced" | | 8 | "variables" | | 9 | "standard" | | 10 | "glinting" | | 11 | "could feel" | | 12 | "vibrated" | | 13 | "familiar" | | 14 | "scanning" |
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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 | 130 | | matches | (empty) | |
| 98.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 130 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1344 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 2 | | matches | | 0 | "Eighteen years on the force had taught her that pain was just information, and right now, it told her she was pushing to…" | | 1 | "I am looking for the man who just went through, she said." |
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| 90.48% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1344 | | uniqueNames | 17 | | maxNameDensity | 1.19 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 16 | | Camden | 1 | | High | 1 | | Street | 1 | | Raven | 2 | | Nest | 2 | | Tomás | 1 | | Herrera | 4 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 5 | | Saint | 1 | | Christopher | 1 | | Three | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Market" | | 6 | "Morris" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" |
| | globalScore | 0.905 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 1 | | matches | | 0 | "graffiti that seemed to shift when she looked at them directly" |
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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 | 1344 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 130 | | matches | (empty) | |
| 83.27% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 58.43 | | std | 25.8 | | cv | 0.441 | | sampleLengths | | 0 | 72 | | 1 | 69 | | 2 | 111 | | 3 | 50 | | 4 | 81 | | 5 | 54 | | 6 | 58 | | 7 | 77 | | 8 | 19 | | 9 | 55 | | 10 | 124 | | 11 | 89 | | 12 | 34 | | 13 | 40 | | 14 | 35 | | 15 | 52 | | 16 | 58 | | 17 | 35 | | 18 | 24 | | 19 | 34 | | 20 | 38 | | 21 | 62 | | 22 | 73 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 130 | | matches | | |
| 19.82% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 222 | | matches | | 0 | "was not hunting" | | 1 | "was hunting" | | 2 | "was pushing" | | 3 | "was shaking" | | 4 | "was looking" | | 5 | "was just beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 130 | | ratio | 0.008 | | matches | | 0 | "The rain in London did not wash things clean; it only made the grime slicker." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1353 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavier, pressing against her" |
| | adverbCount | 37 | | adverbRatio | 0.027346637102734665 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008130081300813009 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 10.34 | | std | 5.98 | | cv | 0.578 | | sampleLengths | | 0 | 15 | | 1 | 22 | | 2 | 23 | | 3 | 12 | | 4 | 14 | | 5 | 15 | | 6 | 4 | | 7 | 16 | | 8 | 6 | | 9 | 14 | | 10 | 8 | | 11 | 18 | | 12 | 21 | | 13 | 26 | | 14 | 20 | | 15 | 7 | | 16 | 11 | | 17 | 14 | | 18 | 7 | | 19 | 24 | | 20 | 5 | | 21 | 11 | | 22 | 17 | | 23 | 18 | | 24 | 5 | | 25 | 14 | | 26 | 4 | | 27 | 12 | | 28 | 8 | | 29 | 10 | | 30 | 10 | | 31 | 9 | | 32 | 17 | | 33 | 13 | | 34 | 7 | | 35 | 14 | | 36 | 11 | | 37 | 13 | | 38 | 21 | | 39 | 11 | | 40 | 14 | | 41 | 10 | | 42 | 11 | | 43 | 7 | | 44 | 3 | | 45 | 13 | | 46 | 6 | | 47 | 10 | | 48 | 5 | | 49 | 14 |
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| 33.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.26153846153846155 | | totalSentences | 130 | | uniqueOpeners | 34 | |
| 53.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 124 | | matches | | 0 | "Just a gap in the" | | 1 | "Then the sound of the" |
| | ratio | 0.016 | |
| 58.71% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 124 | | matches | | 0 | "She kept her head low," | | 1 | "Her breath came in white" | | 2 | "She had been watching the" | | 3 | "She had seen the scar" | | 4 | "He was a medic who" | | 5 | "She was hunting the boy" | | 6 | "She ignored the burn in" | | 7 | "She did not slow down." | | 8 | "It was a hollowed-out thing," | | 9 | "It was colder, too." | | 10 | "She stepped through the gap" | | 11 | "It was small, carved, and" | | 12 | "She had heard the whispers" | | 13 | "She looked down at her" | | 14 | "They had been chasing a" | | 15 | "She had seen the look" | | 16 | "It was the look of" | | 17 | "She was forty-one years old." | | 18 | "She had a sharp jaw," | | 19 | "She suspected the clique operating" |
| | ratio | 0.403 | |
| 32.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 124 | | matches | | 0 | "The rain in London did" | | 1 | "Detective Harlow Quinn pushed through" | | 2 | "She kept her head low," | | 3 | "Her breath came in white" | | 4 | "The figure was lean, fast," | | 5 | "Quinn checked her watch." | | 6 | "The worn leather strap was" | | 7 | "She had been watching the" | | 8 | "The distinctive green neon sign" | | 9 | "She had seen the scar" | | 10 | "He was a medic who" | | 11 | "She was hunting the boy" | | 12 | "Quinn surged forward, her military" | | 13 | "She ignored the burn in" | | 14 | "She did not slow down." | | 15 | "The suspect vanished into the" | | 16 | "It was a hollowed-out thing," | | 17 | "Quinn skidded to a halt" | | 18 | "The air here was different." | | 19 | "The smell of wet concrete" |
| | ratio | 0.855 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 124 | | matches | | 0 | "Even from this distance, Quinn" | | 1 | "If she turned back now," | | 2 | "If she left, the trail" |
| | ratio | 0.024 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 8 | | matches | | 0 | "Detective Harlow Quinn pushed through the downpour, her boots striking the pavement with a rhythm that matched the hammering of her heart." | | 1 | "She was hunting the boy who had run from his table." | | 2 | "It was a hollowed-out thing, fenced off with chain-link that had been sliced open at the bottom." | | 3 | "People moved between them, cloaked in hoods that obscured their faces." | | 4 | "She suspected the clique operating out of the Raven's Nest was moving something dangerous, something that got Morris killed." | | 5 | "She thought of the salt-and-pepper hair in the mirror that grew grayer every year she spent chasing ghosts." | | 6 | "The cold hit her first, a shock that seized her lungs." | | 7 | "She moved into the crowd, her brown eyes scanning the shadows, hunting for the boy, hunting for the truth, hunting for the thing that had taken her partner." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |