| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 2 | | adverbTags | | 0 | "Miller gestured vaguely [vaguely]" | | 1 | "Eva looked around [around]" |
| | dialogueSentences | 72 | | tagDensity | 0.361 | | leniency | 0.722 | | rawRatio | 0.077 | | effectiveRatio | 0.056 | |
| 78.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1374 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "utterly" | | 1 | "cautiously" | | 2 | "lazily" | | 3 | "slightly" | | 4 | "slowly" |
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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) | |
| 63.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1374 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "etched" | | 1 | "echoed" | | 2 | "footsteps" | | 3 | "traced" | | 4 | "pulse" | | 5 | "furrowed" | | 6 | "silence" | | 7 | "echo" |
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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 | 107 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 153 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1369 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 944 | | uniqueNames | 11 | | maxNameDensity | 2.01 | | worstName | "Miller" | | maxWindowNameDensity | 4 | | worstWindowName | "Miller" | | discoveredNames | | Quinn | 1 | | Miller | 19 | | Veil | 2 | | Market | 1 | | Camden | 1 | | Harlow | 17 | | Kowalski | 1 | | Eva | 15 | | London | 1 | | Underground | 1 | | Compass | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Miller" | | 2 | "Market" | | 3 | "Harlow" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | | | globalScore | 0.494 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a primary school teacher who" |
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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 | 1369 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 153 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 71 | | mean | 19.28 | | std | 14.1 | | cv | 0.731 | | sampleLengths | | 0 | 13 | | 1 | 51 | | 2 | 27 | | 3 | 49 | | 4 | 60 | | 5 | 25 | | 6 | 11 | | 7 | 37 | | 8 | 42 | | 9 | 2 | | 10 | 2 | | 11 | 6 | | 12 | 34 | | 13 | 13 | | 14 | 37 | | 15 | 5 | | 16 | 10 | | 17 | 24 | | 18 | 2 | | 19 | 50 | | 20 | 3 | | 21 | 30 | | 22 | 14 | | 23 | 11 | | 24 | 42 | | 25 | 13 | | 26 | 9 | | 27 | 31 | | 28 | 6 | | 29 | 19 | | 30 | 18 | | 31 | 8 | | 32 | 24 | | 33 | 35 | | 34 | 10 | | 35 | 23 | | 36 | 7 | | 37 | 10 | | 38 | 25 | | 39 | 4 | | 40 | 29 | | 41 | 30 | | 42 | 4 | | 43 | 1 | | 44 | 4 | | 45 | 36 | | 46 | 4 | | 47 | 4 | | 48 | 18 | | 49 | 20 |
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| 95.43% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 107 | | matches | | 0 | "were relaxed" | | 1 | "were blackened" | | 2 | "was locked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 168 | | matches | | 0 | "was shaking" | | 1 | "was shifting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 153 | | ratio | 0.013 | | matches | | 0 | "She looked like a primary school teacher who had taken a wrong turn into a war zone—round glasses perched on a freckled nose, curly red hair escaping a messy bun, and a worn leather satchel clutched to her chest like a shield." | | 1 | "They were entirely black—no iris, no sclera." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 948 | | adjectiveStacks | 1 | | stackExamples | | 0 | "sudden, fierce black light." |
| | adverbCount | 32 | | adverbRatio | 0.03375527426160337 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.016877637130801686 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 153 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 153 | | mean | 8.95 | | std | 6.72 | | cv | 0.751 | | sampleLengths | | 0 | 13 | | 1 | 19 | | 2 | 20 | | 3 | 12 | | 4 | 23 | | 5 | 4 | | 6 | 20 | | 7 | 29 | | 8 | 4 | | 9 | 16 | | 10 | 9 | | 11 | 31 | | 12 | 8 | | 13 | 17 | | 14 | 6 | | 15 | 5 | | 16 | 15 | | 17 | 22 | | 18 | 5 | | 19 | 19 | | 20 | 11 | | 21 | 7 | | 22 | 2 | | 23 | 2 | | 24 | 6 | | 25 | 8 | | 26 | 7 | | 27 | 10 | | 28 | 2 | | 29 | 7 | | 30 | 8 | | 31 | 5 | | 32 | 22 | | 33 | 7 | | 34 | 8 | | 35 | 5 | | 36 | 10 | | 37 | 8 | | 38 | 3 | | 39 | 7 | | 40 | 6 | | 41 | 2 | | 42 | 8 | | 43 | 42 | | 44 | 3 | | 45 | 26 | | 46 | 4 | | 47 | 8 | | 48 | 6 | | 49 | 6 |
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| 44.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.35294117647058826 | | totalSentences | 153 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 95 | | matches | | 0 | "Light, hesitant, weighed down by" | | 1 | "Instead, a series of thin," | | 2 | "Slowly, imperceptibly, the ink was" | | 3 | "Just endless void." |
| | ratio | 0.042 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 95 | | matches | | 0 | "She stepped over the rusted" | | 1 | "She adjusted her cuffs, the" | | 2 | "They rounded the bend." | | 3 | "His skin was pale, greyish," | | 4 | "It was the grey of" | | 5 | "He reached into the victim's" | | 6 | "He pulled out a small," | | 7 | "It wafted from the victim's" | | 8 | "She knew the rhythm of" | | 9 | "She looked like a primary" | | 10 | "She opened her satchel and" | | 11 | "She held the compass over" | | 12 | "She snapped it on." | | 13 | "He grabbed the victim's wrist." | | 14 | "He held it for a" | | 15 | "His brow furrowed." | | 16 | "He moved his fingers to" | | 17 | "She stared at the map" | | 18 | "It didn't disperse." | | 19 | "It swirled, forming a vortex" |
| | ratio | 0.274 | |
| 2.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 95 | | matches | | 0 | "Harlow Quinn didn't break stride," | | 1 | "She stepped over the rusted" | | 2 | "The air tasted of wet" | | 3 | "She adjusted her cuffs, the" | | 4 | "DCS Miller huffed behind her," | | 5 | "They rounded the bend." | | 6 | "The Veil Market lived in" | | 7 | "Tonight, it had claimed the" | | 8 | "The crime scene team had" | | 9 | "Miller said, consulting his notebook" | | 10 | "Harlow ducked under the police" | | 11 | "Miller gestured vaguely at the" | | 12 | "Harlow crouched beside the body." | | 13 | "The man lay on his" | | 14 | "His skin was pale, greyish," | | 15 | "It was the grey of" | | 16 | "Miller frowned but knelt, his" | | 17 | "He reached into the victim's" | | 18 | "He pulled out a small," | | 19 | "Miller dropped it into an" |
| | ratio | 0.916 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "She looked like a primary school teacher who had taken a wrong turn into a war zone—round glasses perched on a freckled nose, curly red hair escaping a messy bu…" | | 1 | "It was a wide, terrible expression that stretched the facial muscles too far." | | 2 | "He moved faster than any human should, scrambling across the floor like a spider, his limbs bending at wrong angles, coming straight for Harlow." |
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| 28.85% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 5 | | matches | | 0 | "Eva tucked, her eyes darting to the body" | | 1 | "Eva whispered, her voice trembling slightly" | | 2 | "Eva looked up, her green eyes wide" | | 3 | "Eva said, her voice dropping an octave" | | 4 | "Eva looked around, her gaze darting into the darkness beyond the police lights" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 8 | | fancyTags | | 0 | "Eva whispered (whisper)" | | 1 | "Miller suggested (suggest)" | | 2 | "Miller muttered (mutter)" | | 3 | "Harlow corrected (correct)" | | 4 | "Miller pressed (press)" | | 5 | "Eva yelled (yell)" | | 6 | "Miller stammered (stammer)" | | 7 | "Eva shrieked (shriek)" |
| | dialogueSentences | 72 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.667 | | effectiveRatio | 0.222 | |