| 38.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" |
| | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.25 | | effectiveRatio | 0.222 | |
| 94.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 845 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 845 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "footsteps" | | 1 | "efficient" | | 2 | "scanning" | | 3 | "beacon" | | 4 | "gloom" | | 5 | "weight" | | 6 | "familiar" | | 7 | "charged" | | 8 | "shimmered" | | 9 | "glint" | | 10 | "flicker" | | 11 | "etched" | | 12 | "pulse" | | 13 | "palpable" | | 14 | "sense of" | | 15 | "silence" | | 16 | "intensity" | | 17 | "whisper" | | 18 | "potential" | | 19 | "treacherous" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 87.12% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 44 | | matches | | 0 | "a flicker of surprise" | | 1 | "felt a prickle" |
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| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 44 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 49 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 837 | | ratio | 0.004 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 762 | | uniqueNames | 13 | | maxNameDensity | 0.92 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 7 | | Soho | 1 | | Tomás | 5 | | Herrera | 1 | | Raven | 1 | | Veil | 3 | | Market | 3 | | Camden | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Market" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | 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 | 837 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 49 | | matches | (empty) | |
| 87.19% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 11 | | mean | 76.09 | | std | 34.63 | | cv | 0.455 | | sampleLengths | | 0 | 129 | | 1 | 114 | | 2 | 108 | | 3 | 89 | | 4 | 101 | | 5 | 31 | | 6 | 51 | | 7 | 23 | | 8 | 36 | | 9 | 83 | | 10 | 72 |
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| 73.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 44 | | matches | | 0 | "was plastered" | | 1 | "been pressed" | | 2 | "were lined" | | 3 | "were written" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 106 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 49 | | ratio | 0.082 | | matches | | 0 | "The air was thick with the smell of damp stone, ozone, and something else—something sweet and metallic, like blood and ozone mixed." | | 1 | "She kept her pistol steady, her gaze sweeping the market, taking in the unfamiliar faces—humans with eyes that held too much knowledge, creatures that shimmered at the edges of perception, and the palpable sense of danger that hung like a shroud." | | 2 | "This wasn't just a physical chase anymore; it was a confrontation with forces beyond her understanding." | | 3 | "Follow him deeper into this treacherous, supernatural underworld, where the rules were written in blood and bone, or turn back and risk losing him—and the truth—forever." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 770 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short, curly dark hair" |
| | adverbCount | 19 | | adverbRatio | 0.024675324675324677 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.01038961038961039 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 49 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 49 | | mean | 17.08 | | std | 8.94 | | cv | 0.523 | | sampleLengths | | 0 | 21 | | 1 | 25 | | 2 | 27 | | 3 | 25 | | 4 | 31 | | 5 | 20 | | 6 | 28 | | 7 | 12 | | 8 | 29 | | 9 | 10 | | 10 | 15 | | 11 | 3 | | 12 | 5 | | 13 | 26 | | 14 | 15 | | 15 | 22 | | 16 | 37 | | 17 | 24 | | 18 | 15 | | 19 | 18 | | 20 | 28 | | 21 | 4 | | 22 | 4 | | 23 | 41 | | 24 | 30 | | 25 | 19 | | 26 | 7 | | 27 | 10 | | 28 | 21 | | 29 | 4 | | 30 | 17 | | 31 | 17 | | 32 | 13 | | 33 | 13 | | 34 | 10 | | 35 | 16 | | 36 | 14 | | 37 | 6 | | 38 | 6 | | 39 | 16 | | 40 | 16 | | 41 | 15 | | 42 | 16 | | 43 | 14 | | 44 | 16 | | 45 | 5 | | 46 | 26 | | 47 | 18 | | 48 | 7 |
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| 40.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.30612244897959184 | | totalSentences | 49 | | uniqueOpeners | 15 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 44 | | matches | (empty) | | ratio | 0 | |
| 65.45% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 44 | | matches | | 0 | "Her salt-and-pepper hair, cropped close" | | 1 | "She’d been chasing him for" | | 2 | "Her military precision kept her" | | 3 | "He’d vanished into the mouth" | | 4 | "She pushed past a sagging" | | 5 | "He turned, his eyes meeting" | | 6 | "he said, his voice low" | | 7 | "She kept her pistol steady," | | 8 | "She couldn't let Tomás slip" | | 9 | "she said, her voice cutting" | | 10 | "He didn't answer immediately." | | 11 | "He looked past her, towards" | | 12 | "His voice held a dangerous" | | 13 | "He met her gaze, his" | | 14 | "He gestured vaguely at the" | | 15 | "Her partner’s ghost seemed to" | | 16 | "She looked back at Tomás," |
| | ratio | 0.386 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 44 | | matches | | 0 | "The rain lashed the London" | | 1 | "Detective Harlow Quinn’s breath misted" | | 2 | "Her salt-and-pepper hair, cropped close" | | 3 | "She’d been chasing him for" | | 4 | "Her military precision kept her" | | 5 | "He’d vanished into the mouth" | | 6 | "Quinn followed, her boots splashing" | | 7 | "The alley ended abruptly at" | | 8 | "She pushed past a sagging" | | 9 | "The air grew colder, thicker," | | 10 | "The Veil Market." | | 11 | "Tomás had led her here." | | 12 | "The bone token, a small," | | 13 | "The air was thick with" | | 14 | "The walls were lined with" | | 15 | "Tomás stood near the center" | | 16 | "A Saint Christopher medallion, cold" | | 17 | "He turned, his eyes meeting" | | 18 | "he said, his voice low" | | 19 | "Quinn didn't move closer." |
| | ratio | 0.909 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 44 | | matches | | 0 | "Now, the entrance yawned open," |
| | ratio | 0.023 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "She’d been chasing him for blocks, the slick asphalt swallowing her footsteps, the neon glow of Soho’s signs blurring into streaks of color behind her." | | 1 | "Quinn followed, her boots splashing in a shallow puddle that reflected the flickering sign of *The Raven’s Nest*, its distinctive green neon sign a beacon in th…" | | 2 | "She kept her pistol steady, her gaze sweeping the market, taking in the unfamiliar faces—humans with eyes that held too much knowledge, creatures that shimmered…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice cutting through the damp silence" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |