| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 86.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1451 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "completely" | | 1 | "slightly" | | 2 | "really" |
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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) | |
| 51.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1451 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "clandestine" | | 1 | "weight" | | 2 | "pounding" | | 3 | "footsteps" | | 4 | "gloom" | | 5 | "flickered" | | 6 | "tension" | | 7 | "macabre" | | 8 | "silk" | | 9 | "shimmered" | | 10 | "chaotic" | | 11 | "echoed" | | 12 | "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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1443 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 80.70% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1443 | | uniqueNames | 21 | | maxNameDensity | 1.39 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 3 | | Harlow | 1 | | Quinn | 20 | | Camden | 2 | | Herrera | 11 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Metropolitan | 1 | | Police | 1 | | Detective | 2 | | Sergeant | 1 | | Morris | 3 | | Tube | 1 | | Spanish | 1 | | Seville | 1 | | Veil | 2 | | Market | 3 | | Jubilee | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Police" | | 7 | "Sergeant" | | 8 | "Morris" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "Soho" | | 3 | "Metropolitan" | | 4 | "Seville" |
| | globalScore | 0.807 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.693 | | wordCount | 1443 | | matches | | 0 | "not fully understand the supernatural underbelly of her city yet, but she knew Herrera touched it" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 43.73 | | std | 29.01 | | cv | 0.663 | | sampleLengths | | 0 | 71 | | 1 | 14 | | 2 | 2 | | 3 | 116 | | 4 | 43 | | 5 | 109 | | 6 | 42 | | 7 | 17 | | 8 | 86 | | 9 | 80 | | 10 | 11 | | 11 | 55 | | 12 | 40 | | 13 | 13 | | 14 | 50 | | 15 | 42 | | 16 | 21 | | 17 | 30 | | 18 | 58 | | 19 | 5 | | 20 | 72 | | 21 | 42 | | 22 | 7 | | 23 | 12 | | 24 | 58 | | 25 | 68 | | 26 | 28 | | 27 | 34 | | 28 | 62 | | 29 | 3 | | 30 | 44 | | 31 | 54 | | 32 | 54 |
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| 95.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 106 | | matches | | 0 | "was plastered" | | 1 | "been stripped" | | 2 | "were distracted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 218 | | matches | | 0 | "was doing" | | 1 | "was chasing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 106 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1451 | | adjectiveStacks | 1 | | stackExamples | | 0 | "massive, iron-wrought archway" |
| | adverbCount | 31 | | adverbRatio | 0.021364576154376293 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.012405237767057202 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 13.61 | | std | 7.54 | | cv | 0.554 | | sampleLengths | | 0 | 17 | | 1 | 12 | | 2 | 5 | | 3 | 21 | | 4 | 16 | | 5 | 14 | | 6 | 2 | | 7 | 18 | | 8 | 9 | | 9 | 19 | | 10 | 29 | | 11 | 25 | | 12 | 16 | | 13 | 19 | | 14 | 24 | | 15 | 12 | | 16 | 13 | | 17 | 18 | | 18 | 31 | | 19 | 35 | | 20 | 6 | | 21 | 4 | | 22 | 16 | | 23 | 16 | | 24 | 17 | | 25 | 11 | | 26 | 5 | | 27 | 3 | | 28 | 25 | | 29 | 15 | | 30 | 27 | | 31 | 6 | | 32 | 2 | | 33 | 20 | | 34 | 20 | | 35 | 9 | | 36 | 5 | | 37 | 18 | | 38 | 4 | | 39 | 7 | | 40 | 20 | | 41 | 25 | | 42 | 10 | | 43 | 13 | | 44 | 18 | | 45 | 9 | | 46 | 13 | | 47 | 15 | | 48 | 12 | | 49 | 6 |
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| 59.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3867924528301887 | | totalSentences | 106 | | uniqueOpeners | 41 | |
| 32.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 104 | | matches | | 0 | "Then a stray passing car" |
| | ratio | 0.01 | |
| 93.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 104 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She exhaled a tight, controlled" | | 2 | "She had been tracking the" | | 3 | "She had waited, patient and" | | 4 | "He was twenty-nine, a decade" | | 5 | "His chest heaved as he" | | 6 | "She did not hesitate." | | 7 | "She scaled the links with" | | 8 | "She could call for backup." | | 9 | "She thought of Detective Sergeant" | | 10 | "It had been three years" | | 11 | "She had seen the impossible" | | 12 | "She did not fully understand" | | 13 | "She lowered the radio." | | 14 | "She drew her service weapon," | | 15 | "She crept along the tiled" | | 16 | "They did not look entirely" | | 17 | "Their silhouettes were slightly too" | | 18 | "He reached into his pocket" | | 19 | "It was a piece of" |
| | ratio | 0.317 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 104 | | matches | | 0 | "The rain over London was" | | 1 | "Detective Harlow Quinn checked the" | | 2 | "Her closely cropped salt-and-pepper hair" | | 3 | "She exhaled a tight, controlled" | | 4 | "Quinn hit the corner without" | | 5 | "She had been tracking the" | | 6 | "The stakeout had begun clear" | | 7 | "Quinn had sat at the" | | 8 | "She had waited, patient and" | | 9 | "Whatever he was doing with" | | 10 | "He was twenty-nine, a decade" | | 11 | "The fabric tore, peeling back" | | 12 | "His chest heaved as he" | | 13 | "Quinn hit the fence seconds" | | 14 | "She did not hesitate." | | 15 | "She scaled the links with" | | 16 | "Herrera was already disappearing down" | | 17 | "Quinn unholstered her radio, her" | | 18 | "She could call for backup." | | 19 | "A simple perimeter." |
| | ratio | 0.75 | |
| 96.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 104 | | matches | | 0 | "If she lost him now," | | 1 | "If she brought uniforms down" |
| | ratio | 0.019 | |
| 82.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 6 | | matches | | 0 | "The rain over London was a relentless, freezing sheet that turned the streets into slick black mirrors." | | 1 | "She had waited, patient and motionless, until she saw him slip behind a heavy mahogany bookshelf that swung open to reveal a clandestine back room." | | 2 | "Herrera stood before a massive, iron-wrought archway that spanned the width of the train tunnel." | | 3 | "From his birthplace in Seville to the shadows of London, he had brought something with him, some knowledge that allowed him to bargain with monsters." | | 4 | "An underground network trading in enchanted goods, banned alchemical substances, and secrets that could warp a human mind." | | 5 | "Quinn pulled the collar of her soaked trench coat high up her neck, keeping her head down, her brown eyes tracking the chaotic crowd." |
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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 | |