| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.438 | | leniency | 0.875 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.27% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1486 | | totalAiIsmAdverbs | 2 | | 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) | |
| 29.34% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1486 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "maw" | | 1 | "pulse" | | 2 | "echoing" | | 3 | "could feel" | | 4 | "intensity" | | 5 | "gloom" | | 6 | "silk" | | 7 | "footsteps" | | 8 | "rhythmic" | | 9 | "chaotic" | | 10 | "velvet" | | 11 | "glinting" | | 12 | "sanctuary" | | 13 | "calculated" | | 14 | "scanning" | | 15 | "vibrated" | | 16 | "glistening" | | 17 | "hulking" | | 18 | "familiar" |
| |
| 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 | 99 | | matches | (empty) | |
| 85.14% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1486 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 80.95% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1231 | | uniqueNames | 16 | | maxNameDensity | 1.38 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Adjusted | 1 | | Underground | 1 | | Victorian | 1 | | Morris | 1 | | Tube | 2 | | Veil | 1 | | Market | 2 | | Saint | 1 | | Christopher | 1 | | Tomás | 6 | | Herrera | 1 | | Quinn | 17 | | Metropolitan | 1 | | Police | 1 | | Pip | 1 | | Closing | 1 |
| | persons | | 0 | "Adjusted" | | 1 | "Morris" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Quinn" | | 7 | "Pip" |
| | places | | | globalScore | 0.81 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a man but moved with too many" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.673 | | wordCount | 1486 | | matches | | 0 | "Not into a basement, but deeper, past the level of sewers" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 33.77 | | std | 21.04 | | cv | 0.623 | | sampleLengths | | 0 | 65 | | 1 | 74 | | 2 | 3 | | 3 | 34 | | 4 | 59 | | 5 | 21 | | 6 | 37 | | 7 | 2 | | 8 | 43 | | 9 | 20 | | 10 | 83 | | 11 | 60 | | 12 | 50 | | 13 | 50 | | 14 | 11 | | 15 | 61 | | 16 | 45 | | 17 | 31 | | 18 | 2 | | 19 | 49 | | 20 | 18 | | 21 | 38 | | 22 | 10 | | 23 | 59 | | 24 | 20 | | 25 | 30 | | 26 | 73 | | 27 | 10 | | 28 | 48 | | 29 | 14 | | 30 | 38 | | 31 | 22 | | 32 | 24 | | 33 | 39 | | 34 | 13 | | 35 | 8 | | 36 | 4 | | 37 | 48 | | 38 | 25 | | 39 | 28 | | 40 | 15 | | 41 | 33 | | 42 | 46 | | 43 | 23 |
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| 98.17% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 99 | | matches | | 0 | "been snapped" | | 1 | "were gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 201 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 116 | | ratio | 0 | | matches | (empty) | |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1239 | | adjectiveStacks | 3 | | stackExamples | | 0 | "small jagged white bone" | | 1 | "cracked white ceramic tiles," | | 2 | "long thin jagged scar" |
| | adverbCount | 29 | | adverbRatio | 0.023405972558514933 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.009685230024213076 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 12.81 | | std | 7.39 | | cv | 0.577 | | sampleLengths | | 0 | 24 | | 1 | 20 | | 2 | 3 | | 3 | 18 | | 4 | 20 | | 5 | 25 | | 6 | 9 | | 7 | 20 | | 8 | 3 | | 9 | 6 | | 10 | 14 | | 11 | 14 | | 12 | 17 | | 13 | 8 | | 14 | 23 | | 15 | 11 | | 16 | 21 | | 17 | 22 | | 18 | 15 | | 19 | 2 | | 20 | 7 | | 21 | 15 | | 22 | 6 | | 23 | 15 | | 24 | 20 | | 25 | 7 | | 26 | 9 | | 27 | 7 | | 28 | 31 | | 29 | 5 | | 30 | 6 | | 31 | 18 | | 32 | 8 | | 33 | 3 | | 34 | 9 | | 35 | 15 | | 36 | 9 | | 37 | 16 | | 38 | 13 | | 39 | 1 | | 40 | 9 | | 41 | 7 | | 42 | 20 | | 43 | 4 | | 44 | 14 | | 45 | 14 | | 46 | 18 | | 47 | 11 | | 48 | 20 | | 49 | 11 |
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| 53.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3620689655172414 | | totalSentences | 116 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 93 | | matches | | 0 | "Instead, he shoved a small" | | 1 | "Dimly lit by flickering orange" | | 2 | "Slowly, every head turned." |
| | ratio | 0.032 | |
| 82.37% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 93 | | matches | | 0 | "She didn't shout." | | 1 | "Her prey was fast, but" | | 2 | "He leaped over a sprawling" | | 3 | "He scrambled toward a rusted" | | 4 | "She lunged, catching the heel" | | 5 | "They hit the ground in" | | 6 | "He bucked beneath her, a" | | 7 | "He didn't reach for a" | | 8 | "It wasn't the rumble of" | | 9 | "It felt deeper, ringing in" | | 10 | "He didn't use a key." | | 11 | "He didn't even turn the" | | 12 | "He simply fell through the" | | 13 | "It didn't budge." | | 14 | "She stepped back, hand hovering" | | 15 | "She could feel it in" | | 16 | "She circled the warehouse, her" | | 17 | "She descended a long narrow" | | 18 | "It was an abandoned Tube" | | 19 | "She watched a tall thin" |
| | ratio | 0.344 | |
| 24.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 93 | | matches | | 0 | "Quinn’s boot skidded on a" | | 1 | "The lean young hooded suspect" | | 2 | "She didn't shout." | | 3 | "Shouting wasted oxygen, and her" | | 4 | "The heavy leather watch on" | | 5 | "Her prey was fast, but" | | 6 | "Quinn Adjusted her pace, her" | | 7 | "The suspect didn't even look" | | 8 | "He leaped over a sprawling" | | 9 | "He scrambled toward a rusted" | | 10 | "Quinn reached the base of" | | 11 | "She lunged, catching the heel" | | 12 | "The boy kicked out, a" | | 13 | "They hit the ground in" | | 14 | "Quinn pinned his shoulder with" | | 15 | "the boy wheezed, his eyes" | | 16 | "The boy’s face paled at" | | 17 | "He bucked beneath her, a" | | 18 | "He didn't reach for a" | | 19 | "Quinn gripped his chin, trying" |
| | ratio | 0.871 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 93 | | matches | | 0 | "To her left, a woman" | | 1 | "To her right, a stall" |
| | ratio | 0.022 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 6 | | matches | | 0 | "The boy kicked out, a desperate wild strike that grazed her cheek, but she held on, hauling him back down to the concrete." | | 1 | "He bucked beneath her, a sudden surge of panicked strength that caught her off guard." | | 2 | "She watched a tall thin merchant in a tattered purple velvet coat weigh out shimmering silver dust for a creature that looked like a man but moved with too many…" | | 3 | "He was younger than her, with short curly dark brown hair and olive skin that caught the lamplight." | | 4 | "Her heart hammered against her ribs, a frantic drumming that threatened to crack her sternum." | | 5 | "From the shadows behind the stalls, three hulking figures in grey stone armour emerged, their footsteps shaking the platform." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 4 | | matches | | 0 | "she yelled, her voice cutting through the market’s hum" | | 1 | "the merchant said, his voice sounding like dry leaves skittering over a tombstone" | | 2 | "The merchant leaned, the light finally hitting his face" | | 3 | "Tomás said, his voice tight" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 6 | | fancyTags | | 0 | "the boy wheezed (wheeze)" | | 1 | "she yelled (yell)" | | 2 | "someone whispered (whisper)" | | 3 | "Tomás hissed (hiss)" | | 4 | "the merchant croaked (croak)" | | 5 | "Tomás yelled (yell)" |
| | dialogueSentences | 32 | | tagDensity | 0.281 | | leniency | 0.563 | | rawRatio | 0.667 | | effectiveRatio | 0.375 | |