| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1594 | | 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) | |
| 59.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1594 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "footsteps" | | 1 | "gloom" | | 2 | "echoed" | | 3 | "echoing" | | 4 | "flickered" | | 5 | "velvet" | | 6 | "silk" | | 7 | "scanned" | | 8 | "silence" | | 9 | "charged" | | 10 | "pulsed" | | 11 | "lurched" |
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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 | 1 | | narrationSentences | 165 | | matches | | |
| 99.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 165 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 186 | | 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 | 2 | | markdownWords | 2 | | totalWords | 1575 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 56.20% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1386 | | uniqueNames | 13 | | maxNameDensity | 1.88 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 26 | | Herrera | 17 | | London | 1 | | Thatcher | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Two | 1 | | God | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Market" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "London" | | 4 | "Two" | | 5 | "God" |
| | globalScore | 0.562 | | windowScore | 0.667 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | glossingSentenceCount | 6 | | matches | | 0 | "tasted like a battery on the tongue" | | 1 | "looked like tree bark" | | 2 | "looked like a hairless monkey with the fa" | | 3 | "sounded like rocks grinding together" | | 4 | "corridor that seemed to breathe" | | 5 | "felt like warm meat" |
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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 | 1575 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 186 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 21.88 | | std | 13.99 | | cv | 0.64 | | sampleLengths | | 0 | 39 | | 1 | 27 | | 2 | 14 | | 3 | 42 | | 4 | 45 | | 5 | 23 | | 6 | 33 | | 7 | 1 | | 8 | 3 | | 9 | 33 | | 10 | 56 | | 11 | 6 | | 12 | 32 | | 13 | 26 | | 14 | 5 | | 15 | 20 | | 16 | 52 | | 17 | 9 | | 18 | 49 | | 19 | 6 | | 20 | 44 | | 21 | 3 | | 22 | 24 | | 23 | 34 | | 24 | 16 | | 25 | 47 | | 26 | 1 | | 27 | 22 | | 28 | 29 | | 29 | 35 | | 30 | 23 | | 31 | 28 | | 32 | 7 | | 33 | 2 | | 34 | 27 | | 35 | 2 | | 36 | 13 | | 37 | 43 | | 38 | 11 | | 39 | 21 | | 40 | 26 | | 41 | 17 | | 42 | 3 | | 43 | 19 | | 44 | 17 | | 45 | 10 | | 46 | 31 | | 47 | 19 | | 48 | 6 | | 49 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 165 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 250 | | matches | | |
| 96.77% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 186 | | ratio | 0.016 | | matches | | 0 | "Low murmurs drifted toward her—the hum of commerce, but wrong." | | 1 | "He was arguing with a stall owner—a woman with eyes entirely devoid of irises." | | 2 | "It—he—wore a butcher's apron over a torso that was sheer slab muscle." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1405 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint, sick-green luminescence" |
| | adverbCount | 33 | | adverbRatio | 0.023487544483985764 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.00498220640569395 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 186 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 186 | | mean | 8.47 | | std | 5.22 | | cv | 0.617 | | sampleLengths | | 0 | 19 | | 1 | 9 | | 2 | 11 | | 3 | 6 | | 4 | 2 | | 5 | 19 | | 6 | 14 | | 7 | 4 | | 8 | 2 | | 9 | 14 | | 10 | 14 | | 11 | 8 | | 12 | 7 | | 13 | 20 | | 14 | 3 | | 15 | 15 | | 16 | 15 | | 17 | 3 | | 18 | 5 | | 19 | 11 | | 20 | 7 | | 21 | 9 | | 22 | 6 | | 23 | 1 | | 24 | 3 | | 25 | 12 | | 26 | 3 | | 27 | 2 | | 28 | 16 | | 29 | 6 | | 30 | 11 | | 31 | 8 | | 32 | 15 | | 33 | 6 | | 34 | 10 | | 35 | 6 | | 36 | 3 | | 37 | 5 | | 38 | 17 | | 39 | 7 | | 40 | 8 | | 41 | 18 | | 42 | 3 | | 43 | 2 | | 44 | 8 | | 45 | 12 | | 46 | 21 | | 47 | 18 | | 48 | 5 | | 49 | 1 |
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| 58.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3763440860215054 | | totalSentences | 186 | | uniqueOpeners | 70 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 150 | | matches | | 0 | "Just a grate covered in" | | 1 | "right before the thing in" | | 2 | "Too many joints in the" |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 150 | | matches | | 0 | "She focused on the splash" | | 1 | "His trainers squeaked against the" | | 2 | "He cleared a pile of" | | 3 | "It always did." | | 4 | "She adjusted the grip on" | | 5 | "She flattened herself against the" | | 6 | "She sliced the corner, baton" | | 7 | "She stepped deeper into the" | | 8 | "It tasted like a battery" | | 9 | "Her left wrist began to" | | 10 | "It was a service hatch," | | 11 | "She holstered the baton and" | | 12 | "She grabbed the cold rungs" | | 13 | "She moved by memory and" | | 14 | "She rounded the corner and" | | 15 | "She watched a figure in" | | 16 | "It was an invasion." | | 17 | "She scanned the crowd." | | 18 | "He was arguing with a" | | 19 | "He slapped a hand on" |
| | ratio | 0.273 | |
| 20.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 132 | | totalSentences | 150 | | matches | | 0 | "Rain slicked the cobbles of" | | 1 | "Detective Harlow Quinn ignored the" | | 2 | "She focused on the splash" | | 3 | "The suspect took the corner" | | 4 | "His trainers squeaked against the" | | 5 | "Quinn’s voice was a rusted" | | 6 | "The man didn't slow." | | 7 | "He cleared a pile of" | | 8 | "A hooded jacket flapped behind" | | 9 | "Quinn forced her legs to" | | 10 | "It always did." | | 11 | "She adjusted the grip on" | | 12 | "Herrera ducked into an alleyway" | | 13 | "A dead end." | | 14 | "Quinn skidded to a halt" | | 15 | "She flattened herself against the" | | 16 | "The rain hammered against metal" | | 17 | "She sliced the corner, baton" | | 18 | "The alley terminated in a" | | 19 | "She stepped deeper into the" |
| | ratio | 0.88 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 150 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 1 | | matches | | 0 | "It—he—wore a butcher's apron over a torso that was sheer slab muscle." |
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| 97.22% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 1 | | matches | | 0 | "the woman shrieked, her voice shattering a nearby glass lantern" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 8 | | fancyTags | | 0 | "she hissed (hiss)" | | 1 | "Quinn muttered (mutter)" | | 2 | "the bull-man grunted (grunt)" | | 3 | "a voice screeched (screech)" | | 4 | "he whispered (whisper)" | | 5 | "the woman shrieked (shriek)" | | 6 | "Quinn wheezed (wheeze)" | | 7 | "Quinn spat (spit)" |
| | dialogueSentences | 40 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.571 | | effectiveRatio | 0.4 | |