| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1262 | | totalAiIsmAdverbs | 3 | | 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) | |
| 20.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1262 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "rhythmic" | | 1 | "chill" | | 2 | "shattered" | | 3 | "measured" | | 4 | "chaotic" | | 5 | "tinged" | | 6 | "mechanical" | | 7 | "echoed" | | 8 | "footsteps" | | 9 | "echoing" | | 10 | "structure" | | 11 | "profound" | | 12 | "maw" | | 13 | "stark" | | 14 | "standard" | | 15 | "gloom" | | 16 | "silk" | | 17 | "velvet" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 71 | | matches | | |
| 62.37% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 71 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1261 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 95.97% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1203 | | uniqueNames | 24 | | maxNameDensity | 1.08 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Quinn | 2 | | Metropolitan | 1 | | Police | 1 | | Herrera | 6 | | Soho | 1 | | Victorian | 2 | | Northern | 1 | | Line | 1 | | Tube | 1 | | TfL | 1 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | Detective | 1 | | Sergeant | 1 | | Morris | 1 | | Whitechapel | 1 | | Harlow | 13 | | Veil | 1 | | Market | 1 | | London | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Herrera" | | 3 | "Victorian" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Sergeant" | | 7 | "Morris" | | 8 | "Harlow" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Soho" | | 4 | "Seville" | | 5 | "Veil" | | 6 | "London" |
| | globalScore | 0.96 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | 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 | 1261 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 38.21 | | std | 19.56 | | cv | 0.512 | | sampleLengths | | 0 | 42 | | 1 | 65 | | 2 | 64 | | 3 | 67 | | 4 | 20 | | 5 | 23 | | 6 | 51 | | 7 | 20 | | 8 | 47 | | 9 | 15 | | 10 | 71 | | 11 | 48 | | 12 | 57 | | 13 | 12 | | 14 | 39 | | 15 | 44 | | 16 | 35 | | 17 | 21 | | 18 | 14 | | 19 | 47 | | 20 | 79 | | 21 | 37 | | 22 | 9 | | 23 | 45 | | 24 | 13 | | 25 | 60 | | 26 | 28 | | 27 | 6 | | 28 | 57 | | 29 | 41 | | 30 | 26 | | 31 | 32 | | 32 | 26 |
| |
| 95.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 71 | | matches | | 0 | "were swallowed" | | 1 | "was frozen" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 168 | | matches | | |
| 66.67% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 75 | | ratio | 0.027 | | matches | | 0 | "He reached into his coat pocket and pulled out a small, pale object no larger than a pocket watch—a smooth piece of carved white bone." | | 1 | "It wasn't the sound of an approaching train; it was the slow, wet grind of stone slipping over stone, accompanied by a sudden puff of displaced air." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1220 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short, curly dark hair" |
| | adverbCount | 14 | | adverbRatio | 0.011475409836065573 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009016393442622951 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 16.81 | | std | 8.66 | | cv | 0.515 | | sampleLengths | | 0 | 29 | | 1 | 13 | | 2 | 18 | | 3 | 27 | | 4 | 20 | | 5 | 20 | | 6 | 2 | | 7 | 30 | | 8 | 12 | | 9 | 11 | | 10 | 21 | | 11 | 35 | | 12 | 17 | | 13 | 3 | | 14 | 3 | | 15 | 20 | | 16 | 17 | | 17 | 34 | | 18 | 20 | | 19 | 15 | | 20 | 18 | | 21 | 14 | | 22 | 15 | | 23 | 11 | | 24 | 13 | | 25 | 24 | | 26 | 23 | | 27 | 22 | | 28 | 12 | | 29 | 14 | | 30 | 15 | | 31 | 9 | | 32 | 14 | | 33 | 19 | | 34 | 8 | | 35 | 4 | | 36 | 3 | | 37 | 25 | | 38 | 11 | | 39 | 17 | | 40 | 27 | | 41 | 15 | | 42 | 20 | | 43 | 3 | | 44 | 18 | | 45 | 5 | | 46 | 9 | | 47 | 22 | | 48 | 10 | | 49 | 15 |
| |
| 62.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.41333333333333333 | | totalSentences | 75 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 70.75% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 67 | | matches | | 0 | "She ran with a rhythmic," | | 1 | "She pulled a deep breath" | | 2 | "She had been trailing Herrera" | | 3 | "her voice cut through the" | | 4 | "He didn't stop." | | 5 | "He cut sharply to the" | | 6 | "She rounded the corner expecting" | | 7 | "She stepped through the gate," | | 8 | "He was gasping for air," | | 9 | "She took two slow, measured" | | 10 | "His voice was breathless, tinged" | | 11 | "He reached into his coat" | | 12 | "He thrust it downward, toward" | | 13 | "It wasn't the sound of" | | 14 | "It smelled of scorched cedar," | | 15 | "He plunged down into the" | | 16 | "Her hand tightened on the" | | 17 | "You don't push into an" | | 18 | "You call for a perimeter," | | 19 | "She looked down into the" |
| | ratio | 0.373 | |
| 49.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 67 | | matches | | 0 | "Rain fell in cold, heavy" | | 1 | "Harlow Quinn kept her eyes" | | 2 | "She ran with a rhythmic," | | 3 | "She pulled a deep breath" | | 4 | "Harlow checked her left wrist" | | 5 | "The worn leather strap of" | | 6 | "She had been trailing Herrera" | | 7 | "her voice cut through the" | | 8 | "He didn't stop." | | 9 | "He cut sharply to the" | | 10 | "Harlow closed the distance, her" | | 11 | "She rounded the corner expecting" | | 12 | "Harlow slowed her pace, her" | | 13 | "Rain dripped from her closely" | | 14 | "She stepped through the gate," | | 15 | "The amber glow of a" | | 16 | "He was gasping for air," | | 17 | "The movement pulled his left" | | 18 | "Harlow said, her voice dropping" | | 19 | "She took two slow, measured" |
| | ratio | 0.821 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 64.94% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 6 | | matches | | 0 | "Rain fell in cold, heavy sheets across Camden High Street, turning the asphalt into a black mirror that smeared the red tail lights of idling buses into bloody …" | | 1 | "She ran with a rhythmic, military economy of motion, her boots striking the flooded gutters without losing balance." | | 2 | "Twenty-nine years old, olive skin, five-foot-ten, moving with the agile, erratic desperation of a man who knew precisely what happened to unauthorized medics wh…" | | 3 | "The rain slammed down on her back, hammering a relentless rhythm against the asphalt behind her, urging her toward the mundane safety of the lit streets." | | 4 | "A beam of stark white light sliced through the darkness, revealing water-stained white tiles, rotting peeling posters from forty years ago, and stairs that desc…" | | 5 | "The temperature plummeted with every stair she cleared, but the dampness changed from the clean chill of London rain to a thick, greasy humidity that coated her…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |