| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.611 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 969 | | 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) | |
| 1.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 969 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "rhythmic" | | 1 | "gloom" | | 2 | "glint" | | 3 | "spectral" | | 4 | "etched" | | 5 | "scanned" | | 6 | "flicker" | | 7 | "unreadable" | | 8 | "stark" | | 9 | "chill" | | 10 | "charged" | | 11 | "whisper" | | 12 | "unspoken" | | 13 | "raced" | | 14 | "shattered" | | 15 | "depths" | | 16 | "maw" | | 17 | "oppressive" |
| |
| 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 | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 73 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 5 | | totalWords | 968 | | ratio | 0.005 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 838 | | uniqueNames | 11 | | maxNameDensity | 1.79 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 15 | | Raven | 1 | | Nest | 1 | | Herrera | 10 | | Saint | 1 | | Christopher | 1 | | Veil | 3 | | Market | 3 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | | globalScore | 0.605 | | windowScore | 0.667 | |
| 60.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 2 | | matches | | 0 | "quite fit the conventional medical world" | | 1 | "looked like any other collection of dusty" |
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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 | 968 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 44 | | std | 34.53 | | cv | 0.785 | | sampleLengths | | 0 | 121 | | 1 | 92 | | 2 | 72 | | 3 | 12 | | 4 | 30 | | 5 | 36 | | 6 | 80 | | 7 | 10 | | 8 | 15 | | 9 | 70 | | 10 | 51 | | 11 | 22 | | 12 | 18 | | 13 | 21 | | 14 | 15 | | 15 | 15 | | 16 | 16 | | 17 | 86 | | 18 | 94 | | 19 | 11 | | 20 | 6 | | 21 | 75 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 73 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 127 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 82 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 212 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.018867924528301886 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0047169811320754715 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 11.8 | | std | 6.48 | | cv | 0.549 | | sampleLengths | | 0 | 18 | | 1 | 21 | | 2 | 24 | | 3 | 15 | | 4 | 12 | | 5 | 13 | | 6 | 13 | | 7 | 5 | | 8 | 6 | | 9 | 3 | | 10 | 2 | | 11 | 17 | | 12 | 15 | | 13 | 21 | | 14 | 15 | | 15 | 13 | | 16 | 15 | | 17 | 5 | | 18 | 1 | | 19 | 6 | | 20 | 11 | | 21 | 14 | | 22 | 20 | | 23 | 12 | | 24 | 23 | | 25 | 7 | | 26 | 9 | | 27 | 20 | | 28 | 7 | | 29 | 4 | | 30 | 7 | | 31 | 9 | | 32 | 17 | | 33 | 23 | | 34 | 11 | | 35 | 9 | | 36 | 10 | | 37 | 7 | | 38 | 8 | | 39 | 4 | | 40 | 14 | | 41 | 13 | | 42 | 25 | | 43 | 14 | | 44 | 7 | | 45 | 3 | | 46 | 13 | | 47 | 16 | | 48 | 8 | | 49 | 4 |
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| 67.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4268292682926829 | | totalSentences | 82 | | uniqueOpeners | 35 | |
| 46.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 72 | | matches | | 0 | "Then, a glint of green" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 72 | | matches | | 0 | "Her flashlight beam cut through" | | 1 | "His warm brown eyes met" | | 2 | "He gestured with the glass" | | 3 | "Her gaze swept over the" | | 4 | "It looked like any other" | | 5 | "She'd heard whispers, urban legends" | | 6 | "She walked towards the bookshelf," | | 7 | "She ran a hand along" | | 8 | "Her partner, Morris, had mentioned" | | 9 | "She turned back to Herrera," | | 10 | "She couldn't let her quarry" | | 11 | "She glanced back at the" | | 12 | "She wouldn't let the supernatural" | | 13 | "She stepped through the opening," |
| | ratio | 0.194 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 72 | | matches | | 0 | "Rain lashed the cobblestone streets," | | 1 | "Detective Harlow Quinn’s breath plumed" | | 2 | "The suspect, a blur of" | | 3 | "Quinn sprinted, her military precision" | | 4 | "The alley was a narrow" | | 5 | "Her flashlight beam cut through" | | 6 | "The Raven's Nest." | | 7 | "Quinn pushed through the heavy" | | 8 | "The bar was dimly lit," | | 9 | "Patrons, cloaked figures and faces" | | 10 | "Quinn scanned the room, her" | | 11 | "The suspect wasn't here, not" | | 12 | "A figure emerged from behind" | | 13 | "Tomás Herrera, Quinn recognized him." | | 14 | "clientele, the ones who didn't" | | 15 | "His warm brown eyes met" | | 16 | "A Saint Christopher medallion rested" | | 17 | "Herrera said, his voice a" | | 18 | "Quinn kept her voice even," | | 19 | "The perp had vanished into" |
| | ratio | 0.875 | |
| 69.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 72 | | matches | | 0 | "before the unexplained circumstances." |
| | ratio | 0.014 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 4 | | matches | | 0 | "Eighteen years on the force, a decorated career, and here she was, chasing a shadow through a downpour that threatened to drown the city." | | 1 | "Patrons, cloaked figures and faces etched with a weariness that transcended the mundane, hunched over their drinks, their conversations hushed murmurs." | | 2 | "clientele, the ones who didn't quite fit the conventional medical world." | | 3 | "The hidden door swung shut behind her with a dull thud, plunging her into darkness, save for the beam of her flashlight slicing through the oppressive black." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 7 | | matches | | 0 | "Herrera said, his voice a low rumble" | | 1 | "Quinn asked, her voice hardening" | | 2 | "Quinn murmured, a memory surfacing" | | 3 | "Quinn began, her voice rough," | | 4 | "Herrera replied, his gaze dropping to the floor" | | 5 | "Quinn pressed, her eyes boring into him" | | 6 | "Quinn asked, her voice firming" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn murmured (murmur)" | | 1 | "Quinn pressed (press)" |
| | dialogueSentences | 18 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.333 | | effectiveRatio | 0.222 | |