| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.76% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1133 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "quickly" | | 1 | "sharply" | | 2 | "suddenly" |
| |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1133 | | totalAiIsms | 27 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | word | "practiced ease" | | count | 1 |
| | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | |
| | highlights | | 0 | "calculated" | | 1 | "determined" | | 2 | "flickered" | | 3 | "familiar" | | 4 | "weight" | | 5 | "pounding" | | 6 | "echoed" | | 7 | "racing" | | 8 | "practiced ease" | | 9 | "flicker" | | 10 | "dance" | | 11 | "predator" | | 12 | "clandestine" | | 13 | "cacophony" | | 14 | "unspoken" | | 15 | "loomed" | | 16 | "maw" | | 17 | "oppressive" | | 18 | "silence" | | 19 | "footsteps" | | 20 | "echoing" | | 21 | "resolve" | | 22 | "reminder" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "clenched jaw/fists" | | count | 1 |
| | 2 | | label | "without second thought" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "clenched her jaw" | | 2 | "Without second thought" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 104 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1126 | | ratio | 0 | | matches | (empty) | |
| 93.75% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 1 | | matches | | 0 | "A few more feet, she told herself." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1100 | | uniqueNames | 15 | | maxNameDensity | 1 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 11 | | Quinn | 2 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Tommy | 4 | | Camden | 1 | | Veil | 2 | | Market | 3 | | Tube | 1 | | Old | 1 | | Bailey | 1 | | Incident | 1 | | Follow | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Tommy" | | 5 | "Market" | | 6 | "Old" | | 7 | "Bailey" | | 8 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.888 | | wordCount | 1126 | | matches | | 0 | "not Tommy’s voice, but her old partner, Morris" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 40.21 | | std | 22.15 | | cv | 0.551 | | sampleLengths | | 0 | 84 | | 1 | 74 | | 2 | 49 | | 3 | 53 | | 4 | 55 | | 5 | 11 | | 6 | 22 | | 7 | 64 | | 8 | 58 | | 9 | 52 | | 10 | 60 | | 11 | 20 | | 12 | 61 | | 13 | 35 | | 14 | 44 | | 15 | 6 | | 16 | 59 | | 17 | 55 | | 18 | 26 | | 19 | 62 | | 20 | 6 | | 21 | 38 | | 22 | 15 | | 23 | 50 | | 24 | 10 | | 25 | 23 | | 26 | 25 | | 27 | 9 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 104 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 207 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 104 | | ratio | 0.058 | | matches | | 0 | "She couldn't lose him—not after all the effort it took to track him down." | | 1 | "Her senses heightened immediately—the mingling scents of enchantments, the hushed whispers of illicit deals." | | 2 | "A flicker of movement to her left—a waft of potent incense—threatened to distract her, but she stayed focused." | | 3 | "He was close to something—or someone." | | 4 | "A voice called from within her – not Tommy’s voice, but her old partner, Morris." | | 5 | "A flash of steel in the dim light—the knife thrusted towards her." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 692 | | adjectiveStacks | 1 | | stackExamples | | 0 | "infamous supernatural black market." |
| | adverbCount | 17 | | adverbRatio | 0.024566473988439308 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.011560693641618497 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 10.83 | | std | 5.08 | | cv | 0.469 | | sampleLengths | | 0 | 21 | | 1 | 13 | | 2 | 19 | | 3 | 17 | | 4 | 14 | | 5 | 16 | | 6 | 15 | | 7 | 4 | | 8 | 14 | | 9 | 25 | | 10 | 12 | | 11 | 8 | | 12 | 9 | | 13 | 20 | | 14 | 12 | | 15 | 15 | | 16 | 13 | | 17 | 13 | | 18 | 15 | | 19 | 7 | | 20 | 10 | | 21 | 11 | | 22 | 12 | | 23 | 11 | | 24 | 12 | | 25 | 3 | | 26 | 7 | | 27 | 6 | | 28 | 10 | | 29 | 14 | | 30 | 9 | | 31 | 7 | | 32 | 18 | | 33 | 15 | | 34 | 18 | | 35 | 11 | | 36 | 14 | | 37 | 19 | | 38 | 10 | | 39 | 12 | | 40 | 11 | | 41 | 7 | | 42 | 6 | | 43 | 23 | | 44 | 6 | | 45 | 18 | | 46 | 6 | | 47 | 8 | | 48 | 6 | | 49 | 9 |
| |
| 68.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4326923076923077 | | totalSentences | 104 | | uniqueOpeners | 45 | |
| 98.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 102 | | matches | | 0 | "Once past, the atmosphere shifted" | | 1 | "Suddenly, a silhouette lunged from" | | 2 | "Instinctively, she wrenched his arm," |
| | ratio | 0.029 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 102 | | matches | | 0 | "She couldn't lose him—not after" | | 1 | "She grabbed at the worn" | | 2 | "She rounded a corner just" | | 3 | "She hesitated for a fraction" | | 4 | "She gripped the railing through" | | 5 | "Her breath echoed off the" | | 6 | "She watched the gatekeeper accept" | | 7 | "His eyes narrowed, but recognition" | | 8 | "It was enough." | | 9 | "She slammed her shoulder into" | | 10 | "Her senses heightened immediately—the mingling" | | 11 | "It emitted an eerie hum," | | 12 | "She stifled the discomfort and" | | 13 | "He maneuvered effortlessly, communicating in" | | 14 | "Her breath was strained but" | | 15 | "He was close to something—or" | | 16 | "she muttered to herself" | | 17 | "She wasn’t used to this" | | 18 | "He bolted towards the rear" | | 19 | "She glanced over her shoulder." |
| | ratio | 0.333 | |
| 62.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 102 | | matches | | 0 | "Detective Harlow Quinn pushed through" | | 1 | "Rain whipped at her face," | | 2 | "The suspect was just ahead," | | 3 | "Harlow’s breath came out in" | | 4 | "She couldn't lose him—not after" | | 5 | "The distinctive green neon sign" | | 6 | "The temptation to get Tommy’s" | | 7 | "She grabbed at the worn" | | 8 | "She rounded a corner just" | | 9 | "Harlow blinked away raindrops, her" | | 10 | "The staircase led to the" | | 11 | "She hesitated for a fraction" | | 12 | "The stairwell twisted and darkened," | | 13 | "The pounding rain became a" | | 14 | "She gripped the railing through" | | 15 | "Her breath echoed off the" | | 16 | "A bone token dangled from" | | 17 | "She watched the gatekeeper accept" | | 18 | "His eyes narrowed, but recognition" | | 19 | "Harlow barked, catching the man’s" |
| | ratio | 0.794 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 102 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn pushed through the wet night, her boots splashing in the puddles that lined the narrow alleyways of Soho." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 5 | | fancyTags | | 0 | "Harlow barked (bark)" | | 1 | "she muttered (mutter)" | | 2 | "she gasped (gasp)" | | 3 | "she demanded (demand)" | | 4 | "she pressed (press)" |
| | dialogueSentences | 6 | | tagDensity | 0.833 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |