| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said finally [finally]" |
| | dialogueSentences | 23 | | tagDensity | 0.261 | | leniency | 0.522 | | rawRatio | 0.167 | | effectiveRatio | 0.087 | |
| 90.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1047 | | 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) | |
| 14.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1047 | | totalAiIsms | 18 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | word | "down her spine" | | count | 1 |
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| | highlights | | 0 | "kaleidoscope" | | 1 | "familiar" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "treacherous" | | 5 | "scanned" | | 6 | "racing" | | 7 | "whisper" | | 8 | "jaw clenched" | | 9 | "flicker" | | 10 | "determined" | | 11 | "quickened" | | 12 | "pulse" | | 13 | "gloom" | | 14 | "chill" | | 15 | "down her spine" |
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| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
| | 2 | | label | "air was thick with" | | count | 1 |
| | 3 | | label | "sent a shiver through" | | count | 1 |
|
| | highlights | | 0 | "clenched her fists" | | 1 | "jaw clenched" | | 2 | "The air was thick with" | | 3 | "sent a chill down" |
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| 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 | 1 | | hedgeCount | 0 | | narrationSentences | 73 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1041 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 811 | | uniqueNames | 10 | | maxNameDensity | 0.86 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 1 | | Quinn | 7 | | Soho | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 | | Spanish | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Tomás" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 1 | | matches | | 0 | "as if considering her request" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1041 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 31.55 | | std | 18.26 | | cv | 0.579 | | sampleLengths | | 0 | 61 | | 1 | 20 | | 2 | 75 | | 3 | 54 | | 4 | 32 | | 5 | 66 | | 6 | 47 | | 7 | 49 | | 8 | 32 | | 9 | 3 | | 10 | 42 | | 11 | 19 | | 12 | 24 | | 13 | 15 | | 14 | 17 | | 15 | 4 | | 16 | 28 | | 17 | 3 | | 18 | 43 | | 19 | 50 | | 20 | 32 | | 21 | 40 | | 22 | 13 | | 23 | 27 | | 24 | 49 | | 25 | 27 | | 26 | 28 | | 27 | 24 | | 28 | 23 | | 29 | 13 | | 30 | 47 | | 31 | 29 | | 32 | 5 |
| |
| 95.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 73 | | matches | | 0 | "been swallowed" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 147 | | matches | (empty) | |
| 78.65% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 89 | | ratio | 0.022 | | matches | | 0 | "The suspect—hooded, quick, and unnervingly familiar with the labyrinthine alleys of Soho—vanished around a corner." | | 1 | "There—moving swiftly toward the far end of the platform." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 817 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.02937576499388005 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.01346389228886169 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 11.7 | | std | 6.88 | | cv | 0.588 | | sampleLengths | | 0 | 17 | | 1 | 16 | | 2 | 13 | | 3 | 15 | | 4 | 20 | | 5 | 14 | | 6 | 9 | | 7 | 15 | | 8 | 14 | | 9 | 8 | | 10 | 15 | | 11 | 21 | | 12 | 9 | | 13 | 3 | | 14 | 3 | | 15 | 18 | | 16 | 8 | | 17 | 12 | | 18 | 12 | | 19 | 9 | | 20 | 9 | | 21 | 18 | | 22 | 21 | | 23 | 9 | | 24 | 8 | | 25 | 17 | | 26 | 3 | | 27 | 19 | | 28 | 13 | | 29 | 24 | | 30 | 12 | | 31 | 10 | | 32 | 9 | | 33 | 13 | | 34 | 3 | | 35 | 16 | | 36 | 18 | | 37 | 8 | | 38 | 9 | | 39 | 10 | | 40 | 13 | | 41 | 11 | | 42 | 11 | | 43 | 4 | | 44 | 6 | | 45 | 11 | | 46 | 4 | | 47 | 10 | | 48 | 18 | | 49 | 2 |
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| 64.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4044943820224719 | | totalSentences | 89 | | uniqueOpeners | 36 | |
| 46.95% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 71 | | matches | | | ratio | 0.014 | |
| 67.89% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 71 | | matches | | 0 | "Her breath came in sharp" | | 1 | "Her voice cut through the" | | 2 | "She rounded the corner, her" | | 3 | "Her worn leather watch ticked" | | 4 | "She hesitated, her military-trained instincts" | | 5 | "She clenched her fists, rain" | | 6 | "she muttered under her breath" | | 7 | "She descended carefully, her gaze" | | 8 | "She’d heard whispers of this" | | 9 | "Her eyes scanned the crowd," | | 10 | "She moved to follow, but" | | 11 | "She turned, her heart racing," | | 12 | "His short curly dark brown" | | 13 | "she said, recognition flashing in" | | 14 | "he said, his voice low" | | 15 | "she shot back, pulling her" | | 16 | "He leaned in closer, his" | | 17 | "Her jaw clenched." | | 18 | "She stared at him, rain" | | 19 | "She glanced back at Tomás," |
| | ratio | 0.38 | |
| 16.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 71 | | matches | | 0 | "The rain hammered down, relentless" | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "Her breath came in sharp" | | 3 | "The suspect—hooded, quick, and unnervingly" | | 4 | "Her voice cut through the" | | 5 | "She rounded the corner, her" | | 6 | "The alley narrowed, walls closing" | | 7 | "Quinn skidded to a halt," | | 8 | "The gate hung ajar, creaking" | | 9 | "Her worn leather watch ticked" | | 10 | "She hesitated, her military-trained instincts" | | 11 | "This wasn’t protocol." | | 12 | "This wasn’t smart." | | 13 | "The clatter of footsteps echoed" | | 14 | "She clenched her fists, rain" | | 15 | "she muttered under her breath" | | 16 | "The stairs were treacherous, slick" | | 17 | "She descended carefully, her gaze" | | 18 | "The air grew colder, heavier," | | 19 | "A faint hum of activity" |
| | ratio | 0.887 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 2 | | matches | | 0 | "The air grew colder, heavier, as if the city above had been swallowed by something ancient and indifferent." | | 1 | "The figure tilted their head, as if considering her request." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 2 | | matches | | 0 | "she said, recognition flashing in her mind" | | 1 | "he said, his voice low and edged with caution" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 23 | | tagDensity | 0.174 | | leniency | 0.348 | | rawRatio | 0.25 | | effectiveRatio | 0.087 | |