| 53.66% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 3 | | adverbTags | | 0 | "Silas said slowly [slowly]" | | 1 | "she corrected automatically [automatically]" | | 2 | "Silas said softly [softly]" |
| | dialogueSentences | 41 | | tagDensity | 0.463 | | leniency | 0.927 | | rawRatio | 0.158 | | effectiveRatio | 0.146 | |
| 68.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1285 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "softly" | | 1 | "slightly" | | 2 | "really" | | 3 | "slowly" | | 4 | "completely" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 49.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1285 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "weight" | | 1 | "trembled" | | 2 | "familiar" | | 3 | "tension" | | 4 | "glinting" | | 5 | "chill" | | 6 | "fractured" | | 7 | "beacon" | | 8 | "warmth" |
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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 | 0 | | narrationSentences | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 91 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1285 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1027 | | uniqueNames | 7 | | maxNameDensity | 1.27 | | worstName | "Silas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | London | 2 | | Carter | 1 | | Raven | 1 | | Nest | 1 | | Aurora | 7 | | Evan | 1 | | Silas | 13 |
| | persons | | 0 | "Carter" | | 1 | "Raven" | | 2 | "Aurora" | | 3 | "Evan" | | 4 | "Silas" |
| | places | | | globalScore | 0.867 | | windowScore | 0.833 | |
| 38.06% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 3 | | matches | | 0 | "rumble that seemed to vibrate through the wood of the counter" | | 1 | "looked like when someone had been broken" | | 2 | "quite reach his eyes but softened the hardness of his face" |
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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 | 1285 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 35.69 | | std | 23.26 | | cv | 0.652 | | sampleLengths | | 0 | 97 | | 1 | 58 | | 2 | 60 | | 3 | 71 | | 4 | 1 | | 5 | 52 | | 6 | 19 | | 7 | 28 | | 8 | 24 | | 9 | 21 | | 10 | 68 | | 11 | 23 | | 12 | 8 | | 13 | 2 | | 14 | 27 | | 15 | 32 | | 16 | 13 | | 17 | 19 | | 18 | 52 | | 19 | 58 | | 20 | 22 | | 21 | 24 | | 22 | 32 | | 23 | 74 | | 24 | 42 | | 25 | 7 | | 26 | 28 | | 27 | 28 | | 28 | 22 | | 29 | 9 | | 30 | 52 | | 31 | 62 | | 32 | 9 | | 33 | 29 | | 34 | 67 | | 35 | 45 |
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| 93.70% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 91 | | matches | | 0 | "being spoken" | | 1 | "been broken" | | 2 | "was gone" |
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| 87.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 177 | | matches | | 0 | "was looking" | | 1 | "was already turning" | | 2 | "was watching" |
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| 92.29% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 113 | | ratio | 0.018 | | matches | | 0 | "The rain in London did not wash things clean; it merely pressed the grime deeper into the cobblestones." | | 1 | "He was a man who noticed details; it was in the way his eyes tracked movement, the way he assessed threats in a crowded room." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1030 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar." |
| | adverbCount | 31 | | adverbRatio | 0.030097087378640777 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.014563106796116505 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 11.37 | | std | 6.45 | | cv | 0.567 | | sampleLengths | | 0 | 18 | | 1 | 23 | | 2 | 20 | | 3 | 21 | | 4 | 15 | | 5 | 18 | | 6 | 25 | | 7 | 4 | | 8 | 11 | | 9 | 25 | | 10 | 16 | | 11 | 19 | | 12 | 17 | | 13 | 20 | | 14 | 15 | | 15 | 19 | | 16 | 1 | | 17 | 11 | | 18 | 16 | | 19 | 25 | | 20 | 19 | | 21 | 7 | | 22 | 5 | | 23 | 8 | | 24 | 8 | | 25 | 8 | | 26 | 16 | | 27 | 7 | | 28 | 7 | | 29 | 7 | | 30 | 3 | | 31 | 12 | | 32 | 25 | | 33 | 28 | | 34 | 6 | | 35 | 5 | | 36 | 12 | | 37 | 5 | | 38 | 3 | | 39 | 2 | | 40 | 11 | | 41 | 13 | | 42 | 3 | | 43 | 9 | | 44 | 15 | | 45 | 4 | | 46 | 4 | | 47 | 7 | | 48 | 4 | | 49 | 2 |
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| 40.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.23893805309734514 | | totalSentences | 113 | | uniqueOpeners | 27 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 82 | | matches | | | ratio | 0.012 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 82 | | matches | | 0 | "She shook the umbrella, sending" | | 1 | "It was a smell she" | | 2 | "She set the insulated delivery" | | 3 | "Her delivery shift had run" | | 4 | "She needed a moment." | | 5 | "He was tall, though his" | | 6 | "His hair was auburn, heavily" | | 7 | "He wore a silver signet" | | 8 | "He looked older than the" | | 9 | "He set the glass down" | | 10 | "he asked, his voice a" | | 11 | "Her fingers trembled, just slightly." | | 12 | "Her voice sounded thin to" | | 13 | "she said, then paused" | | 14 | "He looked at her, really" | | 15 | "He was a man who" | | 16 | "He was looking at her" | | 17 | "He set the bottle down." | | 18 | "She didn't want to say" | | 19 | "She didn't want to say" |
| | ratio | 0.585 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 82 | | matches | | 0 | "The rain in London did" | | 1 | "Aurora Carter pushed through the" | | 2 | "She shook the umbrella, sending" | | 3 | "The air inside was thick" | | 4 | "It was a smell she" | | 5 | "She set the insulated delivery" | | 6 | "Her delivery shift had run" | | 7 | "She needed a moment." | | 8 | "The bar was dim, lit" | | 9 | "Maps covered the walls, faded" | | 10 | "Aurora moved toward the back," | | 11 | "He was tall, though his" | | 12 | "His hair was auburn, heavily" | | 13 | "He wore a silver signet" | | 14 | "He looked older than the" | | 15 | "The lines around his hazel" | | 16 | "He set the glass down" | | 17 | "he asked, his voice a" | | 18 | "Aurora gripped the edge of" | | 19 | "Her fingers trembled, just slightly." |
| | ratio | 0.951 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 79.37% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 4 | | matches | | 0 | "His hair was auburn, heavily streaked with grey, matching the beard that framed his jawline." | | 1 | "He was a man who noticed details; it was in the way his eyes tracked movement, the way he assessed threats in a crowded room." | | 2 | "He was looking at her face, the straight black hair plastered to her cheeks, the bright blue eyes that were usually sharp but now held a guarded shadow." | | 3 | "She walked, her boots striking the pavement with a steady rhythm, leaving the warmth behind for the road ahead." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 1 | | fancyTags | | 0 | "she corrected automatically (correct)" |
| | dialogueSentences | 41 | | tagDensity | 0.317 | | leniency | 0.634 | | rawRatio | 0.077 | | effectiveRatio | 0.049 | |