| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.129 | | leniency | 0.258 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 75.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1221 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "perfectly" | | 1 | "gently" | | 2 | "suddenly" | | 3 | "very" | | 4 | "completely" |
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| 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) | |
| 26.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1221 | | totalAiIsms | 18 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | word | "down her spine" | | count | 1 |
| | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "flickered" | | 1 | "echoed" | | 2 | "pulse" | | 3 | "synthetic" | | 4 | "perfect" | | 5 | "raced" | | 6 | "down her spine" | | 7 | "echo" | | 8 | "traced" | | 9 | "silence" | | 10 | "rhythmic" | | 11 | "resonated" | | 12 | "pulsed" | | 13 | "synchronized" | | 14 | "chaotic" | | 15 | "shattered" |
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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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1221 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.91% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 898 | | uniqueNames | 8 | | maxNameDensity | 1.78 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 16 | | Camden | 1 | | Miller | 8 | | Oxford | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Morris" |
| | places | | | globalScore | 0.609 | | windowScore | 0.833 | |
| 39.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like a discarded marionette, limbs" | | 1 | "looked like a stamp, but the skin was cha" | | 2 | "sounded like a heartbeat, slow and ancient" |
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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 | 1221 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 121 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 22.2 | | std | 15.61 | | cv | 0.703 | | sampleLengths | | 0 | 4 | | 1 | 34 | | 2 | 47 | | 3 | 4 | | 4 | 16 | | 5 | 20 | | 6 | 41 | | 7 | 6 | | 8 | 5 | | 9 | 42 | | 10 | 4 | | 11 | 12 | | 12 | 66 | | 13 | 5 | | 14 | 12 | | 15 | 18 | | 16 | 15 | | 17 | 39 | | 18 | 44 | | 19 | 3 | | 20 | 18 | | 21 | 5 | | 22 | 35 | | 23 | 21 | | 24 | 8 | | 25 | 25 | | 26 | 20 | | 27 | 4 | | 28 | 46 | | 29 | 11 | | 30 | 10 | | 31 | 9 | | 32 | 43 | | 33 | 30 | | 34 | 28 | | 35 | 17 | | 36 | 28 | | 37 | 45 | | 38 | 9 | | 39 | 9 | | 40 | 34 | | 41 | 10 | | 42 | 3 | | 43 | 29 | | 44 | 53 | | 45 | 13 | | 46 | 10 | | 47 | 32 | | 48 | 52 | | 49 | 16 |
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| 86.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 94 | | matches | | 0 | "was charred" | | 1 | "were curled" | | 2 | "was carved" | | 3 | "been murdered" | | 4 | "were merged" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 146 | | matches | | 0 | "weren't standing" | | 1 | "was glowing" | | 2 | "was widening" | | 3 | "was becoming" | | 4 | "were inhaling" | | 5 | "was frantically tucking" |
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| 72.02% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 3 | | flaggedSentences | 3 | | totalSentences | 121 | | ratio | 0.025 | | matches | | 0 | "She didn't look at the slime; she looked at the ground beneath the body." | | 1 | "They weren't standing on the ground; they were merged with the shadows of the tunnel walls." | | 2 | "The shimmer was no longer a haze; it was becoming a doorway, a jagged tear in the fabric of the station." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 899 | | adjectiveStacks | 2 | | stackExamples | | 0 | "heavy, pressing against her" | | 1 | "faint, sickly green light pulsed" |
| | adverbCount | 24 | | adverbRatio | 0.026696329254727477 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.01557285873192436 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 10.09 | | std | 6.12 | | cv | 0.607 | | sampleLengths | | 0 | 4 | | 1 | 18 | | 2 | 9 | | 3 | 7 | | 4 | 17 | | 5 | 14 | | 6 | 16 | | 7 | 4 | | 8 | 2 | | 9 | 11 | | 10 | 3 | | 11 | 20 | | 12 | 5 | | 13 | 12 | | 14 | 6 | | 15 | 18 | | 16 | 6 | | 17 | 5 | | 18 | 3 | | 19 | 7 | | 20 | 15 | | 21 | 17 | | 22 | 4 | | 23 | 12 | | 24 | 13 | | 25 | 7 | | 26 | 14 | | 27 | 14 | | 28 | 2 | | 29 | 2 | | 30 | 14 | | 31 | 5 | | 32 | 4 | | 33 | 8 | | 34 | 18 | | 35 | 15 | | 36 | 9 | | 37 | 7 | | 38 | 7 | | 39 | 8 | | 40 | 8 | | 41 | 12 | | 42 | 17 | | 43 | 13 | | 44 | 2 | | 45 | 3 | | 46 | 2 | | 47 | 16 | | 48 | 5 | | 49 | 9 |
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| 47.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.33884297520661155 | | totalSentences | 121 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 71.69% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 89 | | matches | | 0 | "She didn't look at the" | | 1 | "Her eyes remained fixed on" | | 2 | "He looked like a discarded" | | 3 | "He looked bored." | | 4 | "She didn't see a drug" | | 5 | "She leaned closer, her sharp" | | 6 | "She noticed a small, circular" | | 7 | "It looked like a stamp," | | 8 | "She snapped them on with" | | 9 | "She didn't look at the" | | 10 | "She gently pried the stiff" | | 11 | "It was carved with a" | | 12 | "She remembered the look on" | | 13 | "She checked the worn leather" | | 14 | "He looked at the dust," | | 15 | "She traced the brickwork with" | | 16 | "It wasn't slime." | | 17 | "It was a ripple in" | | 18 | "She closed her eyes, listening." | | 19 | "It sounded like a heartbeat," |
| | ratio | 0.371 | |
| 10.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 89 | | matches | | 0 | "Detective Harlow Quinn stepped over" | | 1 | "She didn't look at the" | | 2 | "Her eyes remained fixed on" | | 3 | "The victim lay sprawled across" | | 4 | "He looked like a discarded" | | 5 | "A thick, iridescent slime coated" | | 6 | "a voice echoed" | | 7 | "DS Miller stood a few" | | 8 | "He looked bored." | | 9 | "Quinn knelt beside the body." | | 10 | "The smell hit her, a" | | 11 | "She didn't see a drug" | | 12 | "The victim wore a bespoke" | | 13 | "Quinn ignored him." | | 14 | "She leaned closer, her sharp" | | 15 | "She noticed a small, circular" | | 16 | "It looked like a stamp," | | 17 | "Quinn reached into her pocket" | | 18 | "She snapped them on with" | | 19 | "She didn't look at the" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 17.86% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 7 | | matches | | 0 | "Detective Harlow Quinn stepped over a yellow tape line that flickered under the harsh hum of portable floodlights." | | 1 | "He looked like a discarded marionette, limbs twisted at angles that defied human anatomy." | | 2 | "The victim wore a bespoke charcoal wool suit and leather Oxford shoes that cost more than Miller's car." | | 3 | "There was a rhythmic thrum, a low-frequency vibration that resonated in her teeth." | | 4 | "She remembered the restricted files she'd skimmed in secret, the ones that mentioned the Veil Market." | | 5 | "Three figures, tall and spindly, with skin the color of bruised plums and eyes that glowed with a pale, bioluminescent hunger." | | 6 | "She was short, with a wild mane of curly red hair and round glasses that reflected the chaotic flashing of the floodlights." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 53.23% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "Miller shouted (shout)" | | 1 | "Quinn yelled (yell)" | | 2 | "the woman whispered (whisper)" |
| | dialogueSentences | 31 | | tagDensity | 0.097 | | leniency | 0.194 | | rawRatio | 1 | | effectiveRatio | 0.194 | |