| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 39 | | tagDensity | 0.256 | | leniency | 0.513 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1201 | | totalAiIsmAdverbs | 1 | | 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) | |
| 8.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1201 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "intricate" | | 1 | "traced" | | 2 | "perfect" | | 3 | "etched" | | 4 | "flicked" | | 5 | "quivered" | | 6 | "echoing" | | 7 | "scanning" | | 8 | "echoed" | | 9 | "echo" | | 10 | "charged" | | 11 | "pulse" | | 12 | "vibrated" | | 13 | "flickered" | | 14 | "gloom" | | 15 | "chill" | | 16 | "determined" | | 17 | "echoes" | | 18 | "pulsed" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "eyes narrowed" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 99 | | matches | (empty) | |
| 85.14% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1194 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 30.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 835 | | uniqueNames | 10 | | maxNameDensity | 2.4 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 20 | | Camden | 2 | | Kowalski | 1 | | Eva | 14 | | Veil | 1 | | Compass | 1 | | High | 1 | | Street | 1 | | Jack | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Jack" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.302 | | windowScore | 0.333 | |
| 76.47% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 2 | | matches | | 0 | "hum that seemed to rise from the deepest tunnel" | | 1 | "as if sensing the compass inside" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.838 | | wordCount | 1194 | | matches | | 0 | "not toward the station exit but back into the service tunnel they had just passed" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 31.42 | | std | 18.6 | | cv | 0.592 | | sampleLengths | | 0 | 64 | | 1 | 46 | | 2 | 58 | | 3 | 29 | | 4 | 42 | | 5 | 78 | | 6 | 6 | | 7 | 60 | | 8 | 27 | | 9 | 44 | | 10 | 35 | | 11 | 22 | | 12 | 30 | | 13 | 18 | | 14 | 54 | | 15 | 51 | | 16 | 9 | | 17 | 58 | | 18 | 9 | | 19 | 30 | | 20 | 11 | | 21 | 23 | | 22 | 21 | | 23 | 30 | | 24 | 17 | | 25 | 26 | | 26 | 10 | | 27 | 35 | | 28 | 24 | | 29 | 7 | | 30 | 47 | | 31 | 16 | | 32 | 48 | | 33 | 6 | | 34 | 40 | | 35 | 39 | | 36 | 5 | | 37 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 163 | | matches | (empty) | |
| 6.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 2 | | flaggedSentences | 6 | | totalSentences | 126 | | ratio | 0.048 | | matches | | 0 | "No soot clung to the fabric; the burns had radiated from within." | | 1 | "Near the platform edge, she spotted a chipped bone token—ivory, the size of a thumb—clenched in the victim’s right hand." | | 2 | "“This token, these glyphs—they’re staged. He wasn’t a rift mage; he was bait. They wanted someone to open it for them.”" | | 3 | "She surveyed the station—the cracked walls, the empty tracks, the faint hum that seemed to rise from the deepest tunnel." | | 4 | "Quinn looked at her friend—hair tousled, cheeks pale but determined." | | 5 | "The abandoned station breathed around them—tiles whispering, echoes drifting." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 845 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.020118343195266272 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0035502958579881655 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 9.48 | | std | 5.75 | | cv | 0.607 | | sampleLengths | | 0 | 15 | | 1 | 14 | | 2 | 15 | | 3 | 6 | | 4 | 14 | | 5 | 13 | | 6 | 12 | | 7 | 9 | | 8 | 12 | | 9 | 14 | | 10 | 17 | | 11 | 16 | | 12 | 9 | | 13 | 2 | | 14 | 10 | | 15 | 9 | | 16 | 10 | | 17 | 4 | | 18 | 17 | | 19 | 13 | | 20 | 8 | | 21 | 9 | | 22 | 14 | | 23 | 4 | | 24 | 21 | | 25 | 11 | | 26 | 19 | | 27 | 3 | | 28 | 3 | | 29 | 11 | | 30 | 7 | | 31 | 20 | | 32 | 4 | | 33 | 8 | | 34 | 4 | | 35 | 6 | | 36 | 3 | | 37 | 24 | | 38 | 4 | | 39 | 20 | | 40 | 13 | | 41 | 7 | | 42 | 4 | | 43 | 12 | | 44 | 7 | | 45 | 6 | | 46 | 6 | | 47 | 5 | | 48 | 17 | | 49 | 3 |
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| 73.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.44 | | totalSentences | 125 | | uniqueOpeners | 55 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 98.65% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 89 | | matches | | 0 | "She raised her worn leather" | | 1 | "His skin was ashen, veins" | | 2 | "She tucked a curly red" | | 3 | "She inhaled slowly, tasting copper" | | 4 | "she asked, voice edged with" | | 5 | "She flicked it open." | | 6 | "She snapped the lid shut" | | 7 | "She circled the chalk ring," | | 8 | "She pried it free." | | 9 | "Its surface was scored with" | | 10 | "She flicked the token against" | | 11 | "Her sharp jaw set." | | 12 | "She noted the station’s old" | | 13 | "She bent once more to" | | 14 | "They followed the echo into" | | 15 | "It groaned on rusted hinges," | | 16 | "She sensed a faint pulse," | | 17 | "She slipped the compass into" | | 18 | "Her glasses fogged in the" | | 19 | "They knelt by the body," |
| | ratio | 0.303 | |
| 38.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 89 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The stench of damp rot" | | 2 | "She raised her worn leather" | | 3 | "His skin was ashen, veins" | | 4 | "Eva Kowalski crouched close, her" | | 5 | "Freckles danced across her pale" | | 6 | "She tucked a curly red" | | 7 | "Quinn knelt beside her, gaze" | | 8 | "She inhaled slowly, tasting copper" | | 9 | "she asked, voice edged with" | | 10 | "Eva shook her head." | | 11 | "She’shed her torch across the" | | 12 | "The scorch pattern resembled a" | | 13 | "The small brass case bore" | | 14 | "She flicked it open." | | 15 | "The needle quivered, then steadied," | | 16 | "She snapped the lid shut" | | 17 | "Eva’s eyes widened." | | 18 | "Quinn rose, boots echoing on" | | 19 | "She circled the chalk ring," |
| | ratio | 0.843 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 1 | | matches | | 0 | "She surveyed the station—the cracked walls, the empty tracks, the faint hum that seemed to rise from the deepest tunnel." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 3 | | matches | | 0 | "she asked, voice edged with skepticism" | | 1 | "She glanced, as if sensing the compass inside" | | 2 | "She paused, voice low" |
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| 98.72% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "Eva whispered (whisper)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 39 | | tagDensity | 0.128 | | leniency | 0.256 | | rawRatio | 0.4 | | effectiveRatio | 0.103 | |