| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 964 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 42.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 964 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "fractured" | | 1 | "rhythmic" | | 2 | "vibrated" | | 3 | "flicker" | | 4 | "weight" | | 5 | "cacophony" | | 6 | "velvet" | | 7 | "silk" | | 8 | "scanned" | | 9 | "pulsed" |
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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 | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 963 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 57.83% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 868 | | uniqueNames | 5 | | maxNameDensity | 1.84 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Maintenance | 1 | | Veil | 1 | | Market | 1 | | Quinn | 16 |
| | persons | | | places | (empty) | | globalScore | 0.578 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 963 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 21.4 | | std | 16.29 | | cv | 0.761 | | sampleLengths | | 0 | 50 | | 1 | 31 | | 2 | 30 | | 3 | 3 | | 4 | 20 | | 5 | 30 | | 6 | 50 | | 7 | 25 | | 8 | 53 | | 9 | 11 | | 10 | 5 | | 11 | 13 | | 12 | 5 | | 13 | 41 | | 14 | 5 | | 15 | 10 | | 16 | 9 | | 17 | 1 | | 18 | 15 | | 19 | 18 | | 20 | 15 | | 21 | 63 | | 22 | 14 | | 23 | 59 | | 24 | 23 | | 25 | 6 | | 26 | 38 | | 27 | 32 | | 28 | 31 | | 29 | 13 | | 30 | 5 | | 31 | 12 | | 32 | 4 | | 33 | 8 | | 34 | 18 | | 35 | 44 | | 36 | 13 | | 37 | 9 | | 38 | 19 | | 39 | 25 | | 40 | 31 | | 41 | 8 | | 42 | 1 | | 43 | 12 | | 44 | 35 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 90 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 147 | | matches | (empty) | |
| 58.82% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 102 | | ratio | 0.029 | | matches | | 0 | "Quinn caught the scent of something metallic—copper and ozone." | | 1 | "The light from inside the tunnel didn't flicker; it glowed with an unnatural, amber hue." | | 2 | "He didn't look afraid; he looked expectant." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 874 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.016018306636155607 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0011441647597254005 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 9.44 | | std | 5.15 | | cv | 0.545 | | sampleLengths | | 0 | 14 | | 1 | 7 | | 2 | 9 | | 3 | 20 | | 4 | 3 | | 5 | 13 | | 6 | 15 | | 7 | 9 | | 8 | 12 | | 9 | 9 | | 10 | 3 | | 11 | 5 | | 12 | 15 | | 13 | 7 | | 14 | 7 | | 15 | 13 | | 16 | 3 | | 17 | 2 | | 18 | 3 | | 19 | 17 | | 20 | 9 | | 21 | 10 | | 22 | 9 | | 23 | 11 | | 24 | 11 | | 25 | 3 | | 26 | 6 | | 27 | 17 | | 28 | 5 | | 29 | 9 | | 30 | 16 | | 31 | 11 | | 32 | 5 | | 33 | 5 | | 34 | 8 | | 35 | 5 | | 36 | 14 | | 37 | 12 | | 38 | 15 | | 39 | 5 | | 40 | 10 | | 41 | 9 | | 42 | 1 | | 43 | 4 | | 44 | 11 | | 45 | 18 | | 46 | 10 | | 47 | 5 | | 48 | 5 | | 49 | 23 |
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| 33.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.23529411764705882 | | totalSentences | 102 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 89 | | matches | | 0 | "She vaulted the crates in" | | 1 | "He banked hard right, sliding" | | 2 | "He reached into his pocket" | | 3 | "He wore a coat made" | | 4 | "He held out a hand." | | 5 | "She saw the suspect vanish" | | 6 | "He leaned in, his breath" | | 7 | "She stepped through the threshold." | | 8 | "He held a silver cane" | | 9 | "He leaned in, whispering to" | | 10 | "She wove through the crowd," | | 11 | "He didn't look afraid; he" | | 12 | "He tapped his wrist, mimicking" | | 13 | "They didn't attack." | | 14 | "They simply watched." | | 15 | "She checked her watch." | | 16 | "She spun around, spotting a" | | 17 | "She lunged forward, ripping the" | | 18 | "He held a small glass" | | 19 | "He held it up, a" |
| | ratio | 0.247 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 89 | | matches | | 0 | "Quinn’s boots slammed against the" | | 1 | "Water streamed down her salt-and-pepper" | | 2 | "Quinn didn't slow." | | 3 | "She vaulted the crates in" | | 4 | "The worn leather watch on" | | 5 | "The alley smelled of rotting" | | 6 | "The suspect skidded around a" | | 7 | "Quinn caught the scent of" | | 8 | "The figure didn't look back." | | 9 | "He banked hard right, sliding" | | 10 | "Quinn reached the gate and" | | 11 | "The opening led to a" | | 12 | "A faded sign clung to" | | 13 | "The air shifted." | | 14 | "The roar of the city" | | 15 | "Water dripped from the ceiling" | | 16 | "The walls were stained with" | | 17 | "The suspect paused at the" | | 18 | "He reached into his pocket" | | 19 | "A bone token." |
| | ratio | 0.933 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 65.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 5 | | matches | | 0 | "He wore a coat made of mismatched leather patches and a wide-brimmed hat that shadowed his eyes." | | 1 | "A cacophony of bartering, the clink of glass vials, and a low, guttural chanting that made the hair on her arms stand up." | | 2 | "Merchants sold jars of shimmering smoke and blades that bled shadows." | | 3 | "She wove through the crowd, shoving past a creature draped in heavy furs that smelled of wet dog and ancient dust." | | 4 | "The suspect stepped through a doorway that led into a pitch-black void." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |