| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1329 | | 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) | |
| 54.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1329 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "standard" | | 1 | "mechanical" | | 2 | "racing" | | 3 | "familiar" | | 4 | "aftermath" | | 5 | "flickered" | | 6 | "grave" | | 7 | "resonated" | | 8 | "gloom" | | 9 | "scanned" | | 10 | "velvet" | | 11 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 220 | | matches | (empty) | |
| 84.42% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 5 | | narrationSentences | 220 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "happened to" | | 2 | "tried to" | | 3 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 220 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1328 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.52% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1328 | | uniqueNames | 14 | | maxNameDensity | 1.13 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 2 | | Herrera | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Tube | 1 | | Greek | 1 | | Detective | 3 | | Tomás | 5 | | Veil | 1 | | Market | 1 | | Morris | 6 | | Harlow | 15 | | You | 7 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Tomás" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Harlow" | | 6 | "You" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.935 | | windowScore | 1 | |
| 50.99% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | glossingSentenceCount | 4 | | matches | | 0 | "Sigils that seemed to move in the periphery" | | 1 | "sounded like grinding stones" | | 2 | "felt like the aftermath of the case thr" | | 3 | "looked like people, bartered in hushed to" |
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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 | 1328 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 220 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 20.75 | | std | 17.17 | | cv | 0.827 | | sampleLengths | | 0 | 67 | | 1 | 29 | | 2 | 2 | | 3 | 63 | | 4 | 42 | | 5 | 55 | | 6 | 49 | | 7 | 35 | | 8 | 17 | | 9 | 4 | | 10 | 14 | | 11 | 6 | | 12 | 15 | | 13 | 12 | | 14 | 32 | | 15 | 3 | | 16 | 12 | | 17 | 21 | | 18 | 11 | | 19 | 4 | | 20 | 9 | | 21 | 31 | | 22 | 24 | | 23 | 6 | | 24 | 41 | | 25 | 7 | | 26 | 19 | | 27 | 6 | | 28 | 10 | | 29 | 50 | | 30 | 42 | | 31 | 1 | | 32 | 8 | | 33 | 58 | | 34 | 45 | | 35 | 12 | | 36 | 4 | | 37 | 5 | | 38 | 32 | | 39 | 43 | | 40 | 40 | | 41 | 29 | | 42 | 28 | | 43 | 8 | | 44 | 28 | | 45 | 7 | | 46 | 10 | | 47 | 3 | | 48 | 5 | | 49 | 5 |
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| 98.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 220 | | matches | | 0 | "been chained" | | 1 | "was supposed" | | 2 | "was compromised" | | 3 | "is owed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 262 | | matches | | 0 | "was handing" | | 1 | "was sending" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 220 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1329 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.024830699774266364 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.006772009029345372 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 220 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 220 | | mean | 6.04 | | std | 3.81 | | cv | 0.631 | | sampleLengths | | 0 | 13 | | 1 | 5 | | 2 | 20 | | 3 | 16 | | 4 | 6 | | 5 | 7 | | 6 | 4 | | 7 | 8 | | 8 | 8 | | 9 | 3 | | 10 | 6 | | 11 | 2 | | 12 | 9 | | 13 | 5 | | 14 | 17 | | 15 | 4 | | 16 | 5 | | 17 | 23 | | 18 | 2 | | 19 | 8 | | 20 | 7 | | 21 | 2 | | 22 | 7 | | 23 | 3 | | 24 | 13 | | 25 | 9 | | 26 | 14 | | 27 | 2 | | 28 | 20 | | 29 | 2 | | 30 | 8 | | 31 | 8 | | 32 | 8 | | 33 | 10 | | 34 | 4 | | 35 | 19 | | 36 | 12 | | 37 | 1 | | 38 | 1 | | 39 | 3 | | 40 | 8 | | 41 | 10 | | 42 | 5 | | 43 | 5 | | 44 | 7 | | 45 | 1 | | 46 | 3 | | 47 | 3 | | 48 | 3 | | 49 | 8 |
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| 52.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.36818181818181817 | | totalSentences | 220 | | uniqueOpeners | 81 | |
| 70.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 188 | | matches | | 0 | "Instead, it stood ajar." | | 1 | "Dark curly hair." | | 2 | "Then at the man." | | 3 | "Then at the crowd watching" |
| | ratio | 0.021 | |
| 96.60% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 58 | | totalSentences | 188 | | matches | | 0 | "She tightened her core, pivoted" | | 1 | "They darted across Camden High" | | 2 | "She holstered her radio before" | | 3 | "She pushed the thought down," | | 4 | "It should have been chained" | | 5 | "They held it up to" | | 6 | "She drew her service weapon." | | 7 | "You do not belong here," | | 8 | "I belong wherever I say" | | 9 | "You lack the token." | | 10 | "You lack the invitation." | | 11 | "I have a warrant." | | 12 | "You have a gun." | | 13 | "It will not work here." | | 14 | "She squeezed the trigger." | | 15 | "She had checked it herself" | | 16 | "She holstered the useless weapon" | | 17 | "I am not asking for" | | 18 | "Her breath hitched." | | 19 | "He walks the halls." |
| | ratio | 0.309 | |
| 42.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 157 | | totalSentences | 188 | | matches | | 0 | "Boots slammed against wet pavement," | | 1 | "Harlow Quinn did not slip." | | 2 | "She tightened her core, pivoted" | | 3 | "The suspect wore a hood" | | 4 | "Harlow checked her watch." | | 5 | "The worn leather strap stuck" | | 6 | "Tomás Herrera's clinic." | | 7 | "The connection tightened like a" | | 8 | "The voice tore from her" | | 9 | "The figure did not stop." | | 10 | "They darted across Camden High" | | 11 | "A car horn blared." | | 12 | "Tires hissed on the asphalt." | | 13 | "The suspect vaulted a low" | | 14 | "She holstered her radio before" | | 15 | "This was not a call" | | 16 | "Morris would have waited for" | | 17 | "Morris was dead." | | 18 | "She pushed the thought down," | | 19 | "The air changed as she" |
| | ratio | 0.835 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 188 | | matches | | 0 | "Either way, she was blind." | | 1 | "If she walked away, she" | | 2 | "If she walked through, she" | | 3 | "If he was here, if" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 1 | | matches | | 0 | "A figure stood beside it, tall and gaunt, wearing a coat made of patches that looked suspiciously like skin." |
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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 | |