| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 555 | | 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) | |
| 36.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 555 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "chaotic" | | 2 | "scanned" | | 3 | "silence" | | 4 | "familiar" | | 5 | "gloom" | | 6 | "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 | 46 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 46 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 46 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 555 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 555 | | uniqueNames | 9 | | maxNameDensity | 0.9 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 5 | | London | 1 | | Camden | 2 | | Morris | 1 | | Veil | 1 | | Market | 1 | | Victorian | 1 | | Tube | 1 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like this, but without the rain" | | 1 | "looked like magic" |
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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 | 555 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 46 | | matches | (empty) | |
| 80.63% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 6 | | mean | 92.5 | | std | 39.98 | | cv | 0.432 | | sampleLengths | | |
| 97.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 46 | | matches | | |
| 51.85% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 90 | | matches | | 0 | "wasn't thinking" | | 1 | "was thinking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 559 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.025044722719141325 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.008944543828264758 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 46 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 46 | | mean | 12.07 | | std | 7.82 | | cv | 0.648 | | sampleLengths | | 0 | 23 | | 1 | 18 | | 2 | 5 | | 3 | 22 | | 4 | 16 | | 5 | 28 | | 6 | 20 | | 7 | 4 | | 8 | 20 | | 9 | 2 | | 10 | 12 | | 11 | 28 | | 12 | 13 | | 13 | 6 | | 14 | 16 | | 15 | 2 | | 16 | 18 | | 17 | 11 | | 18 | 11 | | 19 | 16 | | 20 | 5 | | 21 | 11 | | 22 | 9 | | 23 | 16 | | 24 | 2 | | 25 | 12 | | 26 | 3 | | 27 | 11 | | 28 | 18 | | 29 | 2 | | 30 | 12 | | 31 | 6 | | 32 | 6 | | 33 | 3 | | 34 | 28 | | 35 | 3 | | 36 | 20 | | 37 | 5 | | 38 | 5 | | 39 | 4 | | 40 | 19 | | 41 | 5 | | 42 | 19 | | 43 | 13 | | 44 | 24 | | 45 | 3 |
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| 31.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.30434782608695654 | | totalSentences | 46 | | uniqueOpeners | 14 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 42 | | matches | (empty) | | ratio | 0 | |
| 58.10% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 42 | | matches | | 0 | "She didn't have the time." | | 1 | "Her boots hit the wet" | | 2 | "She scanned the shadows, her" | | 3 | "He turned a corner, disappearing" | | 4 | "She checked her watch." | | 5 | "She gripped the butt of" | | 6 | "She wasn't thinking about her" | | 7 | "She was thinking about the" | | 8 | "She stopped at the wall," | | 9 | "She reached out, her fingers" | | 10 | "She looked down." | | 11 | "It was bleached white, carved" | | 12 | "She reached for it, her" | | 13 | "It fit perfectly in her" | | 14 | "She had found the body" | | 15 | "She stepped through the seam." | | 16 | "She was in a cavernous" |
| | ratio | 0.405 | |
| 7.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 42 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn felt the cold" | | 2 | "She didn't have the time." | | 3 | "Her boots hit the wet" | | 4 | "She scanned the shadows, her" | | 5 | "The suspect was a courier," | | 6 | "He turned a corner, disappearing" | | 7 | "She checked her watch." | | 8 | "The leather strap on her" | | 9 | "The city was a graveyard," | | 10 | "Quinn was forty-one, her salt-and-pepper" | | 11 | "She gripped the butt of" | | 12 | "She wasn't thinking about her" | | 13 | "She was thinking about the" | | 14 | "The courier slipped into a" | | 15 | "Quinn closed the gap, her" | | 16 | "The alley ended abruptly at" | | 17 | "She stopped at the wall," | | 18 | "She reached out, her fingers" | | 19 | "There, behind a rusted fire" |
| | ratio | 0.905 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 42 | | matches | | 0 | "Yet, the courier had vanished." |
| | ratio | 0.024 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 3 | | matches | | 0 | "Quinn was forty-one, her salt-and-pepper hair plastered against her skull, but her bearing remained rigid, a ghost of the discipline that had kept her alive for…" | | 1 | "There, behind a rusted fire escape ladder, was a seam in the brickwork that wasn't mortar." | | 2 | "There were no streetlamps, only floating orbs of green and violet that drifted through the gloom like trapped fireflies." |
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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 | |