| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 589 | | 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) | |
| 15.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 589 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "scanned" | | 1 | "measured" | | 2 | "determined" | | 3 | "furrowing" | | 4 | "calculated" | | 5 | "etched" | | 6 | "eyebrow" | | 7 | "scanning" |
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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 | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 32 | | matches | (empty) | |
| 53.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 32 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 42 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 588 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 386 | | uniqueNames | 4 | | maxNameDensity | 4.15 | | worstName | "Quinn" | | maxWindowNameDensity | 6 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 16 | | Davies | 6 | | Shade | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Davies" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 26 | | glossingSentenceCount | 1 | | matches | | 0 | "symbols that seemed to match the ones on the walls" |
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| 29.93% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.701 | | wordCount | 588 | | matches | | 0 | "not just in the physical evidence, but in understanding the motivations" |
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| 7.94% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 42 | | matches | | 0 | "knew that the" | | 1 | "knew that she" |
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| 96.37% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 29.4 | | std | 14.33 | | cv | 0.487 | | sampleLengths | | 0 | 52 | | 1 | 13 | | 2 | 38 | | 3 | 52 | | 4 | 23 | | 5 | 9 | | 6 | 22 | | 7 | 33 | | 8 | 8 | | 9 | 23 | | 10 | 28 | | 11 | 52 | | 12 | 30 | | 13 | 27 | | 14 | 10 | | 15 | 42 | | 16 | 16 | | 17 | 22 | | 18 | 45 | | 19 | 43 |
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| 61.40% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 32 | | matches | | 0 | "was drawn" | | 1 | "was embossed" | | 2 | "was etched" | | 3 | "was determined" |
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| 92.47% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 62 | | matches | | |
| 6.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 42 | | ratio | 0.048 | | matches | | 0 | "The air was thick with the scent of incense and something else – copper, perhaps." | | 1 | "She recognized the craftsmanship – a Shade artisan, if she wasn't mistaken." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 385 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, leather-bound book" |
| | adverbCount | 9 | | adverbRatio | 0.023376623376623377 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.01038961038961039 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 42 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 42 | | mean | 14 | | std | 7.81 | | cv | 0.558 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 15 | | 3 | 6 | | 4 | 13 | | 5 | 13 | | 6 | 25 | | 7 | 13 | | 8 | 15 | | 9 | 24 | | 10 | 12 | | 11 | 11 | | 12 | 4 | | 13 | 5 | | 14 | 2 | | 15 | 20 | | 16 | 17 | | 17 | 16 | | 18 | 8 | | 19 | 7 | | 20 | 16 | | 21 | 3 | | 22 | 25 | | 23 | 15 | | 24 | 18 | | 25 | 19 | | 26 | 13 | | 27 | 5 | | 28 | 12 | | 29 | 27 | | 30 | 4 | | 31 | 6 | | 32 | 6 | | 33 | 36 | | 34 | 7 | | 35 | 9 | | 36 | 8 | | 37 | 14 | | 38 | 17 | | 39 | 28 | | 40 | 14 | | 41 | 29 |
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| 61.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.38095238095238093 | | totalSentences | 42 | | uniqueOpeners | 16 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 31 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 31 | | matches | | 0 | "She spotted the source: a" | | 1 | "She noticed something that had" | | 2 | "She recognized the craftsmanship –" | | 3 | "She knew that the key" | | 4 | "She knew that she had" |
| | ratio | 0.161 | |
| 24.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 27 | | totalSentences | 31 | | matches | | 0 | "Detective Harlow Quinn's eyes scanned" | | 1 | "The air was thick with" | | 2 | "She spotted the source: a" | | 3 | "A faint hum emanated from" | | 4 | "Quinn asked, her voice firm" | | 5 | "Davies, a younger detective, stood" | | 6 | "Quinn's sharp jawline set in" | | 7 | "The woman lay on her" | | 8 | "Quinn's trained eyes took in" | | 9 | "Quinn said, her brow furrowing" | | 10 | "Davies looked up, interested." | | 11 | "The cover was embossed with" | | 12 | "Quinn asked, picking up the" | | 13 | "Davies leaned in, peering at" | | 14 | "Quinn's eyes narrowed." | | 15 | "She noticed something that had" | | 16 | "The face of the compass" | | 17 | "Quinn's thoughts whirled as she" | | 18 | "This was no ordinary compass." | | 19 | "She recognized the craftsmanship –" |
| | ratio | 0.871 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 31 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 1 | | matches | | 0 | "She noticed something that had escaped her initial observation: a small, brass compass clutched in the victim's hand." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn asked, her voice firm but measured" | | 1 | "Quinn said, her brow furrowing" | | 2 | "Quinn said, her voice low and even" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |