| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 580 | | 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) | |
| 56.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 580 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "mosaic" | | 1 | "fractured" | | 2 | "footsteps" | | 3 | "echoing" | | 4 | "pulse" |
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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 | 62 | | matches | (empty) | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 573 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 85.90% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 10 | | wordCount | 468 | | uniqueNames | 4 | | maxNameDensity | 1.28 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 6 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | | globalScore | 0.859 | | windowScore | 1 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like human eyes floating in jars" |
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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 | 573 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 21.22 | | std | 12.2 | | cv | 0.575 | | sampleLengths | | 0 | 47 | | 1 | 5 | | 2 | 21 | | 3 | 47 | | 4 | 22 | | 5 | 9 | | 6 | 12 | | 7 | 20 | | 8 | 13 | | 9 | 5 | | 10 | 18 | | 11 | 27 | | 12 | 45 | | 13 | 15 | | 14 | 29 | | 15 | 29 | | 16 | 17 | | 17 | 9 | | 18 | 16 | | 19 | 12 | | 20 | 43 | | 21 | 14 | | 22 | 13 | | 23 | 27 | | 24 | 13 | | 25 | 15 | | 26 | 30 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 62 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 88 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 72 | | ratio | 0.056 | | matches | | 0 | "The suspect—her only lead on the Morris case—dove down an alley, his footsteps echoing against the brick walls." | | 1 | "He pulled something from his pocket—a small white bone—and pressed it against the wall." | | 2 | "She'd dismissed it as departmental cover-up until tonight—the night she'd seen the suspect handle evidence that shouldn't exist." | | 3 | "And at the center of it all, the man she'd been chasing—now standing beside a stall displaying what looked like human eyes floating in jars." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 477 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.027253668763102725 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.006289308176100629 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 7.96 | | std | 5.42 | | cv | 0.681 | | sampleLengths | | 0 | 17 | | 1 | 12 | | 2 | 18 | | 3 | 5 | | 4 | 5 | | 5 | 16 | | 6 | 5 | | 7 | 6 | | 8 | 2 | | 9 | 20 | | 10 | 14 | | 11 | 2 | | 12 | 18 | | 13 | 1 | | 14 | 1 | | 15 | 9 | | 16 | 2 | | 17 | 10 | | 18 | 4 | | 19 | 9 | | 20 | 7 | | 21 | 13 | | 22 | 5 | | 23 | 3 | | 24 | 15 | | 25 | 14 | | 26 | 7 | | 27 | 4 | | 28 | 2 | | 29 | 9 | | 30 | 12 | | 31 | 6 | | 32 | 18 | | 33 | 10 | | 34 | 5 | | 35 | 2 | | 36 | 4 | | 37 | 7 | | 38 | 16 | | 39 | 3 | | 40 | 3 | | 41 | 2 | | 42 | 1 | | 43 | 1 | | 44 | 9 | | 45 | 10 | | 46 | 11 | | 47 | 6 | | 48 | 2 | | 49 | 7 |
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| 97.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.625 | | totalSentences | 72 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 50 | | matches | | 0 | "Just kept moving with a" | | 1 | "Just the drip, drip, drip" |
| | ratio | 0.04 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 50 | | matches | | 0 | "He didn't even glance back." | | 1 | "Her leather watch slapped against" | | 2 | "Her voice bounced off concrete" | | 3 | "She reached the bottom." | | 4 | "he called, his voice carrying" | | 5 | "He pulled something from his" | | 6 | "She'd dismissed it as departmental" | | 7 | "She stepped forward." | | 8 | "she said, her voice steady" | | 9 | "he said, not turning" | | 10 | "He finally turned, his face" |
| | ratio | 0.22 | |
| 70.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 50 | | matches | | 0 | "Rain sliced through the neon" | | 1 | "Detective Harlow Quinn dodged a" | | 2 | "The suspect—her only lead on" | | 3 | "He didn't even glance back." | | 4 | "Her leather watch slapped against" | | 5 | "The suspect turned left, then" | | 6 | "The steps descended into darkness," | | 7 | "Her voice bounced off concrete" | | 8 | "She reached the bottom." | | 9 | "A tunnel stretched ahead, lit" | | 10 | "The suspect stood fifty meters" | | 11 | "he called, his voice carrying" | | 12 | "The man laughed." | | 13 | "He pulled something from his" | | 14 | "The bricks shifted, revealing a" | | 15 | "Darker than the tunnel." | | 16 | "Quinn's hand rested on the" | | 17 | "She'd dismissed it as departmental" | | 18 | "The man stepped through the" | | 19 | "Quinn had heard whispers." |
| | ratio | 0.78 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 50 | | matches | | 0 | "Either way, nothing would ever" |
| | ratio | 0.02 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn dodged a puddle that swallowed a discarded newspaper whole." | | 1 | "Three years ago, she'd lost DS Morris under circumstances that defied explanation." | | 2 | "She'd dismissed it as departmental cover-up until tonight—the night she'd seen the suspect handle evidence that shouldn't exist." | | 3 | "A place where people traded in things that made her blood run cold despite her skepticism." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 3 | | matches | | 0 | "he called, his voice carrying in the confined space" | | 1 | "she said, her voice steady despite the rapid pulse in her throat" | | 2 | "he said, not turning" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "the man whispered (whisper)" |
| | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.25 | | effectiveRatio | 0.1 | |