| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 564 | | 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) | |
| 11.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 564 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "racing" | | 1 | "wavered" | | 2 | "quickened" | | 3 | "determined" | | 4 | "scanning" | | 5 | "echoing" | | 6 | "could feel" | | 7 | "pounding" | | 8 | "velvet" |
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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 | 1 | | narrationSentences | 42 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 42 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 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 | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 566 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.99% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 566 | | uniqueNames | 4 | | maxNameDensity | 2.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Veil | 1 | | Market | 1 |
| | persons | | | places | (empty) | | globalScore | 0.44 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | 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 | 566 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 42 | | matches | (empty) | |
| 5.05% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 11 | | mean | 51.45 | | std | 8.63 | | cv | 0.168 | | sampleLengths | | 0 | 73 | | 1 | 46 | | 2 | 50 | | 3 | 49 | | 4 | 57 | | 5 | 56 | | 6 | 55 | | 7 | 44 | | 8 | 52 | | 9 | 39 | | 10 | 45 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 42 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 89 | | matches | (empty) | |
| 6.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 42 | | ratio | 0.048 | | matches | | 0 | "That's when she saw it - a faint outline in the brickwork, barely visible in the dim light." | | 1 | "The market was crowded with people - creatures - of all shapes and sizes." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 564 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.01773049645390071 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0070921985815602835 | |
| 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 | 13.48 | | std | 5.49 | | cv | 0.407 | | sampleLengths | | 0 | 17 | | 1 | 21 | | 2 | 18 | | 3 | 17 | | 4 | 15 | | 5 | 8 | | 6 | 11 | | 7 | 12 | | 8 | 19 | | 9 | 14 | | 10 | 17 | | 11 | 9 | | 12 | 15 | | 13 | 7 | | 14 | 18 | | 15 | 18 | | 16 | 13 | | 17 | 1 | | 18 | 8 | | 19 | 17 | | 20 | 8 | | 21 | 20 | | 22 | 15 | | 23 | 13 | | 24 | 11 | | 25 | 17 | | 26 | 27 | | 27 | 9 | | 28 | 15 | | 29 | 20 | | 30 | 15 | | 31 | 18 | | 32 | 3 | | 33 | 16 | | 34 | 14 | | 35 | 8 | | 36 | 17 | | 37 | 1 | | 38 | 15 | | 39 | 5 | | 40 | 9 | | 41 | 15 |
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| 46.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3333333333333333 | | totalSentences | 42 | | uniqueOpeners | 14 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 40 | | matches | | 0 | "Her boots slapped against the" | | 1 | "She should have called for" | | 2 | "She rounded the corner and" | | 3 | "She pressed it, and with" | | 4 | "She had no idea where" | | 5 | "She moved cautiously, one hand" | | 6 | "She rounded a corner and" | | 7 | "She rounded the stall, reaching" |
| | ratio | 0.2 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 35 | | totalSentences | 40 | | matches | | 0 | "The rain pounded against the" | | 1 | "Detective Harlow Quinn ran through" | | 2 | "The suspect darted through alleys" | | 3 | "Her boots slapped against the" | | 4 | "The suspect glanced back, their" | | 5 | "Quinn followed, her breath coming" | | 6 | "She should have called for" | | 7 | "The suspect had information she" | | 8 | "The alley twisted and turned," | | 9 | "The buildings here were old" | | 10 | "She rounded the corner and" | | 11 | "The alley ended abruptly in" | | 12 | "The suspect was nowhere to" | | 13 | "Quinn cursed under her breath," | | 14 | "That's when she saw it" | | 15 | "Quinn ran her fingers along" | | 16 | "A small, metal button, hidden" | | 17 | "She pressed it, and with" | | 18 | "Quinn hesitated for a moment," | | 19 | "She had no idea where" |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |