| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.381 | | leniency | 0.762 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1075 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 76.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1075 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "footsteps" | | 1 | "flicked" | | 2 | "perfect" | | 3 | "flickered" |
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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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1075 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 755 | | uniqueNames | 4 | | maxNameDensity | 1.46 | | worstName | "Patel" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Patel" | | discoveredNames | | Town | 1 | | Quinn | 8 | | Patel | 11 | | Morris | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Patel" | | 2 | "Morris" |
| | places | | | globalScore | 0.772 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | 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 | 1075 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 119 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 22.4 | | std | 18.27 | | cv | 0.816 | | sampleLengths | | 0 | 25 | | 1 | 42 | | 2 | 23 | | 3 | 2 | | 4 | 10 | | 5 | 48 | | 6 | 41 | | 7 | 10 | | 8 | 24 | | 9 | 40 | | 10 | 17 | | 11 | 4 | | 12 | 37 | | 13 | 11 | | 14 | 4 | | 15 | 2 | | 16 | 55 | | 17 | 6 | | 18 | 2 | | 19 | 35 | | 20 | 26 | | 21 | 29 | | 22 | 7 | | 23 | 26 | | 24 | 6 | | 25 | 13 | | 26 | 1 | | 27 | 53 | | 28 | 6 | | 29 | 35 | | 30 | 13 | | 31 | 40 | | 32 | 3 | | 33 | 63 | | 34 | 10 | | 35 | 6 | | 36 | 67 | | 37 | 14 | | 38 | 12 | | 39 | 8 | | 40 | 58 | | 41 | 10 | | 42 | 47 | | 43 | 27 | | 44 | 14 | | 45 | 3 | | 46 | 21 | | 47 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 138 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 119 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 755 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.026490066225165563 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0026490066225165563 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 9.03 | | std | 7.29 | | cv | 0.807 | | sampleLengths | | 0 | 25 | | 1 | 10 | | 2 | 14 | | 3 | 9 | | 4 | 9 | | 5 | 11 | | 6 | 12 | | 7 | 2 | | 8 | 10 | | 9 | 7 | | 10 | 15 | | 11 | 2 | | 12 | 6 | | 13 | 13 | | 14 | 1 | | 15 | 4 | | 16 | 12 | | 17 | 9 | | 18 | 3 | | 19 | 17 | | 20 | 4 | | 21 | 6 | | 22 | 5 | | 23 | 19 | | 24 | 8 | | 25 | 32 | | 26 | 3 | | 27 | 6 | | 28 | 8 | | 29 | 4 | | 30 | 17 | | 31 | 20 | | 32 | 7 | | 33 | 4 | | 34 | 2 | | 35 | 2 | | 36 | 2 | | 37 | 13 | | 38 | 8 | | 39 | 8 | | 40 | 2 | | 41 | 2 | | 42 | 6 | | 43 | 16 | | 44 | 6 | | 45 | 2 | | 46 | 7 | | 47 | 28 | | 48 | 6 | | 49 | 8 |
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| 82.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5294117647058824 | | totalSentences | 119 | | uniqueOpeners | 63 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 77 | | matches | | 0 | "His eyes had gone milky," | | 1 | "She pulled on a glove" | | 2 | "She knew that token." | | 3 | "He flicked the torch off" | | 4 | "She turned the bone token" | | 5 | "She rose and faced him" | | 6 | "She crossed back to the" | | 7 | "She held up the token" | | 8 | "She let the word sit" | | 9 | "She turned it so the" | | 10 | "She'd seen their like once" | | 11 | "She kept her face still." | | 12 | "She tucked it into an" | | 13 | "She spoke first." | | 14 | "He let it go." | | 15 | "She stopped where the lamp" | | 16 | "They came from the booth," | | 17 | "Her knees protested." | | 18 | "She thought of the token" | | 19 | "She started toward the tunnel" |
| | ratio | 0.286 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 77 | | matches | | 0 | "The body sat upright in" | | 1 | "Quinn ducked under the tape" | | 2 | "Camden Town's disused level, sealed" | | 3 | "Someone had strung work lights" | | 4 | "The glare turned everything the" | | 5 | "A young constable hovered near" | | 6 | "Quinn crouched in front of" | | 7 | "The dead man wore a" | | 8 | "His eyes had gone milky," | | 9 | "She pulled on a glove" | | 10 | "The fingers moved." | | 11 | "She knew that token." | | 12 | "Footsteps splashed up the tunnel." | | 13 | "Patel emerged, picking his way" | | 14 | "He flicked the torch off" | | 15 | "Quinn didn't answer." | | 16 | "She turned the bone token" | | 17 | "Sigils ringed its edge, scratched" | | 18 | "Patel nodded down the tunnel," | | 19 | "She rose and faced him" |
| | ratio | 0.701 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 77 | | matches | | 0 | "To his credit, he looked" | | 1 | "As if the walker had" |
| | ratio | 0.026 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "Quinn watched the dead man's milky eyes and the dust and the single line of footprints that began nowhere and ended at a drop into black, and she felt the cold …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 42 | | tagDensity | 0.095 | | leniency | 0.19 | | rawRatio | 0.25 | | effectiveRatio | 0.048 | |