| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.46% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1102 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 68.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1102 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "familiar" | | 3 | "throbbed" | | 4 | "trembled" | | 5 | "mechanical" |
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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 | 72 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 72 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1102 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 784 | | uniqueNames | 10 | | maxNameDensity | 2.42 | | worstName | "Rory" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 19 | | Raven | 1 | | Nest | 1 | | Prague | 1 | | Caucasus | 2 | | Eva | 17 | | Cardiff | 2 | | Evan | 2 | | Aurora | 2 | | Silas | 3 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Eva" | | 3 | "Evan" | | 4 | "Aurora" | | 5 | "Silas" |
| | places | | | globalScore | 0.288 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1102 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 96 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 22.96 | | std | 17.15 | | cv | 0.747 | | sampleLengths | | 0 | 76 | | 1 | 36 | | 2 | 58 | | 3 | 13 | | 4 | 5 | | 5 | 24 | | 6 | 61 | | 7 | 10 | | 8 | 4 | | 9 | 3 | | 10 | 14 | | 11 | 34 | | 12 | 5 | | 13 | 24 | | 14 | 14 | | 15 | 4 | | 16 | 12 | | 17 | 13 | | 18 | 9 | | 19 | 12 | | 20 | 37 | | 21 | 15 | | 22 | 17 | | 23 | 4 | | 24 | 24 | | 25 | 31 | | 26 | 46 | | 27 | 10 | | 28 | 16 | | 29 | 21 | | 30 | 16 | | 31 | 26 | | 32 | 18 | | 33 | 36 | | 34 | 8 | | 35 | 38 | | 36 | 11 | | 37 | 16 | | 38 | 66 | | 39 | 5 | | 40 | 9 | | 41 | 34 | | 42 | 27 | | 43 | 26 | | 44 | 39 | | 45 | 42 | | 46 | 20 | | 47 | 13 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 72 | | matches | | 0 | "being asked" | | 1 | "been named" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 133 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 96 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 790 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.0189873417721519 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0037974683544303796 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 11.48 | | std | 8.14 | | cv | 0.709 | | sampleLengths | | 0 | 14 | | 1 | 17 | | 2 | 17 | | 3 | 9 | | 4 | 19 | | 5 | 14 | | 6 | 22 | | 7 | 17 | | 8 | 9 | | 9 | 13 | | 10 | 19 | | 11 | 13 | | 12 | 5 | | 13 | 20 | | 14 | 4 | | 15 | 20 | | 16 | 23 | | 17 | 18 | | 18 | 3 | | 19 | 7 | | 20 | 4 | | 21 | 3 | | 22 | 7 | | 23 | 6 | | 24 | 1 | | 25 | 7 | | 26 | 27 | | 27 | 5 | | 28 | 21 | | 29 | 3 | | 30 | 5 | | 31 | 7 | | 32 | 2 | | 33 | 4 | | 34 | 12 | | 35 | 2 | | 36 | 6 | | 37 | 5 | | 38 | 9 | | 39 | 12 | | 40 | 3 | | 41 | 34 | | 42 | 15 | | 43 | 10 | | 44 | 7 | | 45 | 4 | | 46 | 13 | | 47 | 11 | | 48 | 11 | | 49 | 5 |
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| 46.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3125 | | totalSentences | 96 | | uniqueOpeners | 30 | |
| 51.28% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 65 | | matches | | | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 65 | | matches | | 0 | "She shook the water from" | | 1 | "His grey-streaked auburn beard caught" | | 2 | "His left leg dragged a" | | 3 | "His voice carried the gravel" | | 4 | "She slid onto the stool" | | 5 | "Her hair, once a wild" | | 6 | "She held a glass of" | | 7 | "They had not spoken since" | | 8 | "She did not hide it." | | 9 | "She only acknowledged it, the" | | 10 | "His hazel eyes met Rory’s" | | 11 | "She walked toward the door." | | 12 | "His presence filled the space" | | 13 | "She thought of Cardiff." | | 14 | "She thought of Eva’s letter," | | 15 | "She thought of her mother’s" | | 16 | "It settled, slow and certain," | | 17 | "She did not finish the" |
| | ratio | 0.277 | |
| 29.23% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 65 | | matches | | 0 | "The green neon above the" | | 1 | "She shook the water from" | | 2 | "The crescent scar on her" | | 3 | "Silas stood behind the mahogany" | | 4 | "His grey-streaked auburn beard caught" | | 5 | "The silver signet ring on" | | 6 | "His left leg dragged a" | | 7 | "His voice carried the gravel" | | 8 | "She slid onto the stool" | | 9 | "Eva sat at the corner" | | 10 | "Her hair, once a wild" | | 11 | "She held a glass of" | | 12 | "Eva looked up." | | 13 | "The glass stopped halfway to" | | 14 | "Rory’s fingers tightened around her" | | 15 | "The cold bit through her" | | 16 | "Years collapsed into the space" | | 17 | "They had not spoken since" | | 18 | "Rory glanced at the leather" | | 19 | "Eva set her glass down." |
| | ratio | 0.862 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 27.03% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 6 | | matches | | 0 | "Ancient maps curled behind glass frames, countries renamed by war, borders drawn in ink that had long since faded." | | 1 | "Silas stood behind the mahogany bar, polishing a glass with a rag that had seen better decades." | | 2 | "Eva sat at the corner table, half-hidden by a pillar, wearing a coat that cost more than Rory’s monthly rent." | | 3 | "Rory glanced at the leather bag beside Eva’s chair, at the heels that had replaced the scuffed trainers of their youth." | | 4 | "Eva stood, smoothing her coat with hands that trembled despite her practice." | | 5 | "Inside, the black-and-white photographs watched, silent and unchanging, witnesses to every departure that had ever been named regret." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |