| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1882 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 62.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1882 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "weight" | | 1 | "pulse" | | 2 | "echoed" | | 3 | "fractured" | | 4 | "traced" | | 5 | "silence" | | 6 | "shattered" | | 7 | "aligned" |
| |
| 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 | 328 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 2 | | narrationSentences | 328 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 328 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1882 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 99 | | wordCount | 1882 | | uniqueNames | 18 | | maxNameDensity | 2.02 | | worstName | "You" | | maxWindowNameDensity | 4.5 | | worstWindowName | "You" | | discoveredNames | | Eva | 2 | | Cheung | 1 | | Mesopotamian | 1 | | Sumerian | 1 | | French | 1 | | Silas | 1 | | Avaros | 1 | | Evan | 1 | | Cardiff | 1 | | London | 1 | | Rory | 17 | | Lucien | 17 | | You | 38 | | Ptolemy | 4 | | Water | 3 | | Fingers | 3 | | Skin | 3 | | Let | 3 |
| | persons | | 0 | "Eva" | | 1 | "Cheung" | | 2 | "Silas" | | 3 | "Avaros" | | 4 | "Evan" | | 5 | "Rory" | | 6 | "Lucien" | | 7 | "You" | | 8 | "Ptolemy" | | 9 | "Water" | | 10 | "Fingers" | | 11 | "Skin" |
| | places | | | globalScore | 0.49 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 142 | | 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 | 1882 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 328 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 77 | | mean | 24.44 | | std | 13.22 | | cv | 0.541 | | sampleLengths | | 0 | 57 | | 1 | 7 | | 2 | 38 | | 3 | 10 | | 4 | 22 | | 5 | 10 | | 6 | 18 | | 7 | 26 | | 8 | 22 | | 9 | 27 | | 10 | 36 | | 11 | 4 | | 12 | 41 | | 13 | 11 | | 14 | 41 | | 15 | 4 | | 16 | 17 | | 17 | 17 | | 18 | 34 | | 19 | 12 | | 20 | 18 | | 21 | 13 | | 22 | 20 | | 23 | 25 | | 24 | 27 | | 25 | 27 | | 26 | 27 | | 27 | 41 | | 28 | 23 | | 29 | 20 | | 30 | 22 | | 31 | 36 | | 32 | 18 | | 33 | 45 | | 34 | 24 | | 35 | 45 | | 36 | 36 | | 37 | 25 | | 38 | 17 | | 39 | 31 | | 40 | 32 | | 41 | 21 | | 42 | 25 | | 43 | 16 | | 44 | 27 | | 45 | 21 | | 46 | 22 | | 47 | 23 | | 48 | 28 | | 49 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 328 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 389 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 328 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1884 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.01751592356687898 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0005307855626326964 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 328 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 328 | | mean | 5.74 | | std | 3.28 | | cv | 0.571 | | sampleLengths | | 0 | 5 | | 1 | 5 | | 2 | 7 | | 3 | 5 | | 4 | 5 | | 5 | 7 | | 6 | 10 | | 7 | 7 | | 8 | 6 | | 9 | 7 | | 10 | 7 | | 11 | 12 | | 12 | 4 | | 13 | 5 | | 14 | 10 | | 15 | 7 | | 16 | 3 | | 17 | 3 | | 18 | 6 | | 19 | 7 | | 20 | 6 | | 21 | 7 | | 22 | 3 | | 23 | 4 | | 24 | 6 | | 25 | 4 | | 26 | 4 | | 27 | 3 | | 28 | 10 | | 29 | 13 | | 30 | 3 | | 31 | 6 | | 32 | 7 | | 33 | 6 | | 34 | 10 | | 35 | 6 | | 36 | 11 | | 37 | 3 | | 38 | 6 | | 39 | 5 | | 40 | 3 | | 41 | 4 | | 42 | 3 | | 43 | 5 | | 44 | 7 | | 45 | 4 | | 46 | 5 | | 47 | 5 | | 48 | 8 | | 49 | 8 |
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| 33.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 55 | | diversityRatio | 0.20426829268292682 | | totalSentences | 328 | | uniqueOpeners | 67 | |
| 45.35% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 294 | | matches | | 0 | "Then you packed your files" | | 1 | "Just the raw edge of" | | 2 | "Then let me put my" | | 3 | "Just pressure and heat and" |
| | ratio | 0.014 | |
| 15.92% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 150 | | totalSentences | 294 | | matches | | 0 | "His knuckles rested on the" | | 1 | "You pick a hell of" | | 2 | "She shifted her grip." | | 3 | "I waited for the rain" | | 4 | "It did not." | | 5 | "He did not cross the" | | 6 | "You want a towel or" | | 7 | "I stock neither." | | 8 | "She shoved it down." | | 9 | "I require neither." | | 10 | "I require the ledger Eva" | | 11 | "He adjusted his grip on" | | 12 | "She left it under a" | | 13 | "You can dig through it" | | 14 | "She left the gap narrow." | | 15 | "His shoulder brushed hers." | | 16 | "He smelled of bergamot and" | | 17 | "You moved the wards." | | 18 | "I just keep the paths" | | 19 | "He did not move to" |
| | ratio | 0.51 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 272 | | totalSentences | 294 | | matches | | 0 | "The final deadbolt clicked back." | | 1 | "Rory pulled the door open." | | 2 | "Lucien stood on the landing." | | 3 | "Charcoal wool brushed his thighs." | | 4 | "Platinum hair caught the weak" | | 5 | "His knuckles rested on the" | | 6 | "The other swallowed the dim" | | 7 | "You pick a hell of" | | 8 | "Rory kept her weight against" | | 9 | "The crescent scar on her" | | 10 | "She shifted her grip." | | 11 | "Ptolemy wound around her ankles." | | 12 | "Curry oil and old paper" | | 13 | "I waited for the rain" | | 14 | "It did not." | | 15 | "Lucien stepped forward." | | 16 | "The cane tip tapped the" | | 17 | "Water darkened the wool at" | | 18 | "He did not cross the" | | 19 | "You want a towel or" |
| | ratio | 0.925 | |
| 17.01% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 294 | | matches | | | ratio | 0.003 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | 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 | |