| 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 | 2513 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 90.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2513 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "porcelain" | | 1 | "weight" | | 2 | "traced" | | 3 | "trembled" | | 4 | "sense of" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 128 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 128 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 259 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2513 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 27 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 79 | | wordCount | 1085 | | uniqueNames | 10 | | maxNameDensity | 2.95 | | worstName | "Aurora" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Blackwood | 1 | | Prague | 1 | | Aurora | 32 | | Silas | 14 | | Eva | 26 |
| | persons | | 0 | "Carter" | | 1 | "Blackwood" | | 2 | "Aurora" | | 3 | "Silas" | | 4 | "Eva" |
| | places | | | globalScore | 0.025 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | 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 | 2513 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 259 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 206 | | mean | 12.2 | | std | 8.9 | | cv | 0.729 | | sampleLengths | | 0 | 21 | | 1 | 22 | | 2 | 39 | | 3 | 30 | | 4 | 26 | | 5 | 29 | | 6 | 4 | | 7 | 16 | | 8 | 22 | | 9 | 21 | | 10 | 2 | | 11 | 7 | | 12 | 10 | | 13 | 10 | | 14 | 14 | | 15 | 16 | | 16 | 6 | | 17 | 9 | | 18 | 4 | | 19 | 5 | | 20 | 12 | | 21 | 5 | | 22 | 11 | | 23 | 6 | | 24 | 11 | | 25 | 15 | | 26 | 11 | | 27 | 11 | | 28 | 11 | | 29 | 38 | | 30 | 18 | | 31 | 3 | | 32 | 15 | | 33 | 7 | | 34 | 5 | | 35 | 17 | | 36 | 18 | | 37 | 17 | | 38 | 1 | | 39 | 30 | | 40 | 1 | | 41 | 2 | | 42 | 5 | | 43 | 15 | | 44 | 2 | | 45 | 2 | | 46 | 20 | | 47 | 8 | | 48 | 12 | | 49 | 9 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 128 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 171 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 259 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1088 | | adjectiveStacks | 1 | | stackExamples | | 0 | "white against wet skin." |
| | adverbCount | 10 | | adverbRatio | 0.009191176470588236 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 259 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 259 | | mean | 9.7 | | std | 7 | | cv | 0.722 | | sampleLengths | | 0 | 21 | | 1 | 22 | | 2 | 8 | | 3 | 7 | | 4 | 24 | | 5 | 14 | | 6 | 16 | | 7 | 7 | | 8 | 12 | | 9 | 7 | | 10 | 11 | | 11 | 18 | | 12 | 4 | | 13 | 8 | | 14 | 8 | | 15 | 22 | | 16 | 5 | | 17 | 16 | | 18 | 2 | | 19 | 7 | | 20 | 10 | | 21 | 10 | | 22 | 9 | | 23 | 5 | | 24 | 16 | | 25 | 6 | | 26 | 9 | | 27 | 4 | | 28 | 5 | | 29 | 7 | | 30 | 5 | | 31 | 5 | | 32 | 11 | | 33 | 6 | | 34 | 11 | | 35 | 15 | | 36 | 11 | | 37 | 11 | | 38 | 3 | | 39 | 8 | | 40 | 11 | | 41 | 11 | | 42 | 7 | | 43 | 9 | | 44 | 6 | | 45 | 12 | | 46 | 3 | | 47 | 15 | | 48 | 7 | | 49 | 5 |
| |
| 43.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.25096525096525096 | | totalSentences | 259 | | uniqueOpeners | 65 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 125 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 125 | | matches | | 0 | "She twisted the hem of" | | 1 | "His grey-streaked auburn hair stuck" | | 2 | "He set it back on" | | 3 | "Her hair ended at her" | | 4 | "His hazel eyes narrowed at" | | 5 | "He slid both toward her" | | 6 | "She plugged in her phone" | | 7 | "Her mouth opened." | | 8 | "She turned her head toward" | | 9 | "Her throat closed around a" | | 10 | "Her stool scraped the floor." | | 11 | "She grabbed Aurora around the" | | 12 | "Her hands stayed on Aurora's" | | 13 | "She looked at Aurora's jacket" | | 14 | "She stared at the phone" | | 15 | "Her fingers drummed a rhythm" | | 16 | "He dried a glass with" | | 17 | "Her phone lit up on" | | 18 | "She turned the phone face" | | 19 | "She blew across the surface." |
| | ratio | 0.248 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 121 | | totalSentences | 125 | | matches | | 0 | "Aurora Carter pushed through the" | | 1 | "Water ran from her jacket" | | 2 | "A dark line spread beside" | | 3 | "She twisted the hem of" | | 4 | "Silas Blackwood stood behind the" | | 5 | "The silver signet ring on" | | 6 | "Maps covered the walls between" | | 7 | "Men in heavy coats stood" | | 8 | "A woman held a bicycle" | | 9 | "Silas favored his left leg" | | 10 | "His grey-streaked auburn hair stuck" | | 11 | "Aurora dumped the delivery bag" | | 12 | "The bag knocked a coaster" | | 13 | "Silas picked up the coaster." | | 14 | "He set it back on" | | 15 | "Silas filled a mug and" | | 16 | "Steam curled past his beard." | | 17 | "Aurora wrapped both hands around" | | 18 | "Aurora lifted the mug and" | | 19 | "The door opened." |
| | ratio | 0.968 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 125 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | 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 | |