| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 100 | | tagDensity | 0.11 | | leniency | 0.22 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.70% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1513 | | 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) | |
| 90.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1513 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "pulsed" | | 1 | "flickered" | | 2 | "silence" |
| |
| 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 | 89 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 89 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 178 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1513 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 833 | | uniqueNames | 7 | | maxNameDensity | 3 | | worstName | "Eva" | | maxWindowNameDensity | 7 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 22 | | Silas | 6 | | Evan | 1 | | Cardiff | 1 | | Eva | 25 |
| | persons | | 0 | "Aurora" | | 1 | "Silas" | | 2 | "Evan" | | 3 | "Eva" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed ordinary until she tilted her head, counting them like steps" | | 1 | "as if checking whether it had returned to the wrong side of her face" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1513 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 178 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 120 | | mean | 12.61 | | std | 14.28 | | cv | 1.132 | | sampleLengths | | 0 | 44 | | 1 | 71 | | 2 | 7 | | 3 | 7 | | 4 | 12 | | 5 | 41 | | 6 | 10 | | 7 | 67 | | 8 | 1 | | 9 | 28 | | 10 | 2 | | 11 | 19 | | 12 | 3 | | 13 | 14 | | 14 | 3 | | 15 | 32 | | 16 | 4 | | 17 | 25 | | 18 | 7 | | 19 | 9 | | 20 | 42 | | 21 | 5 | | 22 | 6 | | 23 | 5 | | 24 | 4 | | 25 | 10 | | 26 | 7 | | 27 | 4 | | 28 | 23 | | 29 | 5 | | 30 | 7 | | 31 | 18 | | 32 | 1 | | 33 | 7 | | 34 | 9 | | 35 | 46 | | 36 | 6 | | 37 | 2 | | 38 | 1 | | 39 | 13 | | 40 | 6 | | 41 | 8 | | 42 | 19 | | 43 | 2 | | 44 | 3 | | 45 | 55 | | 46 | 3 | | 47 | 10 | | 48 | 5 | | 49 | 11 |
| |
| 97.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 89 | | matches | | 0 | "was gone" | | 1 | "been cleaned" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 154 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 178 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 836 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.025119617224880382 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004784688995215311 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 178 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 178 | | mean | 8.5 | | std | 7.66 | | cv | 0.901 | | sampleLengths | | 0 | 12 | | 1 | 19 | | 2 | 13 | | 3 | 10 | | 4 | 7 | | 5 | 22 | | 6 | 11 | | 7 | 8 | | 8 | 13 | | 9 | 7 | | 10 | 7 | | 11 | 12 | | 12 | 7 | | 13 | 34 | | 14 | 10 | | 15 | 11 | | 16 | 17 | | 17 | 15 | | 18 | 24 | | 19 | 1 | | 20 | 5 | | 21 | 3 | | 22 | 20 | | 23 | 2 | | 24 | 4 | | 25 | 9 | | 26 | 6 | | 27 | 3 | | 28 | 14 | | 29 | 3 | | 30 | 4 | | 31 | 4 | | 32 | 24 | | 33 | 4 | | 34 | 7 | | 35 | 3 | | 36 | 15 | | 37 | 7 | | 38 | 9 | | 39 | 11 | | 40 | 20 | | 41 | 11 | | 42 | 5 | | 43 | 6 | | 44 | 5 | | 45 | 4 | | 46 | 5 | | 47 | 5 | | 48 | 3 | | 49 | 4 |
| |
| 43.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.29775280898876405 | | totalSentences | 178 | | uniqueOpeners | 53 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 78 | | matches | | 0 | "Bright, guarded eyes." | | 1 | "Then the surface hardened again." |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 78 | | matches | | 0 | "His left foot rested at" | | 1 | "Her flat above the bar" | | 2 | "She looked across the room" | | 3 | "Her coat was charcoal and" | | 4 | "Her hair, once long and" | | 5 | "Her boots left wet prints" | | 6 | "It was empty." | | 7 | "He set one near Eva." | | 8 | "Her nails had been cleaned" | | 9 | "Her throat moved." | | 10 | "She looked at the empty" | | 11 | "She looked into it as" | | 12 | "It had become the inside" | | 13 | "They were grey-brown under the" |
| | ratio | 0.179 | |
| 11.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 78 | | matches | | 0 | "Rain had flattened the street" | | 1 | "The green neon sign above" | | 2 | "Aurora pulled off her delivery" | | 3 | "Silas stood behind the bar" | | 4 | "His left foot rested at" | | 5 | "The silver ring on his" | | 6 | "Aurora dropped her satchel by" | | 7 | "Her flat above the bar" | | 8 | "She looked across the room" | | 9 | "A woman sat alone in" | | 10 | "Her coat was charcoal and" | | 11 | "Her hair, once long and" | | 12 | "A thin pale line ran" | | 13 | "The woman lifted her face." | | 14 | "The mouth Aurora remembered from" | | 15 | "Aurora crossed the floor." | | 16 | "Her boots left wet prints" | | 17 | "Aurora sat opposite her." | | 18 | "The vinyl booth squeaked." | | 19 | "Eva moved her glass half" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "Black-and-white photographs lined the shelves: men in coats, women in hats, faces that looked as if they had learned how to wait." | | 1 | "For a second, the new carefulness slipped, and Aurora saw the girl who had packed sandwiches into her schoolbag and told her to eat before the prefects saw." | | 2 | "They were grey-brown under the neon and carried a weariness that had outlasted youth, distance, and every friendly joke they had ever relied on to soften the tr…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 100 | | tagDensity | 0.11 | | leniency | 0.22 | | rawRatio | 0 | | effectiveRatio | 0 | |