| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva pulled back [back]" |
| | dialogueSentences | 44 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0.063 | | effectiveRatio | 0.045 | |
| 80.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1039 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "carefully" | | 1 | "suddenly" | | 2 | "very" | | 3 | "really" |
| |
| 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) | |
| 71.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1039 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "warmth" | | 2 | "silence" | | 3 | "weight" | | 4 | "traced" | | 5 | "familiar" |
| |
| 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 | 48 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 48 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 76 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 60 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1038 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 560 | | uniqueNames | 5 | | maxNameDensity | 2.32 | | worstName | "Eva" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 11 | | Eva | 13 | | Silas | 5 |
| | persons | | 0 | "Raven" | | 1 | "Aurora" | | 2 | "Eva" | | 3 | "Silas" |
| | places | (empty) | | globalScore | 0.339 | | windowScore | 0.333 | |
| 80.56% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 36 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like she'd never been cold in her" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1038 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 76 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 26.62 | | std | 22.86 | | cv | 0.859 | | sampleLengths | | 0 | 67 | | 1 | 40 | | 2 | 1 | | 3 | 16 | | 4 | 1 | | 5 | 68 | | 6 | 18 | | 7 | 7 | | 8 | 22 | | 9 | 24 | | 10 | 31 | | 11 | 36 | | 12 | 81 | | 13 | 30 | | 14 | 9 | | 15 | 8 | | 16 | 22 | | 17 | 47 | | 18 | 11 | | 19 | 2 | | 20 | 2 | | 21 | 31 | | 22 | 56 | | 23 | 6 | | 24 | 49 | | 25 | 18 | | 26 | 4 | | 27 | 34 | | 28 | 30 | | 29 | 16 | | 30 | 25 | | 31 | 100 | | 32 | 29 | | 33 | 12 | | 34 | 29 | | 35 | 2 | | 36 | 5 | | 37 | 30 | | 38 | 19 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 48 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 106 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 76 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 563 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.028419182948490232 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.007104795737122558 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 76 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 76 | | mean | 13.66 | | std | 11.29 | | cv | 0.826 | | sampleLengths | | 0 | 13 | | 1 | 33 | | 2 | 7 | | 3 | 14 | | 4 | 16 | | 5 | 24 | | 6 | 1 | | 7 | 9 | | 8 | 4 | | 9 | 3 | | 10 | 1 | | 11 | 7 | | 12 | 30 | | 13 | 31 | | 14 | 14 | | 15 | 4 | | 16 | 7 | | 17 | 22 | | 18 | 11 | | 19 | 13 | | 20 | 22 | | 21 | 9 | | 22 | 10 | | 23 | 20 | | 24 | 3 | | 25 | 3 | | 26 | 2 | | 27 | 17 | | 28 | 30 | | 29 | 32 | | 30 | 8 | | 31 | 22 | | 32 | 9 | | 33 | 8 | | 34 | 13 | | 35 | 9 | | 36 | 6 | | 37 | 41 | | 38 | 11 | | 39 | 2 | | 40 | 2 | | 41 | 7 | | 42 | 24 | | 43 | 7 | | 44 | 41 | | 45 | 8 | | 46 | 6 | | 47 | 11 | | 48 | 38 | | 49 | 9 |
| |
| 71.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4605263157894737 | | totalSentences | 76 | | uniqueOpeners | 35 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 42 | | matches | | 0 | "Then she crossed the room" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 42 | | matches | | 0 | "She'd done this run a" | | 1 | "She knew the voice before" | | 2 | "He poured a measure of" | | 3 | "She took it." | | 4 | "They sat with that for" | | 5 | "She laughed, but there was" | | 6 | "She heard the edge come" | | 7 | "She traced the rim of" | | 8 | "She nudged her glass against" |
| | ratio | 0.214 | |
| 19.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 42 | | matches | | 0 | "The green neon buzzed overhead," | | 1 | "Aurora pushed through the door" | | 2 | "She'd done this run a" | | 3 | "Silas always tipped in stories" | | 4 | "The bar was half-empty, the" | | 5 | "Somebody at the far end" | | 6 | "She knew the voice before" | | 7 | "The face had changed." | | 8 | "The voice hadn't." | | 9 | "The woman at the bar" | | 10 | "Eva pulled back, held her" | | 11 | "Eva said it carefully, the" | | 12 | "Aurora set the bags on" | | 13 | "Eva returned to her stool" | | 14 | "He poured a measure of" | | 15 | "A small kindness." | | 16 | "She took it." | | 17 | "Eva had grown into something" | | 18 | "The girl who'd once shared" | | 19 | "Aurora turned the glass on" |
| | ratio | 0.881 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 42 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 17 | | technicalSentenceCount | 2 | | matches | | 0 | "Then she crossed the room and Aurora let the bags hang at her sides while Eva folded her into an embrace that smelled of expensive perfume and cigarette smoke l…" | | 1 | "Eva had grown into something sleek and finished, her dark hair cut sharp at the jaw, a watch on her wrist that cost more than Aurora made in a season." |
| |
| 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 | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 44 | | tagDensity | 0.159 | | leniency | 0.318 | | rawRatio | 0.143 | | effectiveRatio | 0.045 | |