| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said finally [finally]" |
| | dialogueSentences | 49 | | tagDensity | 0.367 | | leniency | 0.735 | | rawRatio | 0.056 | | effectiveRatio | 0.041 | |
| 92.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1330 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 32.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1330 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "echoing" | | 1 | "weight" | | 2 | "intensity" | | 3 | "implication" | | 4 | "silence" | | 5 | "charged" | | 6 | "unspoken" | | 7 | "familiar" | | 8 | "trembled" | | 9 | "scanning" | | 10 | "could feel" | | 11 | "tracing" | | 12 | "resolve" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
| | 2 | | label | "sent a shiver through" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "hung in the air" | | 2 | "sent a jolt through" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 68 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 68 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1325 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 54.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 894 | | uniqueNames | 7 | | maxNameDensity | 1.9 | | worstName | "Eva" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 17 | | Oxford | 1 | | French | 1 | | London | 1 | | Marseille | 1 | | Lucien | 7 | | Order | 1 |
| | persons | | | places | | 0 | "Oxford" | | 1 | "London" | | 2 | "Marseille" |
| | globalScore | 0.549 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like dried blood" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1325 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 98 | | matches | | |
| 92.73% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 30.81 | | std | 14.62 | | cv | 0.475 | | sampleLengths | | 0 | 40 | | 1 | 27 | | 2 | 39 | | 3 | 20 | | 4 | 57 | | 5 | 27 | | 6 | 20 | | 7 | 46 | | 8 | 16 | | 9 | 23 | | 10 | 6 | | 11 | 44 | | 12 | 47 | | 13 | 36 | | 14 | 39 | | 15 | 28 | | 16 | 57 | | 17 | 54 | | 18 | 28 | | 19 | 28 | | 20 | 31 | | 21 | 32 | | 22 | 72 | | 23 | 4 | | 24 | 27 | | 25 | 33 | | 26 | 16 | | 27 | 51 | | 28 | 11 | | 29 | 31 | | 30 | 34 | | 31 | 14 | | 32 | 31 | | 33 | 33 | | 34 | 21 | | 35 | 13 | | 36 | 40 | | 37 | 13 | | 38 | 30 | | 39 | 44 | | 40 | 30 | | 41 | 14 | | 42 | 18 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 160 | | matches | | |
| 55.39% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 98 | | ratio | 0.031 | | matches | | 0 | "Her gaze swept over the tailored charcoal suit, the slicked-back platinum blond hair, the heterochromatic eyes—one amber, one black—that held hers with unnerving intensity." | | 1 | "Eva could smell his familiar cologne—sandalwood and something metallic, like ozone after a storm—and it sent a jolt through her that she refused to acknowledge." | | 2 | "The touch sent a cascade of memories through her—late nights, whispered secrets, the taste of his kiss on her skin." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 901 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.03440621531631521 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009988901220865706 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 13.52 | | std | 7.7 | | cv | 0.57 | | sampleLengths | | 0 | 28 | | 1 | 12 | | 2 | 22 | | 3 | 5 | | 4 | 3 | | 5 | 24 | | 6 | 12 | | 7 | 15 | | 8 | 5 | | 9 | 10 | | 10 | 28 | | 11 | 19 | | 12 | 9 | | 13 | 18 | | 14 | 16 | | 15 | 4 | | 16 | 18 | | 17 | 28 | | 18 | 12 | | 19 | 4 | | 20 | 13 | | 21 | 10 | | 22 | 4 | | 23 | 2 | | 24 | 19 | | 25 | 25 | | 26 | 13 | | 27 | 15 | | 28 | 19 | | 29 | 7 | | 30 | 2 | | 31 | 18 | | 32 | 9 | | 33 | 31 | | 34 | 8 | | 35 | 11 | | 36 | 12 | | 37 | 5 | | 38 | 28 | | 39 | 29 | | 40 | 8 | | 41 | 16 | | 42 | 13 | | 43 | 17 | | 44 | 14 | | 45 | 14 | | 46 | 2 | | 47 | 26 | | 48 | 12 | | 49 | 19 |
| |
| 46.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.336734693877551 | | totalSentences | 98 | | uniqueOpeners | 33 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 66 | | matches | | 0 | "Instead, he reached out and" | | 1 | "Maybe she was a fool" | | 2 | "Maybe she was asking for" |
| | ratio | 0.045 | |
| 50.30% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 66 | | matches | | 0 | "She didn't move." | | 1 | "Her gaze swept over the" | | 2 | "His voice was smooth as" | | 3 | "He'd left her with nothing" | | 4 | "She hesitated, her fingers tightening" | | 5 | "He surveyed the flat, his" | | 6 | "he remarked, his voice devoid" | | 7 | "He winced almost imperceptibly." | | 8 | "They stood in the narrow" | | 9 | "His amber eye, the one" | | 10 | "He reached into his jacket" | | 11 | "She hadn't seen her mother's" | | 12 | "He held it out to" | | 13 | "She flipped through the pages," | | 14 | "she murmured, more to herself" | | 15 | "He met her gaze directly," | | 16 | "She remembered the way he'd" | | 17 | "she whispered, the words catching" | | 18 | "He reached out, his fingers" | | 19 | "she asked, her voice barely" |
| | ratio | 0.424 | |
| 13.03% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 66 | | matches | | 0 | "The three deadbolts on the" | | 1 | "Eva stood frozen, hand still" | | 2 | "The man on the other" | | 3 | "She didn't move." | | 4 | "Her gaze swept over the" | | 5 | "His voice was smooth as" | | 6 | "The word hung in the" | | 7 | "Eva remembered the last time" | | 8 | "He'd left her with nothing" | | 9 | "She hesitated, her fingers tightening" | | 10 | "Ptolemy, the tabby cat, wound" | | 11 | "Eva said finally, swinging the" | | 12 | "Lucien moved with the predatory" | | 13 | "He surveyed the flat, his" | | 14 | "he remarked, his voice devoid" | | 15 | "Eva shot back, closing the" | | 16 | "He winced almost imperceptibly." | | 17 | "They stood in the narrow" | | 18 | "Eva could smell his familiar" | | 19 | "Lucien said, breaking the silence" |
| | ratio | 0.894 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 9 | | matches | | 0 | "he remarked, his voice devoid of judgment" | | 1 | "she murmured, more to herself than to him" | | 2 | "Eva said, her voice tight with suppressed emotion" | | 3 | "she whispered, the words catching in her throat" | | 4 | "she asked, her voice barely audible" | | 5 | "he said, his thumb tracing the line of her jaw" | | 6 | "Lucien said, his voice firm" | | 7 | "she reminded, her voice cold" | | 8 | "she repeated, her voice softer this time" |
| |
| 88.78% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 3 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "she whispered (whisper)" | | 2 | "she repeated (repeat)" |
| | dialogueSentences | 49 | | tagDensity | 0.224 | | leniency | 0.449 | | rawRatio | 0.273 | | effectiveRatio | 0.122 | |