| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.327 | | leniency | 0.653 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1106 | | 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) | |
| 14.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1106 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | word | "skipped a beat" | | count | 1 |
| | 7 | | | 8 | | word | "down her spine" | | count | 1 |
| | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "fleeting" | | 1 | "unspoken" | | 2 | "tension" | | 3 | "measured" | | 4 | "encounter" | | 5 | "wavering" | | 6 | "skipped a beat" | | 7 | "chill" | | 8 | "down her spine" | | 9 | "flicker" | | 10 | "weight" | | 11 | "intensity" | | 12 | "race" | | 13 | "warmth" | | 14 | "silence" | | 15 | "raced" | | 16 | "scanning" | | 17 | "sense of" |
| |
| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "weight of words/silence" | | count | 1 |
| | 1 | | label | "sent a shiver through" | | count | 1 |
| | 2 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "the weight of his words" | | 1 | "sent a jolt through" | | 2 | "a glimmer of hope" |
| |
| 79.95% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 4 | | narrationSentences | 74 | | matches | | 0 | "felt a chill" | | 1 | "filled with regret" | | 2 | "d with regret" | | 3 | "p with relief" |
| |
| 84.94% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1105 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 809 | | uniqueNames | 10 | | maxNameDensity | 1.73 | | worstName | "Lucien" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Lucien" | | discoveredNames | | Carter | 1 | | Eva | 2 | | Ptolemy | 1 | | Moreau | 1 | | Rory | 12 | | Evan | 2 | | Lucien | 14 | | East | 1 | | London | 1 | | Silas | 1 |
| | persons | | 0 | "Carter" | | 1 | "Eva" | | 2 | "Ptolemy" | | 3 | "Moreau" | | 4 | "Rory" | | 5 | "Evan" | | 6 | "Lucien" | | 7 | "Silas" |
| | places | | | globalScore | 0.635 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | 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 | 1105 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 107 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 24.02 | | std | 14.95 | | cv | 0.622 | | sampleLengths | | 0 | 60 | | 1 | 55 | | 2 | 8 | | 3 | 30 | | 4 | 14 | | 5 | 18 | | 6 | 13 | | 7 | 14 | | 8 | 20 | | 9 | 60 | | 10 | 15 | | 11 | 15 | | 12 | 9 | | 13 | 14 | | 14 | 12 | | 15 | 12 | | 16 | 25 | | 17 | 22 | | 18 | 25 | | 19 | 18 | | 20 | 16 | | 21 | 38 | | 22 | 20 | | 23 | 24 | | 24 | 20 | | 25 | 45 | | 26 | 25 | | 27 | 37 | | 28 | 12 | | 29 | 13 | | 30 | 48 | | 31 | 64 | | 32 | 22 | | 33 | 12 | | 34 | 18 | | 35 | 41 | | 36 | 7 | | 37 | 15 | | 38 | 14 | | 39 | 42 | | 40 | 7 | | 41 | 11 | | 42 | 18 | | 43 | 27 | | 44 | 17 | | 45 | 33 |
| |
| 91.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 74 | | matches | | 0 | "was slicked" | | 1 | "get entangled" | | 2 | "been drawn" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 149 | | matches | | 0 | "was fleeting" | | 1 | "was, standing" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 107 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 810 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.025925925925925925 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007407407407407408 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 10.33 | | std | 4.4 | | cv | 0.426 | | sampleLengths | | 0 | 17 | | 1 | 8 | | 2 | 10 | | 3 | 16 | | 4 | 9 | | 5 | 13 | | 6 | 16 | | 7 | 15 | | 8 | 11 | | 9 | 8 | | 10 | 9 | | 11 | 7 | | 12 | 14 | | 13 | 11 | | 14 | 3 | | 15 | 13 | | 16 | 5 | | 17 | 9 | | 18 | 4 | | 19 | 8 | | 20 | 6 | | 21 | 7 | | 22 | 13 | | 23 | 17 | | 24 | 14 | | 25 | 12 | | 26 | 17 | | 27 | 9 | | 28 | 6 | | 29 | 11 | | 30 | 4 | | 31 | 9 | | 32 | 11 | | 33 | 3 | | 34 | 3 | | 35 | 9 | | 36 | 8 | | 37 | 4 | | 38 | 9 | | 39 | 16 | | 40 | 11 | | 41 | 11 | | 42 | 14 | | 43 | 11 | | 44 | 12 | | 45 | 6 | | 46 | 8 | | 47 | 8 | | 48 | 13 | | 49 | 17 |
| |
| 57.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.35514018691588783 | | totalSentences | 107 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 25.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 74 | | matches | | 0 | "She hesitated, her breath catching" | | 1 | "She glanced down at him," | | 2 | "She turned the final lock" | | 3 | "His platinum blond hair was" | | 4 | "he greeted, his voice smooth" | | 5 | "she replied, her tone cooler" | | 6 | "She stepped aside, allowing him" | | 7 | "He glanced around, taking in" | | 8 | "He turned to face her," | | 9 | "she shot back, unable to" | | 10 | "She had walked away, vowing" | | 11 | "He took a step closer," | | 12 | "she said, her voice softening" | | 13 | "he said, his tone serious" | | 14 | "She felt a chill run" | | 15 | "he said, his voice low" | | 16 | "She looked away, the weight" | | 17 | "he said, stepping closer" | | 18 | "She wanted to believe him," | | 19 | "He reached out, his hand" |
| | ratio | 0.486 | |
| 7.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 74 | | matches | | 0 | "Aurora Carter stood in the" | | 1 | "She hesitated, her breath catching" | | 2 | "The tabby cat, Ptolemy, weaved" | | 3 | "She glanced down at him," | | 4 | "She turned the final lock" | | 5 | "Lucien Moreau stood on the" | | 6 | "The sight of him, impeccably" | | 7 | "His platinum blond hair was" | | 8 | "The air between them crackled" | | 9 | "he greeted, his voice smooth" | | 10 | "she replied, her tone cooler" | | 11 | "She stepped aside, allowing him" | | 12 | "The flat felt even smaller" | | 13 | "He glanced around, taking in" | | 14 | "Rory said, closing the door" | | 15 | "He turned to face her," | | 16 | "Rory crossed her arms, leaning" | | 17 | "Lucien's lips twitched into a" | | 18 | "she shot back, unable to" | | 19 | "The memories of their last" |
| | ratio | 0.905 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 2 | | matches | | 0 | "Yet here he was, standing in front of her, as if the past months had never happened." | | 1 | "There was something about him that made her feel alive, even when everything else seemed to be falling apart." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 10 | | matches | | 0 | "he greeted, his voice smooth and measured" | | 1 | "she replied, her tone cooler than she intended" | | 2 | "she shot back, unable to keep the edge out of her voice" | | 3 | "she said, her voice softening despite herself" | | 4 | "he said, his tone serious" | | 5 | "he said, his voice low" | | 6 | "she said, her voice steady" | | 7 | "he said, his tone earnest" | | 8 | "he said, his voice low" | | 9 | "Lucien said, his tone firm" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 2 | | fancyTags | | 0 | "he whispered (whisper)" | | 1 | "the man grunted (grunt)" |
| | dialogueSentences | 49 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.143 | | effectiveRatio | 0.082 | |