| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 30 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.789 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1089 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1089 | | totalAiIsms | 26 | | found | | 0 | | | 1 | | word | "skipped a beat" | | count | 1 |
| | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "beacon" | | 1 | "skipped a beat" | | 2 | "familiar" | | 3 | "weight" | | 4 | "pang" | | 5 | "intensity" | | 6 | "traced" | | 7 | "shattered" | | 8 | "silence" | | 9 | "furrowed" | | 10 | "wavering" | | 11 | "whisper" | | 12 | "anticipation" | | 13 | "resolve" | | 14 | "tension" | | 15 | "glint" | | 16 | "sense of" | | 17 | "solace" |
| |
| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "weight of words/silence" | | count | 2 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "the weight of her words" | | 2 | "the weight of words" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 55 | | matches | (empty) | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 6 | | narrationSentences | 55 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 64 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1086 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 34.51% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 736 | | uniqueNames | 7 | | maxNameDensity | 2.31 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 17 | | Silas | 10 | | Eve | 9 | | London | 1 | | Cardiff | 1 |
| | persons | | | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 0.345 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | 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 | 1086 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 64 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 38.79 | | std | 21.96 | | cv | 0.566 | | sampleLengths | | 0 | 47 | | 1 | 92 | | 2 | 87 | | 3 | 44 | | 4 | 68 | | 5 | 8 | | 6 | 40 | | 7 | 79 | | 8 | 36 | | 9 | 28 | | 10 | 9 | | 11 | 27 | | 12 | 17 | | 13 | 9 | | 14 | 37 | | 15 | 37 | | 16 | 60 | | 17 | 18 | | 18 | 49 | | 19 | 40 | | 20 | 28 | | 21 | 16 | | 22 | 49 | | 23 | 19 | | 24 | 25 | | 25 | 39 | | 26 | 40 | | 27 | 38 |
| |
| 92.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 55 | | matches | | 0 | "was tied" | | 1 | "got wrapped" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 132 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 560 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.026785714285714284 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.010714285714285714 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 64 | | echoCount | 0 | | echoWords | (empty) | |
| 94.69% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 64 | | mean | 16.97 | | std | 6.56 | | cv | 0.387 | | sampleLengths | | 0 | 13 | | 1 | 22 | | 2 | 12 | | 3 | 14 | | 4 | 10 | | 5 | 10 | | 6 | 15 | | 7 | 24 | | 8 | 19 | | 9 | 23 | | 10 | 23 | | 11 | 26 | | 12 | 15 | | 13 | 11 | | 14 | 16 | | 15 | 17 | | 16 | 21 | | 17 | 9 | | 18 | 9 | | 19 | 12 | | 20 | 17 | | 21 | 8 | | 22 | 8 | | 23 | 18 | | 24 | 14 | | 25 | 16 | | 26 | 5 | | 27 | 24 | | 28 | 26 | | 29 | 8 | | 30 | 14 | | 31 | 22 | | 32 | 5 | | 33 | 23 | | 34 | 9 | | 35 | 27 | | 36 | 17 | | 37 | 9 | | 38 | 19 | | 39 | 18 | | 40 | 16 | | 41 | 21 | | 42 | 28 | | 43 | 14 | | 44 | 18 | | 45 | 18 | | 46 | 11 | | 47 | 28 | | 48 | 10 | | 49 | 18 |
| |
| 94.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.578125 | | totalSentences | 64 | | uniqueOpeners | 37 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 55 | | matches | | 0 | "Suddenly, the room's temperature seemed" |
| | ratio | 0.018 | |
| 96.36% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 55 | | matches | | 0 | "She strode over, engulfing the" | | 1 | "He chuckled, his voice carrying" | | 2 | "Her cascade of golden curls" | | 3 | "She felt the weight of" | | 4 | "she replied, her voice faltering" | | 5 | "It was her moment of" | | 6 | "She trailed off, focusing on" | | 7 | "Her eyes met his, and" | | 8 | "She let out a heavy" | | 9 | "His tone conveyed a fatherly" | | 10 | "She paused, her gaze fixed" | | 11 | "He set down the glass," | | 12 | "He raised his glass in" | | 13 | "she asked, a glimmer of" | | 14 | "She had left a haunting" | | 15 | "She gave Aurora a playful" | | 16 | "He locked eyes with Aurora," |
| | ratio | 0.309 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 55 | | matches | | 0 | "The sign was unmistakable: a" | | 1 | "Aurora's heart skipped a beat" | | 2 | "a deep, gravelly voice filled" | | 3 | "Aurora's eyes widened, a smile" | | 4 | "She strode over, engulfing the" | | 5 | "Aurora's eyes took in his" | | 6 | "He chuckled, his voice carrying" | | 7 | "the other figure greeted her," | | 8 | "Aurora felt a pang as" | | 9 | "Her cascade of golden curls" | | 10 | "Eve responded, leaning back against" | | 11 | "Aurora's fingers traced the scar" | | 12 | "The room seemed to close" | | 13 | "She felt the weight of" | | 14 | "Eve asked, breaking the heavy" | | 15 | "Aurora's brows furrowed, her fingers" | | 16 | "she replied, her voice faltering" | | 17 | "It was her moment of" | | 18 | "Silas urged, his eyes conveying" | | 19 | "Aurora's voice broke as she" |
| | ratio | 0.927 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "Her eyes met his, and she saw understanding and assurance in the spy's gaze, seeing both the promise of protection and threat of vengeance." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 30 | | uselessAdditionCount | 12 | | matches | | 0 | "Aurora's eyes widened, a smile spreading across her features" | | 1 | "He chuckled, his voice carrying the weight of a thousand secrets" | | 2 | "the other figure greeted, a hint of bitterness marring the formal tone" | | 3 | "she replied, her voice faltering" | | 4 | "Silas urged, his eyes conveying understanding" | | 5 | "Silas interrupted, his voice carrying the weight of authority and experience" | | 6 | "Silas asked, his penetrating gaze never wavering" | | 7 | "Silas asked, his voice dropping to a whisper as he leaned in" | | 8 | "she asked, a glimmer of mischief in her eyes" | | 9 | "Silas replied, his hand reaching for a cigar in his pocket" | | 10 | "Eve conceded, a faint smile playing on her lips" | | 11 | "Aurora said, her voice steady and intent" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 20 | | fancyCount | 8 | | fancyTags | | 0 | "He chuckled (chuckle)" | | 1 | "Eve responded (respond)" | | 2 | "Silas urged (urge)" | | 3 | "Silas interrupted (interrupt)" | | 4 | "His tone conveyed (convey)" | | 5 | "Eve hesitated (hesitate)" | | 6 | "Aurora observed (observe)" | | 7 | "Eve conceded (concede)" |
| | dialogueSentences | 38 | | tagDensity | 0.526 | | leniency | 1 | | rawRatio | 0.4 | | effectiveRatio | 0.4 | |