| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 75.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1019 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "completely" | | 1 | "quickly" | | 2 | "very" |
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| 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 | 1019 | | totalAiIsms | 29 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "skipped a beat" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "familiar" | | 1 | "whisper" | | 2 | "intensity" | | 3 | "skipped a beat" | | 4 | "shattered" | | 5 | "eyebrow" | | 6 | "racing" | | 7 | "tension" | | 8 | "flicker" | | 9 | "weight" | | 10 | "pulsed" | | 11 | "unspoken" | | 12 | "silence" | | 13 | "treacherous" | | 14 | "warmth" | | 15 | "sense of" | | 16 | "flickered" | | 17 | "unreadable" | | 18 | "encounter" | | 19 | "trepidation" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 57 | | matches | | |
| 92.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 57 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1017 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 52.79% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 823 | | uniqueNames | 8 | | maxNameDensity | 1.94 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 1 | | Raven | 2 | | Nest | 2 | | Evan | 14 | | Cardiff | 2 | | Aurora | 16 | | London | 1 | | Silas | 5 |
| | persons | | 0 | "Carter" | | 1 | "Raven" | | 2 | "Evan" | | 3 | "Aurora" | | 4 | "Silas" |
| | places | | | globalScore | 0.528 | | windowScore | 0.667 | |
| 11.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like an eternity before he finally" | | 1 | "seemed worn as if the weight of time had finally caught up with him" | | 2 | "quite put her finger on what it was" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1017 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 73 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 30.82 | | std | 18.01 | | cv | 0.584 | | sampleLengths | | 0 | 86 | | 1 | 55 | | 2 | 46 | | 3 | 21 | | 4 | 18 | | 5 | 22 | | 6 | 17 | | 7 | 27 | | 8 | 15 | | 9 | 24 | | 10 | 44 | | 11 | 27 | | 12 | 75 | | 13 | 16 | | 14 | 29 | | 15 | 7 | | 16 | 16 | | 17 | 36 | | 18 | 43 | | 19 | 31 | | 20 | 12 | | 21 | 20 | | 22 | 52 | | 23 | 59 | | 24 | 34 | | 25 | 14 | | 26 | 17 | | 27 | 31 | | 28 | 19 | | 29 | 26 | | 30 | 15 | | 31 | 26 | | 32 | 37 |
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| 92.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 57 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 144 | | matches | | 0 | "was hiding was letting" |
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| 64.58% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 73 | | ratio | 0.027 | | matches | | 0 | "Aurora's heart skipped a beat as she realized who it was – Evan, her ex-boyfriend from Cardiff." | | 1 | "For a moment, Aurora thought she saw a flicker of something – regret, maybe, or remorse – but it was quickly extinguished, replaced by his usual charming smile." |
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| 91.83% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 824 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 35 | | adverbRatio | 0.04247572815533981 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.013349514563106795 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 13.93 | | std | 6.31 | | cv | 0.453 | | sampleLengths | | 0 | 18 | | 1 | 17 | | 2 | 23 | | 3 | 16 | | 4 | 12 | | 5 | 23 | | 6 | 17 | | 7 | 15 | | 8 | 20 | | 9 | 26 | | 10 | 17 | | 11 | 4 | | 12 | 10 | | 13 | 8 | | 14 | 12 | | 15 | 10 | | 16 | 17 | | 17 | 15 | | 18 | 12 | | 19 | 6 | | 20 | 9 | | 21 | 9 | | 22 | 15 | | 23 | 16 | | 24 | 28 | | 25 | 12 | | 26 | 15 | | 27 | 8 | | 28 | 27 | | 29 | 12 | | 30 | 28 | | 31 | 16 | | 32 | 10 | | 33 | 19 | | 34 | 3 | | 35 | 4 | | 36 | 8 | | 37 | 8 | | 38 | 25 | | 39 | 11 | | 40 | 15 | | 41 | 14 | | 42 | 14 | | 43 | 11 | | 44 | 20 | | 45 | 7 | | 46 | 5 | | 47 | 10 | | 48 | 10 | | 49 | 16 |
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| 52.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3424657534246575 | | totalSentences | 73 | | uniqueOpeners | 25 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 57 | | matches | | 0 | "She signaled the bartender, a" | | 1 | "he said, his voice low" | | 2 | "he said, signaling the bartender" | | 3 | "She shrugged, her shoulders barely" | | 4 | "he said, his voice a" | | 5 | "she asked, her voice a" | | 6 | "He leaned in, his voice" | | 7 | "They both knew that their" | | 8 | "It was Silas, the owner" | | 9 | "He limped over to their" | | 10 | "he said, his voice a" | | 11 | "She nodded, her heart still" | | 12 | "he said, his voice a" | | 13 | "he said, his voice low" |
| | ratio | 0.246 | |
| 82.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 57 | | matches | | 0 | "Aurora Carter slid onto a" | | 1 | "She signaled the bartender, a" | | 2 | "The walls, adorned with old" | | 3 | "The air was thick with" | | 4 | "Aurora's heart skipped a beat" | | 5 | "The one she'd fled from," | | 6 | "Evan's gaze held hers for" | | 7 | "The smile was a little" | | 8 | "he said, his voice low" | | 9 | "Aurora raised an eyebrow, her" | | 10 | "he said, signaling the bartender" | | 11 | "The bartender, sensing the tension" | | 12 | "Aurora took a sip of" | | 13 | "Evan's eyes lit up with" | | 14 | "She shrugged, her shoulders barely" | | 15 | "Evan's gaze roved over her" | | 16 | "he said, his voice a" | | 17 | "Aurora raised an eyebrow, unsure" | | 18 | "The truth was, she'd spent" | | 19 | "The sharp jawline was softer," |
| | ratio | 0.754 | |
| 87.72% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 57 | | matches | | 0 | "Just as the bartender handed" |
| | ratio | 0.018 | |
| 89.29% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 3 | | matches | | 0 | "The smile was a little too wide, a little too forced, but it was still the same charming smile that had once disarmed her so completely." | | 1 | "The bartender, sensing the tension between them, wisely kept his distance, busying himself with polishing a mug." | | 2 | "The sharp jawline was softer, the eyes less bright, and the once-chiseled features now seemed worn, as if the weight of time had finally caught up with him." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 6 | | matches | | 0 | "he said, his voice a little too soft" | | 1 | "she asked, her voice a little sharper than she'd intended" | | 2 | "Evan said, his voice low" | | 3 | "he said, his voice a gentle rumble" | | 4 | "he said, his voice a little softer" | | 5 | "he said, his voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |