| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 923 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 923 | | totalAiIsms | 32 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | word | "moth to a flame" | | count | 1 |
| | 20 | | | 21 | | | 22 | | | 23 | |
| | highlights | | 0 | "chill" | | 1 | "familiar" | | 2 | "navigated" | | 3 | "stomach" | | 4 | "oppressive" | | 5 | "silence" | | 6 | "weight" | | 7 | "rhythmic" | | 8 | "pulse" | | 9 | "scanned" | | 10 | "loomed" | | 11 | "maw" | | 12 | "warmth" | | 13 | "pulsed" | | 14 | "profound" | | 15 | "flicker" | | 16 | "beacon" | | 17 | "vibrated" | | 18 | "fleeting" | | 19 | "moth to a flame" | | 20 | "throbbed" | | 21 | "could feel" | | 22 | "tangible" | | 23 | "cacophony" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 85 | | matches | (empty) | |
| 92.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 85 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | 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 | 921 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 918 | | uniqueNames | 8 | | maxNameDensity | 0.54 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "Evan" | | discoveredNames | | London | 1 | | Richmond | 1 | | Park | 1 | | Eva | 1 | | Heartstone | 1 | | Pendant | 1 | | Evan | 2 | | Rory | 5 |
| | persons | | 0 | "Eva" | | 1 | "Pendant" | | 2 | "Evan" | | 3 | "Rory" |
| | places | | 0 | "London" | | 1 | "Richmond" | | 2 | "Park" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 4 | | matches | | 0 | "felt like teeth in a gaping maw" | | 1 | "sound that seemed to carry a thousand whispers" | | 2 | "felt like being watched, not by one pai" | | 3 | "felt like the world was vibrating aroun" |
| |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 2.172 | | wordCount | 921 | | matches | | 0 | "not by one pair of eyes, but by many, hidden" | | 1 | "not in her ears, but deep within her chest" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 74.38% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 57.56 | | std | 23.62 | | cv | 0.41 | | sampleLengths | | 0 | 86 | | 1 | 74 | | 2 | 85 | | 3 | 71 | | 4 | 61 | | 5 | 82 | | 6 | 50 | | 7 | 41 | | 8 | 2 | | 9 | 72 | | 10 | 47 | | 11 | 11 | | 12 | 57 | | 13 | 48 | | 14 | 59 | | 15 | 75 |
| |
| 97.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 85 | | matches | | 0 | "was hidden" | | 1 | "was closed" |
| |
| 97.44% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 130 | | matches | | 0 | "was deafening" | | 1 | "was vibrating" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 85 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 920 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.03152173913043478 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007608695652173913 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 10.84 | | std | 6.21 | | cv | 0.573 | | sampleLengths | | 0 | 19 | | 1 | 29 | | 2 | 15 | | 3 | 23 | | 4 | 21 | | 5 | 14 | | 6 | 10 | | 7 | 13 | | 8 | 16 | | 9 | 4 | | 10 | 7 | | 11 | 15 | | 12 | 10 | | 13 | 1 | | 14 | 11 | | 15 | 14 | | 16 | 14 | | 17 | 9 | | 18 | 15 | | 19 | 20 | | 20 | 15 | | 21 | 21 | | 22 | 8 | | 23 | 7 | | 24 | 5 | | 25 | 7 | | 26 | 5 | | 27 | 11 | | 28 | 18 | | 29 | 10 | | 30 | 11 | | 31 | 5 | | 32 | 17 | | 33 | 12 | | 34 | 14 | | 35 | 9 | | 36 | 4 | | 37 | 10 | | 38 | 5 | | 39 | 1 | | 40 | 4 | | 41 | 13 | | 42 | 17 | | 43 | 11 | | 44 | 4 | | 45 | 13 | | 46 | 13 | | 47 | 2 | | 48 | 4 | | 49 | 23 |
| |
| 40.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3058823529411765 | | totalSentences | 85 | | uniqueOpeners | 26 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 79 | | matches | | 0 | "Just trees, their branches like" | | 1 | "Just the sighing of the" | | 2 | "Just shadows playing tricks." |
| | ratio | 0.038 | |
| 73.16% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 79 | | matches | | 0 | "Her heart hammered a tight" | | 1 | "She’d come here for Eva," | | 2 | "She killed the engine." | | 3 | "She stood, scooter’s weight grounding" | | 4 | "She’d found them before, in" | | 5 | "She dismounted, pushing the scooter" | | 6 | "It had been glowing faintly" | | 7 | "She froze, straining her ears." | | 8 | "She waited, every nerve ending" | | 9 | "She moved again, her boots" | | 10 | "It wasn’t a natural chill." | | 11 | "It was a deep, profound" | | 12 | "She whipped her head around." | | 13 | "It felt like being watched," | | 14 | "She tightened her grip on" | | 15 | "Her breathing grew shallow." | | 16 | "she called out, her voice" | | 17 | "She took another step." | | 18 | "Their bark seemed to twist" | | 19 | "she demanded, her voice shaking" |
| | ratio | 0.367 | |
| 35.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 79 | | matches | | 0 | "Rory clutched the handlebars of" | | 1 | "The familiar rumble of London" | | 2 | "Her heart hammered a tight" | | 3 | "She’d come here for Eva," | | 4 | "The scooter’s headlight beam cut" | | 5 | "The air grew heavy, thick" | | 6 | "something ancient and earthy, like" | | 7 | "Rory’s breath plumed in front" | | 8 | "She killed the engine." | | 9 | "The sudden absence of sound" | | 10 | "The only noise now was" | | 11 | "She stood, scooter’s weight grounding" | | 12 | "The ancient oak standing stones," | | 13 | "She’d found them before, in" | | 14 | "She dismounted, pushing the scooter" | | 15 | "The faint warmth of the" | | 16 | "It had been glowing faintly" | | 17 | "Hel portal, the little voice" | | 18 | "Rory took a tentative step" | | 19 | "A twig snapped somewhere to" |
| | ratio | 0.848 | |
| 63.29% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 79 | | matches | | 0 | "Now, they felt like teeth" |
| | ratio | 0.013 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 4 | | matches | | 0 | "Flowers, her mind supplied, wildflowers that bloomed year-round here." | | 1 | "The ground beneath her feet felt softer, more yielding, as if she walked on damp moss, though her boots still made rustling sounds." | | 2 | "Their bark seemed to twist and writhe, their branches interlocked overhead, forming a canopy that swallowed the faint starlight." | | 3 | "In the centre of the clearing stood a single, gnarled oak, its branches reaching upwards like supplicating arms." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 2 | | matches | | 0 | "she called out, her voice a thin thread in the vast silence" | | 1 | "she demanded, her voice shaking despite her best efforts" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she called out (call out)" | | 1 | "she demanded (demand)" |
| | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |