| 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 | |
| 94.81% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 964 | | totalAiIsmAdverbs | 1 | | 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) | |
| 68.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 964 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "pulsed" | | 1 | "racing" | | 2 | "familiar" | | 3 | "perfect" | | 4 | "vibrated" | | 5 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 85 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 26 | | totalWords | 970 | | ratio | 0.027 | | matches | | 0 | "Third oak past the fallen willow. Touch the gnarled root. Speak your name aloud." | | 1 | "they’re coming for me" | | 2 | "was" | | 3 | "Run" | | 4 | "Get out. Get out. Get out." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 794 | | uniqueNames | 7 | | maxNameDensity | 1.26 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Heartstone | 6 | | Aurora | 10 | | Park | 1 | | Eva | 10 | | Stupid | 1 | | September | 1 | | London | 1 |
| | persons | | 0 | "Heartstone" | | 1 | "Aurora" | | 2 | "Eva" |
| | places | | | globalScore | 0.87 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 1 | | matches | | 0 | "shapes that seemed to shift when she focused on them" |
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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 | 970 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 22.56 | | std | 15.37 | | cv | 0.681 | | sampleLengths | | 0 | 40 | | 1 | 21 | | 2 | 38 | | 3 | 42 | | 4 | 2 | | 5 | 2 | | 6 | 29 | | 7 | 9 | | 8 | 42 | | 9 | 10 | | 10 | 10 | | 11 | 70 | | 12 | 15 | | 13 | 1 | | 14 | 43 | | 15 | 17 | | 16 | 31 | | 17 | 31 | | 18 | 23 | | 19 | 6 | | 20 | 37 | | 21 | 19 | | 22 | 3 | | 23 | 37 | | 24 | 19 | | 25 | 32 | | 26 | 20 | | 27 | 42 | | 28 | 15 | | 29 | 46 | | 30 | 26 | | 31 | 1 | | 32 | 30 | | 33 | 2 | | 34 | 31 | | 35 | 21 | | 36 | 9 | | 37 | 26 | | 38 | 7 | | 39 | 30 | | 40 | 7 | | 41 | 17 | | 42 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 85 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 152 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 100 | | ratio | 0.06 | | matches | | 0 | "Richmond Park at midnight was a different beast entirely — the familiar daytime paths twisted into something ancient and predatory under the sliver of moon." | | 1 | "The wind picked up, carrying a smell that didn’t belong in a London park — woodsmoke and wet earth and something floral, cloying, like funeral arrangements left too long in a closed room." | | 2 | "Its silhouette was wrong — too tall, too thin, shoulders that sloped at angles that broke human anatomy." | | 3 | "It wasn’t even human — it layered over itself, a chorus of whispers speaking in perfect unison." | | 4 | "The eyes had no whites — just darkness, bottomless and hungry." | | 5 | "Aurora looked down at it, and in its deep crimson glow she saw something she hadn’t noticed before — a crack running through its centre, hairline thin but spreading." |
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| 95.03% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 788 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.04568527918781726 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007614213197969543 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 9.7 | | std | 7.41 | | cv | 0.764 | | sampleLengths | | 0 | 15 | | 1 | 25 | | 2 | 7 | | 3 | 6 | | 4 | 4 | | 5 | 4 | | 6 | 1 | | 7 | 5 | | 8 | 30 | | 9 | 2 | | 10 | 7 | | 11 | 2 | | 12 | 7 | | 13 | 14 | | 14 | 12 | | 15 | 2 | | 16 | 2 | | 17 | 2 | | 18 | 3 | | 19 | 10 | | 20 | 7 | | 21 | 7 | | 22 | 7 | | 23 | 2 | | 24 | 33 | | 25 | 9 | | 26 | 4 | | 27 | 6 | | 28 | 10 | | 29 | 10 | | 30 | 7 | | 31 | 14 | | 32 | 17 | | 33 | 22 | | 34 | 15 | | 35 | 1 | | 36 | 4 | | 37 | 18 | | 38 | 21 | | 39 | 7 | | 40 | 4 | | 41 | 2 | | 42 | 2 | | 43 | 2 | | 44 | 7 | | 45 | 24 | | 46 | 6 | | 47 | 17 | | 48 | 8 | | 49 | 17 |
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| 57.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.41 | | totalSentences | 100 | | uniqueOpeners | 41 | |
| 45.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 73 | | matches | | 0 | "Too alive, like walking on" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 73 | | matches | | 0 | "She’d followed Eva’s instructions to" | | 1 | "She knew it was stupid." | | 2 | "Her breath fogged in air" | | 3 | "She spun, but only shadows" | | 4 | "Her voice came out too" | | 5 | "She touched it instinctively." | | 6 | "Its silhouette was wrong —" | | 7 | "It wasn’t even human —" | | 8 | "Its face was Eva’s face." | | 9 | "Its feet didn’t bend like" | | 10 | "Its smile widened impossibly, splitting" | | 11 | "It reached for her, fingers" | | 12 | "She fell into darkness, into" |
| | ratio | 0.178 | |
| 83.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 73 | | matches | | 0 | "The Heartstone pulsed against Aurora’s" | | 1 | "Richmond Park at midnight was" | | 2 | "She’d followed Eva’s instructions to" | | 3 | "She knew it was stupid." | | 4 | "The grass beneath her trainers" | | 5 | "Aurora stopped at the third" | | 6 | "Her breath fogged in air" | | 7 | "The park remained still, holding" | | 8 | "A twig snapped somewhere to" | | 9 | "She spun, but only shadows" | | 10 | "Her voice came out too" | | 11 | "The wind picked up, carrying" | | 12 | "The Heartstone grew warm against" | | 13 | "She touched it instinctively." | | 14 | "The silver chain seared her" | | 15 | "The grove opened around her" | | 16 | "The next, the clearing *was*" | | 17 | "The oak trees rearranged themselves," | | 18 | "Moss covered their surfaces in" | | 19 | "The figure didn't turn." |
| | ratio | 0.753 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 6 | | matches | | 0 | "The Heartstone pulsed against Aurora’s collarbone, a living thrum that matched her own racing heart." | | 1 | "The wind picked up, carrying a smell that didn’t belong in a London park — woodsmoke and wet earth and something floral, cloying, like funeral arrangements left…" | | 2 | "Its silhouette was wrong — too tall, too thin, shoulders that sloped at angles that broke human anatomy." | | 3 | "But the smile stretched too wide, revealing teeth that came to points." | | 4 | "Wildflowers at Aurora’s feet shrivelled and died, turning to black dust that scattered in a wind that came from nowhere." | | 5 | "Something that had been waiting a very long time for this exact moment." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "The thing counted on, each digit bending backward" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.042 | | leniency | 0.083 | | rawRatio | 0 | | effectiveRatio | 0 | |