| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1153 | | 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) | |
| 56.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1153 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "velvet" | | 1 | "silence" | | 2 | "echo" | | 3 | "pulse" | | 4 | "footsteps" | | 5 | "glistening" | | 6 | "rhythmic" | | 7 | "warmth" | | 8 | "racing" | | 9 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1153 | | ratio | 0.002 | | matches | | 0 | "Tick-tock." | | 1 | "Flip-flop." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 1115 | | uniqueNames | 12 | | maxNameDensity | 0.72 | | worstName | "Aurora" | | maxWindowNameDensity | 1 | | worstWindowName | "Aurora" | | discoveredNames | | Cheung | 1 | | Richmond | 2 | | Park | 2 | | London | 2 | | Grove | 1 | | Evan | 1 | | Cardiff | 1 | | Pre-Law | 1 | | Aurora | 8 | | Heartstone | 3 | | Pendant | 1 | | Welsh | 1 |
| | persons | | 0 | "Cheung" | | 1 | "Evan" | | 2 | "Aurora" | | 3 | "Heartstone" | | 4 | "Pendant" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | 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 | 1153 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 22.61 | | std | 16.69 | | cv | 0.738 | | sampleLengths | | 0 | 19 | | 1 | 48 | | 2 | 9 | | 3 | 14 | | 4 | 11 | | 5 | 46 | | 6 | 4 | | 7 | 47 | | 8 | 50 | | 9 | 8 | | 10 | 30 | | 11 | 20 | | 12 | 49 | | 13 | 9 | | 14 | 1 | | 15 | 13 | | 16 | 48 | | 17 | 13 | | 18 | 12 | | 19 | 34 | | 20 | 47 | | 21 | 12 | | 22 | 33 | | 23 | 7 | | 24 | 19 | | 25 | 14 | | 26 | 1 | | 27 | 22 | | 28 | 10 | | 29 | 33 | | 30 | 3 | | 31 | 40 | | 32 | 9 | | 33 | 1 | | 34 | 30 | | 35 | 17 | | 36 | 41 | | 37 | 13 | | 38 | 11 | | 39 | 9 | | 40 | 59 | | 41 | 13 | | 42 | 18 | | 43 | 5 | | 44 | 8 | | 45 | 31 | | 46 | 36 | | 47 | 12 | | 48 | 65 | | 49 | 29 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 98 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 171 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 102 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1129 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 25 | | adverbRatio | 0.022143489813994686 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005314437555358724 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 11.3 | | std | 8.22 | | cv | 0.727 | | sampleLengths | | 0 | 19 | | 1 | 5 | | 2 | 11 | | 3 | 32 | | 4 | 9 | | 5 | 14 | | 6 | 11 | | 7 | 12 | | 8 | 23 | | 9 | 11 | | 10 | 4 | | 11 | 17 | | 12 | 3 | | 13 | 2 | | 14 | 25 | | 15 | 18 | | 16 | 32 | | 17 | 8 | | 18 | 7 | | 19 | 7 | | 20 | 16 | | 21 | 10 | | 22 | 10 | | 23 | 4 | | 24 | 2 | | 25 | 10 | | 26 | 1 | | 27 | 32 | | 28 | 9 | | 29 | 1 | | 30 | 2 | | 31 | 11 | | 32 | 7 | | 33 | 28 | | 34 | 13 | | 35 | 5 | | 36 | 8 | | 37 | 1 | | 38 | 11 | | 39 | 34 | | 40 | 6 | | 41 | 8 | | 42 | 19 | | 43 | 14 | | 44 | 5 | | 45 | 7 | | 46 | 12 | | 47 | 21 | | 48 | 7 | | 49 | 6 |
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| 67.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.45098039215686275 | | totalSentences | 102 | | uniqueOpeners | 46 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 86 | | matches | | 0 | "Just the pale, unnatural bloom" | | 1 | "Instead, she closed the distance" | | 2 | "Just a long, vertical shadow" |
| | ratio | 0.035 | |
| 85.12% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 86 | | matches | | 0 | "She checked the crumpled paper" | | 1 | "It had snapped off, like" | | 2 | "They didn't sway." | | 3 | "Her boots crushed a patch" | | 4 | "She adjusted the bag on" | | 5 | "she told herself" | | 6 | "Her voice died against the" | | 7 | "She wasn't a Pre-Law student" | | 8 | "Her boot hovered an inch" | | 9 | "It sounded wet, like a" | | 10 | "It came from the left," | | 11 | "She whipped her head around." | | 12 | "Her pulse hammered against her" | | 13 | "She didn't reach for it." | | 14 | "She unclipped the red nylon" | | 15 | "Her fingers prised open the" | | 16 | "She pivoted, bringing her torch" | | 17 | "She swung the light left." | | 18 | "She brought the beam back" | | 19 | "She hadn't walked ten feet." |
| | ratio | 0.337 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 86 | | matches | | 0 | "The insulated delivery bag snagged" | | 1 | "Aurora yanked the strap free." | | 2 | "Polystyrene containers shifted inside, sloshing" | | 3 | "Yu-Fei Cheung did not accept" | | 4 | "She checked the crumpled paper" | | 5 | "Aurora muttered the instructions to" | | 6 | "The park had swallowed the" | | 7 | "It had snapped off, like" | | 8 | "Wildflowers carpeted the grass in" | | 9 | "They didn't sway." | | 10 | "Her boots crushed a patch" | | 11 | "She adjusted the bag on" | | 12 | "An old habit from her" | | 13 | "she told herself" | | 14 | "Her voice died against the" | | 15 | "The trees formed a tight," | | 16 | "A rusted biscuit tin sat" | | 17 | "Aurora marched toward it." | | 18 | "She wasn't a Pre-Law student" | | 19 | "Halfway to the stump, a" |
| | ratio | 0.698 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 86 | | matches | | 0 | "Yet, at the very edge" | | 1 | "Now, it radiated a sudden," | | 2 | "If the light showed them" |
| | ratio | 0.035 | |
| 42.86% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 7 | | matches | | 0 | "When she stepped between the towering, twisted oak standing stones that bordered the clearing, the residual hum of the city hadn't just faded." | | 1 | "Her boots crushed a patch of purple petals, releasing a heavy, cloying scent that reminded her of lilies left too long in stagnant vase water." | | 2 | "She wasn't a Pre-Law student anymore, arguing hypotheticals in bright lecture halls, she was a girl who delivered takeaway to rich eccentrics and occult hobbyis…" | | 3 | "The Heartstone seared against her chest, pulsing with a rhythmic, feverish warmth that matched her racing blood." | | 4 | "The words stretched out, syllables sagging in the middle as if the speaker didn't understand where one word ended and the next began, only the shape of the nois…" | | 5 | "Her eyes adjusted, drinking in the faint,sourceless luminescence that clung to the year-round flowers." | | 6 | "The Heartstone burned against her skin, glowing so bright the deep crimson light bled right through the black fabric of her fleece, painting a red target over h…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |