| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1470 | | 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) | |
| 55.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1470 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "measured" | | 1 | "vibrated" | | 2 | "cataloged" | | 3 | "etched" | | 4 | "weight" | | 5 | "shattered" | | 6 | "tracing" | | 7 | "aftermath" | | 8 | "traced" | | 9 | "standard" | | 10 | "familiar" |
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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 | 159 | | matches | (empty) | |
| 97.93% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 159 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 159 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1470 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 8 | | matches | | 0 | "It isn t pooling upward, Eva said, adjusting her glasses." | | 1 | "Veil Compass, Eva corrected softly." | | 2 | "The Veil Market moves every full moon, Eva said, watching Quinn s hands." | | 3 | "They didn t come here by accident, Quinn said." | | 4 | "Because the rift didn t stay open, Eva said." | | 5 | "Call it in as an industrial accident, Quinn said, her voice flat." | | 6 | "I m protecting the perimeter, Quinn corrected." | | 7 | "You re an idiot, Eva said, but she was already stepping into the dark, satchel bumping against her hip." |
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| 78.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1470 | | uniqueNames | 13 | | maxNameDensity | 1.43 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | Harlow | 3 | | Quinn | 21 | | Met | 2 | | Morris | 4 | | Kowalski | 1 | | Eva | 13 | | British | 2 | | Museum | 2 | | Compass | 1 | | Veil | 2 | | Market | 2 | | You | 7 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Met" | | 4 | "Morris" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Market" | | 8 | "You" |
| | places | | | globalScore | 0.786 | | windowScore | 0.833 | |
| 78.57% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 105 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like braided copper wire" | | 1 | "sigils that seemed to shift when the fluorescent work lights caught them at an angle" | | 2 | "smelled like the bottom of a well" |
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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 | 1470 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 159 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 86.47 | | std | 49.28 | | cv | 0.57 | | sampleLengths | | 0 | 118 | | 1 | 134 | | 2 | 96 | | 3 | 7 | | 4 | 102 | | 5 | 164 | | 6 | 140 | | 7 | 135 | | 8 | 77 | | 9 | 143 | | 10 | 21 | | 11 | 9 | | 12 | 72 | | 13 | 48 | | 14 | 81 | | 15 | 19 | | 16 | 104 |
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| 92.02% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 159 | | matches | | 0 | "was pinned" | | 1 | "was etched" | | 2 | "was curled" | | 3 | "been forged" | | 4 | "were brought" | | 5 | "was built" |
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| 93.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 250 | | matches | | 0 | "were wearing" | | 1 | "was tracing" | | 2 | "were using" | | 3 | "was already stepping" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 159 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1472 | | adjectiveStacks | 1 | | stackExamples | | 0 | "fresh gray industrial paint" |
| | adverbCount | 36 | | adverbRatio | 0.024456521739130436 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005434782608695652 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 159 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 159 | | mean | 9.25 | | std | 6.05 | | cv | 0.654 | | sampleLengths | | 0 | 21 | | 1 | 17 | | 2 | 17 | | 3 | 8 | | 4 | 13 | | 5 | 5 | | 6 | 15 | | 7 | 5 | | 8 | 6 | | 9 | 11 | | 10 | 14 | | 11 | 33 | | 12 | 12 | | 13 | 4 | | 14 | 7 | | 15 | 13 | | 16 | 7 | | 17 | 6 | | 18 | 20 | | 19 | 12 | | 20 | 6 | | 21 | 12 | | 22 | 4 | | 23 | 4 | | 24 | 20 | | 25 | 20 | | 26 | 17 | | 27 | 10 | | 28 | 3 | | 29 | 6 | | 30 | 5 | | 31 | 2 | | 32 | 2 | | 33 | 11 | | 34 | 20 | | 35 | 9 | | 36 | 2 | | 37 | 3 | | 38 | 7 | | 39 | 17 | | 40 | 12 | | 41 | 19 | | 42 | 11 | | 43 | 8 | | 44 | 22 | | 45 | 4 | | 46 | 6 | | 47 | 3 | | 48 | 1 | | 49 | 2 |
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| 38.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3018867924528302 | | totalSentences | 159 | | uniqueOpeners | 48 | |
| 44.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 151 | | matches | | 0 | "Just a low, metallic hum" | | 1 | "Preferably from a creature that" |
| | ratio | 0.013 | |
| 69.01% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 57 | | totalSentences | 151 | | matches | | 0 | "She kept her shoulders squared," | | 1 | "She crouched near the center," | | 2 | "It wasn t soot." | | 3 | "It smelled of ozone and" | | 4 | "Her partner, Morris, had died" | | 5 | "She kept her face neutral," | | 6 | "She had spent a thousand" | | 7 | "You re staring at the" | | 8 | "She knew the cadence." | | 9 | "She tucked a loose curl" | | 10 | "It isn t pooling upward," | | 11 | "It s clinging." | | 12 | "She opened her satchel, the" | | 13 | "You called me because the" | | 14 | "You called me because you" | | 15 | "You suspect the clique is" | | 16 | "It wasn t British Museum" | | 17 | "It was older." | | 18 | "She turned it." | | 19 | "It jittered, then locked onto" |
| | ratio | 0.377 | |
| 92.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 111 | | totalSentences | 151 | | matches | | 0 | "Rain had seeped through the" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "She kept her shoulders squared," | | 3 | "The scene inside was wrong" | | 4 | "This was sterile, almost ceremonial." | | 5 | "She crouched near the center," | | 6 | "The lead victim s chest" | | 7 | "Quinn s gloved fingers hovered" | | 8 | "It wasn t soot." | | 9 | "It smelled of ozone and" | | 10 | "Her partner, Morris, had died" | | 11 | "The coroner had called it" | | 12 | "Quinn had called it a" | | 13 | "She kept her face neutral," | | 14 | "She had spent a thousand" | | 15 | "You re staring at the" | | 16 | "Quinn didn t turn." | | 17 | "She knew the cadence." | | 18 | "Eva Kowalski stepped into her" | | 19 | "Eva s curly red hair" |
| | ratio | 0.735 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 151 | | matches | | 0 | "Now the ghosts were wearing" | | 1 | "As if it had been" | | 2 | "If it points to the" | | 3 | "Because the rift didn t" | | 4 | "To mask a transaction gone" | | 5 | "If the Met knows a" |
| | ratio | 0.04 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 1 | | matches | | 0 | "The face was etched with tight, interlocking sigils that seemed to shift when the fluorescent work lights caught them at an angle." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |