| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva stepped aside [aside]" |
| | dialogueSentences | 49 | | tagDensity | 0.306 | | leniency | 0.612 | | rawRatio | 0.067 | | effectiveRatio | 0.041 | |
| 90.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1574 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "nervously" | | 2 | "very" |
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| 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) | |
| 11.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1574 | | totalAiIsms | 28 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "fluttered" | | 1 | "familiar" | | 2 | "stark" | | 3 | "gloom" | | 4 | "glint" | | 5 | "echoed" | | 6 | "rhythmic" | | 7 | "crystal" | | 8 | "whisper" | | 9 | "chill" | | 10 | "shattered" | | 11 | "velvet" | | 12 | "furrowing" | | 13 | "chaotic" | | 14 | "scanned" | | 15 | "etched" | | 16 | "glistening" | | 17 | "magnetic" | | 18 | "mechanical" |
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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 | 111 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 111 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 145 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 13 | | totalWords | 1566 | | ratio | 0.008 | | matches | | 0 | "Officer down," | | 1 | "Or something like it." | | 2 | "away" | | 3 | "Liquid Polymer-Silicate Compound. Industrial Use Only." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 22.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1099 | | uniqueNames | 12 | | maxNameDensity | 2.55 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 28 | | Quinn | 1 | | Camden | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 | | Kowalski | 1 | | Eva | 18 | | Silas | 1 | | Polymer-Silicate | 1 | | Compound | 1 | | Use | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Silas" |
| | places | (empty) | | globalScore | 0.226 | | windowScore | 0.333 | |
| 93.82% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 2 | | matches | | 0 | "something like it.* But Morris was three yea" | | 1 | "looked like a compass, but the face was e" |
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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 | 1566 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 24.86 | | std | 14.92 | | cv | 0.6 | | sampleLengths | | 0 | 60 | | 1 | 52 | | 2 | 45 | | 3 | 32 | | 4 | 20 | | 5 | 2 | | 6 | 24 | | 7 | 40 | | 8 | 9 | | 9 | 23 | | 10 | 15 | | 11 | 48 | | 12 | 11 | | 13 | 16 | | 14 | 58 | | 15 | 9 | | 16 | 40 | | 17 | 17 | | 18 | 37 | | 19 | 36 | | 20 | 8 | | 21 | 5 | | 22 | 37 | | 23 | 28 | | 24 | 17 | | 25 | 20 | | 26 | 23 | | 27 | 26 | | 28 | 35 | | 29 | 15 | | 30 | 32 | | 31 | 33 | | 32 | 50 | | 33 | 5 | | 34 | 24 | | 35 | 27 | | 36 | 28 | | 37 | 9 | | 38 | 13 | | 39 | 4 | | 40 | 36 | | 41 | 42 | | 42 | 7 | | 43 | 39 | | 44 | 6 | | 45 | 11 | | 46 | 33 | | 47 | 19 | | 48 | 41 | | 49 | 30 |
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| 98.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 111 | | matches | | 0 | "was etched" | | 1 | "was pinned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 192 | | matches | | 0 | "wasn't floating" | | 1 | "was reading" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 145 | | ratio | 0.014 | | matches | | 0 | "The needle inside wasn't floating; it was pinned hard to the south-east, vibrating slightly." | | 1 | "\"He was sprayed. High-velocity coating. Flash-hardens on contact with organic fibers. It's not quartz; it's a resin shell. He suffocated inside it.\"" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1104 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.019021739130434784 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007246376811594203 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 145 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 145 | | mean | 10.8 | | std | 6.75 | | cv | 0.625 | | sampleLengths | | 0 | 16 | | 1 | 21 | | 2 | 11 | | 3 | 12 | | 4 | 16 | | 5 | 2 | | 6 | 15 | | 7 | 4 | | 8 | 15 | | 9 | 12 | | 10 | 19 | | 11 | 14 | | 12 | 13 | | 13 | 16 | | 14 | 3 | | 15 | 8 | | 16 | 12 | | 17 | 2 | | 18 | 10 | | 19 | 14 | | 20 | 24 | | 21 | 16 | | 22 | 9 | | 23 | 8 | | 24 | 15 | | 25 | 15 | | 26 | 7 | | 27 | 12 | | 28 | 29 | | 29 | 6 | | 30 | 5 | | 31 | 16 | | 32 | 8 | | 33 | 8 | | 34 | 5 | | 35 | 9 | | 36 | 10 | | 37 | 18 | | 38 | 8 | | 39 | 1 | | 40 | 16 | | 41 | 7 | | 42 | 17 | | 43 | 3 | | 44 | 12 | | 45 | 2 | | 46 | 25 | | 47 | 12 | | 48 | 24 | | 49 | 4 |
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| 68.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.42758620689655175 | | totalSentences | 145 | | uniqueOpeners | 62 | |
| 32.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 104 | | matches | | 0 | "Only his head remained flesh," |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 104 | | matches | | 0 | "It hummed, a low vibration" | | 1 | "She descended the spiraling concrete" | | 2 | "Her flashlight cut a stark" | | 3 | "She wore a heavy coat" | | 4 | "She marched across the cracked" | | 5 | "She pointed a thumb over" | | 6 | "He looked peaceful, hands folded" | | 7 | "His clothes were trapped inside" | | 8 | "She pulled a notepad from" | | 9 | "She looked from the crate" | | 10 | "She scanned the shelves." | | 11 | "It looked like a compass," | | 12 | "It stayed locked in its" | | 13 | "She walked five paces to" | | 14 | "It pointed at the wall" | | 15 | "She approached the tiled wall" | | 16 | "She ran her hand over" | | 17 | "It sounded hollow." | | 18 | "She jammed the blade into" | | 19 | "She reached into the cavity" |
| | ratio | 0.26 | |
| 41.73% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 104 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "It hummed, a low vibration" | | 2 | "Harlow adjusted the worn leather" | | 3 | "The call had come in" | | 4 | "*Officer down,* they said." | | 5 | "She descended the spiraling concrete" | | 6 | "Her flashlight cut a stark" | | 7 | "Jars floating with unrecognizable organs" | | 8 | "The Veil Market." | | 9 | "Harlow kept her hand near" | | 10 | "The place was empty of" | | 11 | "The voice echoed from the" | | 12 | "Harlow swung the light, pinpointing" | | 13 | "Eva Kowalski shielded her eyes" | | 14 | "She wore a heavy coat" | | 15 | "Harlow lowered the beam but" | | 16 | "She marched across the cracked" | | 17 | "Eva stood, brushing dust from" | | 18 | "She pointed a thumb over" | | 19 | "Harlow stopped three feet from" |
| | ratio | 0.837 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 104 | | matches | | 0 | "*Or something like it.* But" | | 1 | "To the untrained eye, it" | | 2 | "To Harlow, it was a" |
| | ratio | 0.029 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 3 | | matches | | 0 | "Jars floating with unrecognizable organs sat next to dried herbs that smelled of rot and nutmeg." | | 1 | "The crystal surface was cold, radiating a chill that misted her breath." | | 2 | "Harlow turned her back on the glittering corpse and started walking toward the exit, the yellow beam of her flashlight cutting a path through the false magic, s…" |
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| 58.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva said, her voice dropping to a whisper" | | 1 | "Harlow said, her voice flat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 49 | | tagDensity | 0.082 | | leniency | 0.163 | | rawRatio | 0.25 | | effectiveRatio | 0.041 | |