| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1116 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 41.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1116 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "echoing" | | 1 | "pulsed" | | 2 | "stark" | | 3 | "tracing" | | 4 | "flicked" | | 5 | "glint" | | 6 | "etched" | | 7 | "scanned" | | 8 | "shimmered" | | 9 | "traced" | | 10 | "jaw clenched" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 92 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 92 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1110 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 608 | | uniqueNames | 6 | | maxNameDensity | 3.45 | | worstName | "Eva" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 18 | | Kowalski | 1 | | Soot | 1 | | Eva | 21 | | Chemical | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Soot" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.901 | | wordCount | 1110 | | matches | | 0 | "not through space but through lockboxes" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 125 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 27.75 | | std | 15.38 | | cv | 0.554 | | sampleLengths | | 0 | 57 | | 1 | 28 | | 2 | 27 | | 3 | 42 | | 4 | 25 | | 5 | 13 | | 6 | 32 | | 7 | 22 | | 8 | 17 | | 9 | 15 | | 10 | 13 | | 11 | 44 | | 12 | 8 | | 13 | 24 | | 14 | 26 | | 15 | 39 | | 16 | 23 | | 17 | 18 | | 18 | 37 | | 19 | 14 | | 20 | 37 | | 21 | 19 | | 22 | 58 | | 23 | 14 | | 24 | 39 | | 25 | 21 | | 26 | 31 | | 27 | 12 | | 28 | 40 | | 29 | 5 | | 30 | 37 | | 31 | 11 | | 32 | 55 | | 33 | 7 | | 34 | 69 | | 35 | 19 | | 36 | 39 | | 37 | 21 | | 38 | 9 | | 39 | 43 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 117 | | matches | (empty) | |
| 97.14% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 125 | | ratio | 0.016 | | matches | | 0 | "A smear of dark red ran along the edge of the platform—fresh blood, stark against the grime." | | 1 | "“No. But there’s something I did find. Look.” She lifted a speckled circle on the beam—a scorch, not from fire." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 610 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.019672131147540985 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004918032786885246 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 8.88 | | std | 6.38 | | cv | 0.719 | | sampleLengths | | 0 | 15 | | 1 | 13 | | 2 | 17 | | 3 | 12 | | 4 | 15 | | 5 | 7 | | 6 | 6 | | 7 | 18 | | 8 | 9 | | 9 | 16 | | 10 | 6 | | 11 | 10 | | 12 | 10 | | 13 | 7 | | 14 | 5 | | 15 | 13 | | 16 | 3 | | 17 | 9 | | 18 | 1 | | 19 | 10 | | 20 | 2 | | 21 | 10 | | 22 | 10 | | 23 | 8 | | 24 | 8 | | 25 | 2 | | 26 | 4 | | 27 | 4 | | 28 | 13 | | 29 | 13 | | 30 | 2 | | 31 | 3 | | 32 | 10 | | 33 | 4 | | 34 | 2 | | 35 | 9 | | 36 | 4 | | 37 | 7 | | 38 | 6 | | 39 | 12 | | 40 | 8 | | 41 | 8 | | 42 | 11 | | 43 | 5 | | 44 | 3 | | 45 | 14 | | 46 | 9 | | 47 | 13 | | 48 | 24 | | 49 | 2 |
| |
| 65.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.408 | | totalSentences | 125 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 99.52% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 83 | | matches | | 0 | "She crouched, tracing a jagged" | | 1 | "She slid a fresh glove" | | 2 | "She tapped a tablet" | | 3 | "She fished it out." | | 4 | "She flicked a smudge of" | | 5 | "She tapped the glass" | | 6 | "She crouched, inspecting the gutter" | | 7 | "She picked up a small" | | 8 | "Her jacket brushed the barrier’s" | | 9 | "She pressed her palm against" | | 10 | "She scanned the charcoal sigils" | | 11 | "She picked at the grout" | | 12 | "Her watch glowed faintly in" | | 13 | "She flicked on the torch." | | 14 | "She tucked the compass into" | | 15 | "It fell away, revealing a" | | 16 | "She bent, slipped her shoulder" | | 17 | "She set her flashlight on" | | 18 | "She lifted a speckled circle" | | 19 | "She traced the outline with" |
| | ratio | 0.301 | |
| 2.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 83 | | matches | | 0 | "Detective Harlow Quinn swung through" | | 1 | "A smear of dark red" | | 2 | "She crouched, tracing a jagged" | | 3 | "A woman knelt nearby, head" | | 4 | "Eva Kowalski looked up, lips" | | 5 | "Eva said, pointing to a" | | 6 | "Quinn’s gaze flicked from the" | | 7 | "She slid a fresh glove" | | 8 | "Eva pushed hair behind her" | | 9 | "Freckles danced across her cheeks." | | 10 | "Quinn crouched again." | | 11 | "The blood pooled like ink," | | 12 | "Eva pressed a fingertip to" | | 13 | "She tapped a tablet" | | 14 | "The detective swept her flashlight" | | 15 | "Eva checked her watch." | | 16 | "Quinn studied the dark mouth" | | 17 | "Eva’s eyes narrowed." | | 18 | "Quinn’s shoulder tapped metal." | | 19 | "A brass glint lay among" |
| | ratio | 0.916 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 8 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "Eva pressed (press)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 55 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0.4 | | effectiveRatio | 0.073 | |