| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1512 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 60.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1512 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "tracing" | | 1 | "scanned" | | 2 | "etched" | | 3 | "pulsed" | | 4 | "shattered" | | 5 | "vibrated" | | 6 | "weight" | | 7 | "determined" | | 8 | "raced" | | 9 | "fractured" | | 10 | "grave" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 125 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 125 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 153 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1512 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1056 | | uniqueNames | 11 | | maxNameDensity | 2.18 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Patel | 13 | | Camden | 1 | | Tube | 1 | | Morris | 4 | | Kowalski | 1 | | Quinn | 23 | | Oxford | 1 | | Eva | 12 | | Vale | 1 | | Veil | 1 | | Compass | 1 |
| | persons | | 0 | "Patel" | | 1 | "Morris" | | 2 | "Kowalski" | | 3 | "Quinn" | | 4 | "Eva" | | 5 | "Vale" | | 6 | "Compass" |
| | places | | | globalScore | 0.411 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | 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 | 1512 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 153 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 30.24 | | std | 18.97 | | cv | 0.627 | | sampleLengths | | 0 | 42 | | 1 | 3 | | 2 | 26 | | 3 | 97 | | 4 | 23 | | 5 | 29 | | 6 | 47 | | 7 | 21 | | 8 | 2 | | 9 | 51 | | 10 | 33 | | 11 | 18 | | 12 | 16 | | 13 | 50 | | 14 | 9 | | 15 | 65 | | 16 | 32 | | 17 | 40 | | 18 | 20 | | 19 | 67 | | 20 | 33 | | 21 | 40 | | 22 | 32 | | 23 | 61 | | 24 | 14 | | 25 | 50 | | 26 | 36 | | 27 | 28 | | 28 | 17 | | 29 | 9 | | 30 | 50 | | 31 | 26 | | 32 | 15 | | 33 | 26 | | 34 | 43 | | 35 | 29 | | 36 | 37 | | 37 | 7 | | 38 | 42 | | 39 | 16 | | 40 | 47 | | 41 | 15 | | 42 | 26 | | 43 | 4 | | 44 | 29 | | 45 | 43 | | 46 | 18 | | 47 | 14 | | 48 | 3 | | 49 | 11 |
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| 99.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 125 | | matches | | 0 | "been mortared" | | 1 | "being sucked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 192 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 153 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1057 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.0293282876064333 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008514664143803218 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 153 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 153 | | mean | 9.88 | | std | 6.47 | | cv | 0.654 | | sampleLengths | | 0 | 18 | | 1 | 6 | | 2 | 18 | | 3 | 3 | | 4 | 8 | | 5 | 18 | | 6 | 8 | | 7 | 13 | | 8 | 18 | | 9 | 17 | | 10 | 24 | | 11 | 17 | | 12 | 11 | | 13 | 6 | | 14 | 2 | | 15 | 4 | | 16 | 4 | | 17 | 25 | | 18 | 10 | | 19 | 22 | | 20 | 15 | | 21 | 12 | | 22 | 7 | | 23 | 2 | | 24 | 2 | | 25 | 9 | | 26 | 16 | | 27 | 9 | | 28 | 17 | | 29 | 12 | | 30 | 21 | | 31 | 6 | | 32 | 5 | | 33 | 7 | | 34 | 2 | | 35 | 14 | | 36 | 3 | | 37 | 16 | | 38 | 4 | | 39 | 5 | | 40 | 14 | | 41 | 8 | | 42 | 3 | | 43 | 6 | | 44 | 14 | | 45 | 5 | | 46 | 9 | | 47 | 9 | | 48 | 28 | | 49 | 8 |
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| 69.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.43137254901960786 | | totalSentences | 153 | | uniqueOpeners | 66 | |
| 28.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 117 | | matches | | 0 | "Instead she drew a quick" |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 117 | | matches | | 0 | "She checked the worn leather" | | 1 | "She scanned the scattered bone" | | 2 | "Her brown eyes narrowed." | | 3 | "She tucked a strand behind" | | 4 | "Her freckled face betrayed nothing" | | 5 | "She gestured at the corpse." | | 6 | "She crouched once more and" | | 7 | "Its creator had been clever." | | 8 | "She had seen that word" | | 9 | "Her green eyes tracked the" | | 10 | "She ran her free hand" | | 11 | "She adjusted her glasses, studying" | | 12 | "She liked how it fit" | | 13 | "Its weight tugged at her" | | 14 | "She pried at a loose" | | 15 | "It came free easily." | | 16 | "he muttered, but his voice" | | 17 | "It flared once but held." | | 18 | "Its sigils seared her palm." | | 19 | "She would not waste his" |
| | ratio | 0.214 | |
| 11.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 105 | | totalSentences | 117 | | matches | | 0 | "Quinn knelt over the body," | | 1 | "The air down here carried" | | 2 | "DS Patel hovered behind her," | | 3 | "Quinn rose, military straight, her" | | 4 | "She checked the worn leather" | | 5 | "This abandoned stretch of Camden" | | 6 | "Stalls lined the curved platform," | | 7 | "A black market, the kind" | | 8 | "She scanned the scattered bone" | | 9 | "Each carved with the same" | | 10 | "Her brown eyes narrowed." | | 11 | "Patel flipped a page." | | 12 | "Quinn stepped over a toppled" | | 13 | "The sigils etched into the" | | 14 | "A soft scrape sounded from" | | 15 | "Quinn drew her baton, not" | | 16 | "Eva Kowalski emerged, curly red" | | 17 | "She tucked a strand behind" | | 18 | "The worn leather satchel she" | | 19 | "Her freckled face betrayed nothing" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 117 | | matches | (empty) | | ratio | 0 | |
| 48.87% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 5 | | matches | | 0 | "Quinn knelt over the body, her fingers tracing the edges of the burn that sealed the stab wound." | | 1 | "Stalls lined the curved platform, tarps half thrown over jars of glowing resin and bundles of dried roots that should not twitch when untouched." | | 2 | "Patel followed at a reluctant distance, boots crunching on shattered glass from a broken vial that smelled of liquorice and burnt sugar." | | 3 | "Tiles on the walls bore faint luminescent veins that brightened as the compass approached." | | 4 | "Through the fractured wall she saw movement, long limbs unfolding in a space that should not exist." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "he muttered, but his voice lacked conviction" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 40 | | tagDensity | 0.025 | | leniency | 0.05 | | rawRatio | 1 | | effectiveRatio | 0.05 | |