| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.472 | | leniency | 0.944 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1133 | | 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) | |
| 60.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1133 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "maw" | | 1 | "chill" | | 2 | "macabre" | | 3 | "etched" | | 4 | "magnetic" | | 5 | "long shadow" | | 6 | "pristine" | | 7 | "depths" | | 8 | "flickered" |
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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 | 76 | | matches | (empty) | |
| 48.87% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1124 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 63.79% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 812 | | uniqueNames | 13 | | maxNameDensity | 1.72 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 14 | | Tube | 1 | | Victorian | 1 | | Barnes | 9 | | Veil | 2 | | Market | 1 | | Morris | 1 | | Compass | 1 | | Italian | 1 | | Kowalski | 1 | | Oxford | 1 | | Eva | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Barnes" | | 3 | "Morris" | | 4 | "Compass" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | | | globalScore | 0.638 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "sigils that seemed to shift under the torchlight" |
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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 | 1124 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 32.11 | | std | 21.82 | | cv | 0.679 | | sampleLengths | | 0 | 6 | | 1 | 79 | | 2 | 34 | | 3 | 93 | | 4 | 6 | | 5 | 31 | | 6 | 42 | | 7 | 33 | | 8 | 70 | | 9 | 46 | | 10 | 40 | | 11 | 47 | | 12 | 8 | | 13 | 34 | | 14 | 13 | | 15 | 29 | | 16 | 35 | | 17 | 58 | | 18 | 5 | | 19 | 1 | | 20 | 2 | | 21 | 28 | | 22 | 22 | | 23 | 22 | | 24 | 32 | | 25 | 5 | | 26 | 49 | | 27 | 7 | | 28 | 46 | | 29 | 42 | | 30 | 28 | | 31 | 11 | | 32 | 41 | | 33 | 52 | | 34 | 27 |
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| 96.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 76 | | matches | | 0 | "been disturbed" | | 1 | "been decommissioned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 127 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 95 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 820 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.024390243902439025 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.01097560975609756 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 11.83 | | std | 8.22 | | cv | 0.695 | | sampleLengths | | 0 | 6 | | 1 | 22 | | 2 | 29 | | 3 | 16 | | 4 | 12 | | 5 | 5 | | 6 | 18 | | 7 | 11 | | 8 | 4 | | 9 | 9 | | 10 | 1 | | 11 | 11 | | 12 | 27 | | 13 | 41 | | 14 | 6 | | 15 | 12 | | 16 | 19 | | 17 | 12 | | 18 | 14 | | 19 | 16 | | 20 | 2 | | 21 | 14 | | 22 | 17 | | 23 | 14 | | 24 | 18 | | 25 | 6 | | 26 | 24 | | 27 | 8 | | 28 | 18 | | 29 | 22 | | 30 | 6 | | 31 | 10 | | 32 | 14 | | 33 | 12 | | 34 | 4 | | 35 | 20 | | 36 | 7 | | 37 | 20 | | 38 | 3 | | 39 | 5 | | 40 | 5 | | 41 | 7 | | 42 | 8 | | 43 | 14 | | 44 | 13 | | 45 | 6 | | 46 | 16 | | 47 | 7 | | 48 | 7 | | 49 | 28 |
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| 70.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4631578947368421 | | totalSentences | 95 | | uniqueOpeners | 44 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 63 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 63 | | matches | | 0 | "He stood two metres back," | | 1 | "She touched the tile directly" | | 2 | "He was twenty years her" | | 3 | "She'd seen one similar in" | | 4 | "His gaze fixed on the" | | 5 | "She patted the victim's coat" | | 6 | "Her fingers closed around a" | | 7 | "She drew it out." | | 8 | "It pointed directly at the" | | 9 | "She hadn't spoken his name" | | 10 | "She stood, her sharp jaw" | | 11 | "She followed the blood's impossible" | | 12 | "It led to a service" | | 13 | "He'd stepped back from the" | | 14 | "She gripped the compass tighter," | | 15 | "Her voice carried the precise" | | 16 | "She stared at the blood" |
| | ratio | 0.27 | |
| 39.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 63 | | matches | | 0 | "The blood had climbed the" | | 1 | "Detective Harlow Quinn crouched beside" | | 2 | "The salt-and-pepper stubble of her" | | 3 | "DS Barnes said" | | 4 | "He stood two metres back," | | 5 | "Quinn didn't look up." | | 6 | "She touched the tile directly" | | 7 | "The grout between the ceramics" | | 8 | "The corpse at her feet," | | 9 | "Barnes stepped closer, his shoes" | | 10 | "Quinn pointed her torch at" | | 11 | "The blood on his skin" | | 12 | "He was twenty years her" | | 13 | "Quinn rolled the victim's wrist" | | 14 | "Entry requirement for the Veil" | | 15 | "She'd seen one similar in" | | 16 | "Barnes laughed, the sound bouncing" | | 17 | "His gaze fixed on the" | | 18 | "She patted the victim's coat" | | 19 | "Her fingers closed around a" |
| | ratio | 0.841 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 63 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "Yet the arterial spray had travelled upward, against every biological imperative, to paint the wall in symbols that almost matched the protective sigils she'd s…" | | 1 | "The Veil Compass sat in her palm, its face etched with protective sigils that seemed to shift under the torchlight." | | 2 | "The blood on the wall began to move, the tacky surface rippling as if alive, flowing upward now in earnest, toward the ceiling, toward the curved arches of the …" |
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| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "Barnes stepped, his shoes crunching on debris" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | 0 | "Barnes laughed (laugh)" |
| | dialogueSentences | 36 | | tagDensity | 0.278 | | leniency | 0.556 | | rawRatio | 0.1 | | effectiveRatio | 0.056 | |