| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1446 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "carefully" | | 1 | "slowly" | | 2 | "softly" |
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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) | |
| 41.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1446 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "chill" | | 1 | "echoing" | | 2 | "pulse" | | 3 | "flicker" | | 4 | "weight" | | 5 | "reminder" | | 6 | "scanning" | | 7 | "synthetic" | | 8 | "velvet" | | 9 | "etched" | | 10 | "aligned" | | 11 | "glinting" | | 12 | "silence" | | 13 | "gloom" | | 14 | "sense of" |
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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 | 112 | | matches | (empty) | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 112 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1446 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 27.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1103 | | uniqueNames | 10 | | maxNameDensity | 2.45 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 27 | | Quinn | 2 | | Piccadilly | 1 | | Morris | 3 | | East | 1 | | London | 1 | | Kowalski | 2 | | Eva | 13 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Market" |
| | places | | | globalScore | 0.276 | | windowScore | 0.5 | |
| 81.51% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like the result of a heat that cam" | | 1 | "sigils that seemed to shift when looked at sideways" |
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| 61.69% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.383 | | wordCount | 1446 | | matches | | 0 | "not toward the body, but toward the dark tunnel mouth at the end of the platform" | | 1 | "not to the tunnel, but straight back at the body she had just left behind" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 37.08 | | std | 23.61 | | cv | 0.637 | | sampleLengths | | 0 | 90 | | 1 | 73 | | 2 | 67 | | 3 | 21 | | 4 | 55 | | 5 | 56 | | 6 | 33 | | 7 | 42 | | 8 | 49 | | 9 | 66 | | 10 | 12 | | 11 | 14 | | 12 | 49 | | 13 | 10 | | 14 | 14 | | 15 | 13 | | 16 | 48 | | 17 | 64 | | 18 | 28 | | 19 | 19 | | 20 | 66 | | 21 | 37 | | 22 | 9 | | 23 | 40 | | 24 | 14 | | 25 | 50 | | 26 | 7 | | 27 | 54 | | 28 | 46 | | 29 | 4 | | 30 | 8 | | 31 | 7 | | 32 | 68 | | 33 | 47 | | 34 | 16 | | 35 | 12 | | 36 | 77 | | 37 | 22 | | 38 | 39 |
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| 89.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 112 | | matches | | 0 | "been sealed" | | 1 | "was hunched" | | 2 | "were etched" | | 3 | "was involved" | | 4 | "was compromised" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 191 | | matches | | 0 | "was escaping" | | 1 | "wasn't pointing" | | 2 | "was acting" | | 3 | "were using" | | 4 | "was pooling" | | 5 | "was biting" | | 6 | "was pointing" |
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| 99.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 132 | | ratio | 0.015 | | matches | | 0 | "It didn't soak into the concrete; it sat atop the surface like oil." | | 1 | "\"It's not just a stop, Detective. It's a drain. The ambient magic in this sector spiked at 01:00. He didn't bleed out; he was bled. Something here opened up.\"" |
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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 | 32 | | adverbRatio | 0.028985507246376812 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009963768115942028 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 10.95 | | std | 6.95 | | cv | 0.634 | | sampleLengths | | 0 | 11 | | 1 | 22 | | 2 | 27 | | 3 | 19 | | 4 | 11 | | 5 | 12 | | 6 | 1 | | 7 | 20 | | 8 | 25 | | 9 | 15 | | 10 | 10 | | 11 | 14 | | 12 | 16 | | 13 | 27 | | 14 | 8 | | 15 | 13 | | 16 | 6 | | 17 | 25 | | 18 | 11 | | 19 | 13 | | 20 | 1 | | 21 | 3 | | 22 | 25 | | 23 | 3 | | 24 | 24 | | 25 | 8 | | 26 | 17 | | 27 | 8 | | 28 | 13 | | 29 | 29 | | 30 | 4 | | 31 | 14 | | 32 | 31 | | 33 | 16 | | 34 | 15 | | 35 | 4 | | 36 | 13 | | 37 | 18 | | 38 | 4 | | 39 | 3 | | 40 | 5 | | 41 | 14 | | 42 | 4 | | 43 | 11 | | 44 | 4 | | 45 | 5 | | 46 | 7 | | 47 | 18 | | 48 | 5 | | 49 | 5 |
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| 40.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.29770992366412213 | | totalSentences | 131 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 64.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 100 | | matches | | 0 | "She stopped on the tiled" | | 1 | "She checked her watch, the" | | 2 | "They looked like the result" | | 3 | "It snapped back against her" | | 4 | "It didn't soak into the" | | 5 | "Her curly red hair was" | | 6 | "She didn't smile." | | 7 | "She just reached out and" | | 8 | "She kept her voice low," | | 9 | "It skittered across the tiles" | | 10 | "She reached into her satchel" | | 11 | "She unwrapped it carefully." | | 12 | "It was a brass compass," | | 13 | "It was warm, heavier than" | | 14 | "She flipped it open." | | 15 | "It was trembling, spinning slowly" | | 16 | "It pointed not toward the" | | 17 | "She gestured to the corpse" | | 18 | "She turned the compass over" | | 19 | "She had suspected the clique" |
| | ratio | 0.39 | |
| 5.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 100 | | matches | | 0 | "The air down here tasted" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "She stopped on the tiled" | | 3 | "She checked her watch, the" | | 4 | "The file had been sealed," | | 5 | "They looked like the result" | | 6 | "A flicker of movement to" | | 7 | "Eva Kowalski stood over a" | | 8 | "The young researcher was hunched," | | 9 | "A leather satchel full of" | | 10 | "Quinn stepped over the caution" | | 11 | "It snapped back against her" | | 12 | "Harlow looked down at the" | | 13 | "A man in his thirties," | | 14 | "The blood was black, not" | | 15 | "It didn't soak into the" | | 16 | "Kowalski looked up." | | 17 | "Her curly red hair was" | | 18 | "She didn't smile." | | 19 | "She just reached out and" |
| | ratio | 0.91 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 100 | | matches | | 0 | "To the outside world, this" | | 1 | "To Harlow, it was simply" | | 2 | "If this was a ritual" | | 3 | "If it was a theft," |
| | ratio | 0.04 | |
| 86.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn adjusted the collar of her trench coat, ignoring the damp chill that had already seeped through to her skin." | | 1 | "The file had been sealed, the cause listed as a structural collapse, but Harlow had seen the burns on the floorboards that didn't look chemical." | | 2 | "Her curly red hair was escaping its tie, and she had a dusting of freckles across her nose that stood out against her pale skin." | | 3 | "The memory of Morris flooded back, the smell of ozone, the heat that shouldn't have been possible." |
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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 gaining speed" | | 1 | "Eva said, confused" |
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| 64.29% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 3 | | fancyTags | | 0 | "Harlow muttered (mutter)" | | 1 | "Eva whispered (whisper)" | | 2 | "Harlow ordered (order)" |
| | dialogueSentences | 35 | | tagDensity | 0.343 | | leniency | 0.686 | | rawRatio | 0.25 | | effectiveRatio | 0.171 | |