| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1011 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 55.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1011 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "gloom" | | 1 | "standard" | | 2 | "silence" | | 3 | "stomach" | | 4 | "flicker" | | 5 | "glinting" | | 6 | "weight" | | 7 | "could feel" |
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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 | 96 | | matches | (empty) | |
| 98.21% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1009 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 1009 | | uniqueNames | 10 | | maxNameDensity | 0.59 | | worstName | "Harlow" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Quinn | 5 | | Morris | 3 | | Soho | 1 | | Camden | 1 | | Metropolitan | 1 | | Police | 1 | | Veil | 1 | | Market | 1 | | Harlow | 6 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Harlow" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 5 | | matches | | 0 | "tasted like copper in her mouth and hadn'" | | 1 | "wind that seemed to originate from below rather than outside" | | 2 | "gloom that seemed to swallow the light from her torch" | | 3 | "looked like currency or sigils" | | 4 | "felt like the stomach of an animal that" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.991 | | wordCount | 1009 | | matches | | 0 | "not for trains, but for trade" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 75.71% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 53.11 | | std | 22.04 | | cv | 0.415 | | sampleLengths | | 0 | 81 | | 1 | 12 | | 2 | 74 | | 3 | 81 | | 4 | 76 | | 5 | 58 | | 6 | 16 | | 7 | 68 | | 8 | 63 | | 9 | 65 | | 10 | 67 | | 11 | 55 | | 12 | 60 | | 13 | 15 | | 14 | 63 | | 15 | 47 | | 16 | 21 | | 17 | 36 | | 18 | 51 |
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| 86.99% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 96 | | matches | | 0 | "was jammed" | | 1 | "been closed" | | 2 | "been modified" | | 3 | "were covered" | | 4 | "were gone" |
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| 1.59% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 168 | | matches | | 0 | "was disappearing" | | 1 | "was still missing" | | 2 | "were whispering" | | 3 | "was looking" | | 4 | "was assessing" |
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| 23.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 3 | | flaggedSentences | 4 | | totalSentences | 96 | | ratio | 0.042 | | matches | | 0 | "The rain didn't fall in London; it fell like a verdict, hammering against the pavement with the cold finality of a gavel." | | 1 | "She didn't stumble; she didn't breathe heavily." | | 2 | "If this was the link—if the supernatural origins that had swallowed her partner were here in the earth below—she needed the truth." | | 3 | "He wasn't alarmed; he was assessing." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1011 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, pressing against her" |
| | adverbCount | 24 | | adverbRatio | 0.02373887240356083 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00791295746785361 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 10.51 | | std | 5.89 | | cv | 0.56 | | sampleLengths | | 0 | 22 | | 1 | 16 | | 2 | 20 | | 3 | 3 | | 4 | 4 | | 5 | 16 | | 6 | 12 | | 7 | 12 | | 8 | 7 | | 9 | 17 | | 10 | 20 | | 11 | 18 | | 12 | 2 | | 13 | 4 | | 14 | 18 | | 15 | 16 | | 16 | 17 | | 17 | 15 | | 18 | 9 | | 19 | 5 | | 20 | 22 | | 21 | 5 | | 22 | 4 | | 23 | 11 | | 24 | 9 | | 25 | 13 | | 26 | 7 | | 27 | 5 | | 28 | 15 | | 29 | 5 | | 30 | 12 | | 31 | 4 | | 32 | 17 | | 33 | 7 | | 34 | 9 | | 35 | 6 | | 36 | 8 | | 37 | 8 | | 38 | 24 | | 39 | 22 | | 40 | 7 | | 41 | 11 | | 42 | 17 | | 43 | 20 | | 44 | 3 | | 45 | 5 | | 46 | 2 | | 47 | 13 | | 48 | 3 | | 49 | 14 |
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| 28.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.15625 | | totalSentences | 96 | | uniqueOpeners | 15 | |
| 35.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 94 | | matches | | 0 | "Maybe the city's heart beat" |
| | ratio | 0.011 | |
| 32.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 94 | | matches | | 0 | "She checked the leather face" | | 1 | "It was 02:14." | | 2 | "She didn't stumble; she didn't" | | 3 | "Her bearing was military precision" | | 4 | "She had no backup." | | 5 | "Her dispatch was jammed in" | | 6 | "She rounded the corner and" | | 7 | "It was locked, but the" | | 8 | "It was a mouth." | | 9 | "She gripped the gate, her" | | 10 | "It wasn't the usual smell" | | 11 | "It smelled of ozone and" | | 12 | "She peered over the edge." | | 13 | "She didn't have a torch." | | 14 | "She only had a standard" | | 15 | "She flipped it on." | | 16 | "She moved methodically, one step" | | 17 | "Her training screamed at her" | | 18 | "She reached the bottom of" | | 19 | "They were whispering." |
| | ratio | 0.468 | |
| 7.87% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 94 | | matches | | 0 | "The rain didn't fall in" | | 1 | "Harlow Quinn pulled the collar" | | 2 | "She checked the leather face" | | 3 | "It was 02:14." | | 4 | "The time Morris disappeared." | | 5 | "A coincidence that tasted like" | | 6 | "Quinn broke into a run," | | 7 | "She didn't stumble; she didn't" | | 8 | "Her bearing was military precision" | | 9 | "The suspect, a slender figure" | | 10 | "The shadow turned left, towards" | | 11 | "She had no backup." | | 12 | "Her dispatch was jammed in" | | 13 | "She rounded the corner and" | | 14 | "The station had been closed" | | 15 | "The grey duster figure was" | | 16 | "Harlow hit the iron gate." | | 17 | "It was locked, but the" | | 18 | "This wasn't a public station." | | 19 | "It was a mouth." |
| | ratio | 0.904 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 94 | | matches | | 0 | "Yet, there was a slither" | | 1 | "If this was the link—if" | | 2 | "If she stepped back out," | | 3 | "If she stayed, she walked" | | 4 | "If she turned back, she" | | 5 | "If she stayed, she would" |
| | ratio | 0.064 | |
| 65.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 5 | | matches | | 0 | "Her bearing was military precision honed by the force of a grief that wouldn't let her sleep." | | 1 | "It was locked, but the chain hung loose, swinging slightly in the wind that seemed to originate from below rather than outside." | | 2 | "It wasn't the usual smell of mold and urine that characterized abandoned tubes." | | 3 | "The beam cut through the damp haze, illuminating dust motes that floated unnaturally, swirling against the airflow." | | 4 | "If this was the link—if the supernatural origins that had swallowed her partner were here in the earth below—she needed the truth." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |