| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.304 | | leniency | 0.609 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 906 | | 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) | |
| 77.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 906 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "fractured" | | 1 | "pulse" | | 2 | "depths" | | 3 | "flicked" |
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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 | 76 | | matches | (empty) | |
| 67.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 906 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.09% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 756 | | uniqueNames | 15 | | maxNameDensity | 1.06 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Soho | 1 | | Harlow | 1 | | Quinn | 8 | | Saint | 1 | | Christopher | 1 | | Herrera | 7 | | Morris | 3 | | Camden | 2 | | High | 1 | | Street | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" |
| | globalScore | 0.971 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 906 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 92 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 25.17 | | std | 23.02 | | cv | 0.915 | | sampleLengths | | 0 | 97 | | 1 | 50 | | 2 | 5 | | 3 | 5 | | 4 | 17 | | 5 | 6 | | 6 | 54 | | 7 | 16 | | 8 | 14 | | 9 | 12 | | 10 | 12 | | 11 | 54 | | 12 | 48 | | 13 | 20 | | 14 | 16 | | 15 | 4 | | 16 | 9 | | 17 | 10 | | 18 | 21 | | 19 | 60 | | 20 | 9 | | 21 | 12 | | 22 | 76 | | 23 | 37 | | 24 | 13 | | 25 | 4 | | 26 | 53 | | 27 | 13 | | 28 | 10 | | 29 | 45 | | 30 | 19 | | 31 | 20 | | 32 | 7 | | 33 | 5 | | 34 | 5 | | 35 | 48 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 76 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 135 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 7 | | flaggedSentences | 7 | | totalSentences | 92 | | ratio | 0.076 | | matches | | 0 | "Eighteen years of the job had drilled her spine straight; even in the downpour she held that military precision." | | 1 | "Quinn's longer stride ate the distance; her coat flared behind her." | | 2 | "Metal cracked; the driver leaned on the horn, the blast swallowed by the rain." | | 3 | "\"Not dead.\" He vaulted a bin; the lid clanged." | | 4 | "Herrera knew the rat-runs; he took them through an alley reeking of chip fat and piss." | | 5 | "The full moon had dragged the underground black market here three nights past; by the next moon it would move again." | | 6 | "Her shoulder took the edge; pain flared along her arm, but she was through." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 764 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.014397905759162303 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003926701570680628 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 9.85 | | std | 5.45 | | cv | 0.553 | | sampleLengths | | 0 | 24 | | 1 | 18 | | 2 | 20 | | 3 | 19 | | 4 | 6 | | 5 | 8 | | 6 | 2 | | 7 | 7 | | 8 | 9 | | 9 | 14 | | 10 | 2 | | 11 | 18 | | 12 | 5 | | 13 | 5 | | 14 | 17 | | 15 | 6 | | 16 | 10 | | 17 | 11 | | 18 | 12 | | 19 | 7 | | 20 | 14 | | 21 | 6 | | 22 | 10 | | 23 | 5 | | 24 | 9 | | 25 | 12 | | 26 | 9 | | 27 | 3 | | 28 | 10 | | 29 | 10 | | 30 | 12 | | 31 | 17 | | 32 | 5 | | 33 | 16 | | 34 | 16 | | 35 | 16 | | 36 | 6 | | 37 | 14 | | 38 | 3 | | 39 | 6 | | 40 | 7 | | 41 | 4 | | 42 | 9 | | 43 | 4 | | 44 | 6 | | 45 | 21 | | 46 | 7 | | 47 | 2 | | 48 | 18 | | 49 | 18 |
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| 56.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3695652173913043 | | totalSentences | 92 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 63.48% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 69 | | matches | | 0 | "She snapped her left wrist" | | 1 | "He froze for half a" | | 2 | "She swore and went after" | | 3 | "His trainers slapped through puddles" | | 4 | "She followed, shoulder clipping the" | | 5 | "Her boots struck wet grit" | | 6 | "He laughed, the sound ragged." | | 7 | "He vaulted a bin; the" | | 8 | "She hooked the bin with" | | 9 | "Her pulse beat a hard" | | 10 | "She'd lost Morris in weather" | | 11 | "She shoved the thought down." | | 12 | "They hammered north, past shuttered" | | 13 | "He glanced back." | | 14 | "His voice cracked on the" | | 15 | "Her partner, DS Morris, gone" | | 16 | "She remembered the photographs she" | | 17 | "He burst out of the" | | 18 | "He dug into his pocket" | | 19 | "She planted her feet, shoulders" |
| | ratio | 0.391 | |
| 3.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 69 | | matches | | 0 | "The green neon sign above" | | 1 | "Detective Harlow Quinn stood in" | | 2 | "She snapped her left wrist" | | 3 | "The worn leather watch creaked" | | 4 | "The door of the Nest" | | 5 | "A man stepped out, shrugging" | | 6 | "The scar on his left" | | 7 | "Quinn pushed off the wall." | | 8 | "He froze for half a" | | 9 | "She swore and went after" | | 10 | "His trainers slapped through puddles" | | 11 | "Quinn's longer stride ate the" | | 12 | "A black cab blared its" | | 13 | "She followed, shoulder clipping the" | | 14 | "Metal cracked; the driver leaned" | | 15 | "Her boots struck wet grit" | | 16 | "He laughed, the sound ragged." | | 17 | "He vaulted a bin; the" | | 18 | "She hooked the bin with" | | 19 | "Rain filled her mouth, tasted" |
| | ratio | 0.913 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 2 | | matches | | 0 | "Enchanted goods, banned alchemical substances, information that could gut a case." | | 1 | "Voices murmured in the depths, the clink of glass, the rustle of things that shouldn't breathe." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.13 | | leniency | 0.261 | | rawRatio | 0 | | effectiveRatio | 0 | |