| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.75 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1037 | | 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) | |
| 22.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1037 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "thundered" | | 1 | "sanctuary" | | 2 | "calculated" | | 3 | "gleaming" | | 4 | "maw" | | 5 | "standard" | | 6 | "electric" | | 7 | "footsteps" | | 8 | "silence" | | 9 | "flickered" | | 10 | "etched" | | 11 | "silk" | | 12 | "velvet" |
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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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1033 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1000 | | uniqueNames | 28 | | maxNameDensity | 0.6 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Morris" | | discoveredNames | | Saint | 1 | | Christopher | 1 | | Camden | 1 | | Road | 1 | | High | 1 | | Street | 1 | | Lock | 1 | | Market | 2 | | Seville | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 6 | | Morris | 6 | | Classification | 1 | | Tube | 1 | | Kentish | 1 | | Town | 1 | | West | 1 | | Prosecuted | 1 | | Death | 1 | | Metropolitan | 1 | | Police | 1 | | London | 1 | | Glock | 3 | | Veil | 1 | | Moon | 1 | | Key | 1 | | Quinn | 6 |
| | persons | | 0 | "Saint" | | 1 | "Christopher" | | 2 | "Market" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Metropolitan" | | 6 | "Glock" | | 7 | "Moon" | | 8 | "Quinn" |
| | places | | 0 | "Camden" | | 1 | "Road" | | 2 | "High" | | 3 | "Street" | | 4 | "Seville" | | 5 | "Raven" | | 6 | "Kentish" | | 7 | "Town" | | 8 | "West" | | 9 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.968 | | wordCount | 1033 | | matches | | 0 | "not in surrender, but warning" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 107 | | matches | | 0 | "stripped that license" | | 1 | "held that watch" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 32.28 | | std | 28.08 | | cv | 0.87 | | sampleLengths | | 0 | 13 | | 1 | 92 | | 2 | 63 | | 3 | 60 | | 4 | 7 | | 5 | 57 | | 6 | 67 | | 7 | 49 | | 8 | 62 | | 9 | 66 | | 10 | 29 | | 11 | 16 | | 12 | 7 | | 13 | 16 | | 14 | 48 | | 15 | 4 | | 16 | 98 | | 17 | 27 | | 18 | 4 | | 19 | 16 | | 20 | 12 | | 21 | 70 | | 22 | 5 | | 23 | 9 | | 24 | 19 | | 25 | 3 | | 26 | 22 | | 27 | 55 | | 28 | 24 | | 29 | 6 | | 30 | 3 | | 31 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 176 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 107 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1004 | | adjectiveStacks | 1 | | stackExamples | | 0 | "open, gleaming new brass" |
| | adverbCount | 27 | | adverbRatio | 0.026892430278884463 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0069721115537848604 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 9.65 | | std | 7.3 | | cv | 0.756 | | sampleLengths | | 0 | 13 | | 1 | 16 | | 2 | 23 | | 3 | 16 | | 4 | 25 | | 5 | 6 | | 6 | 6 | | 7 | 13 | | 8 | 39 | | 9 | 11 | | 10 | 4 | | 11 | 15 | | 12 | 32 | | 13 | 9 | | 14 | 7 | | 15 | 3 | | 16 | 15 | | 17 | 8 | | 18 | 5 | | 19 | 3 | | 20 | 6 | | 21 | 4 | | 22 | 2 | | 23 | 2 | | 24 | 9 | | 25 | 2 | | 26 | 6 | | 27 | 17 | | 28 | 2 | | 29 | 7 | | 30 | 16 | | 31 | 17 | | 32 | 17 | | 33 | 6 | | 34 | 15 | | 35 | 11 | | 36 | 2 | | 37 | 7 | | 38 | 2 | | 39 | 3 | | 40 | 22 | | 41 | 5 | | 42 | 1 | | 43 | 1 | | 44 | 19 | | 45 | 4 | | 46 | 23 | | 47 | 10 | | 48 | 29 | | 49 | 7 |
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| 65.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4392523364485981 | | totalSentences | 107 | | uniqueOpeners | 47 | |
| 74.07% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 90 | | matches | | 0 | "Then the stairs ended." | | 1 | "Only the tracks remained, leading" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 90 | | matches | | 0 | "She cleared the same barrier" | | 1 | "Her heart hammered against her" | | 2 | "She knew that distinctive scar" | | 3 | "He moved like a man" | | 4 | "It plastered her salt-and-pepper crop" | | 5 | "She'd lost her umbrella at" | | 6 | "He'd seen her badge before" | | 7 | "He didn't hesitate." | | 8 | "He sprinted past the shuttered" | | 9 | "Her fingers brushed her holster." | | 10 | "She calculated the gap." | | 11 | "Her fingertips grazed the damp" | | 12 | "He dodged left, toward the" | | 13 | "Her landing sent shockwaves through" | | 14 | "He slipped through the gap" | | 15 | "Her torch beam caught the" | | 16 | "She knew about thresholds." | | 17 | "She'd filed the reports herself," | | 18 | "Her Glock felt heavy against" | | 19 | "She touched Morris's old case" |
| | ratio | 0.3 | |
| 37.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 90 | | matches | | 0 | "The Saint Christopher medallion swung" | | 1 | "Quinn's worn leather watch strap" | | 2 | "She cleared the same barrier" | | 3 | "Traffic thundered overhead on the" | | 4 | "Her heart hammered against her" | | 5 | "Some hunts required velocity over" | | 6 | "Herrera's short curly hair vanished" | | 7 | "She knew that distinctive scar" | | 8 | "He moved like a man" | | 9 | "Rain drilled the pavement." | | 10 | "It plastered her salt-and-pepper crop" | | 11 | "She'd lost her umbrella at" | | 12 | "He'd seen her badge before" | | 13 | "The word dissolved in the" | | 14 | "He didn't hesitate." | | 15 | "He sprinted past the shuttered" | | 16 | "Quinn's longer stride ate the" | | 17 | "Her fingers brushed her holster." | | 18 | "She calculated the gap." | | 19 | "Her fingertips grazed the damp" |
| | ratio | 0.844 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 90 | | matches | | 0 | "Now she stood before a" | | 1 | "Now it counted down to" |
| | ratio | 0.022 | |
| 93.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 3 | | matches | | 0 | "She knew that distinctive scar on his left forearm, pale against olive skin, the souvenir from a knife attack in Seville before the NHS had licensed him, before…" | | 1 | "Beneath the standard Metropolitan Police notices, older symbols marked the brickwork, white paint that looked almost like bone fragments arranged in patterns." | | 2 | "Now she stood before a door that shouldn't be open, smelling air that shouldn't exist in London, chasing a man who stitched wounds for creatures that lacked med…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "he whispered (whisper)" |
| | dialogueSentences | 8 | | tagDensity | 0.625 | | leniency | 1 | | rawRatio | 0.2 | | effectiveRatio | 0.2 | |