| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 699 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 699 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "charged" | | 1 | "pounding" | | 2 | "gleaming" | | 3 | "loomed" | | 4 | "electric" | | 5 | "navigated" | | 6 | "sentinels" | | 7 | "reminder" | | 8 | "weight" | | 9 | "echo" | | 10 | "racing" | | 11 | "delve" | | 12 | "resolve" | | 13 | "unwavering" | | 14 | "pulsed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 46 | | matches | | |
| 80.75% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 46 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 46 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 699 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 92.78% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 699 | | uniqueNames | 13 | | maxNameDensity | 1.14 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Market" | | discoveredNames | | Harlow | 8 | | Quinn | 2 | | Soho | 1 | | London | 1 | | Tomás | 4 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Veil | 1 | | Market | 5 | | Morris | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tube" | | 7 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Market" |
| | globalScore | 0.928 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | 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 | 699 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 46 | | matches | (empty) | |
| 83.79% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 58.25 | | std | 25.82 | | cv | 0.443 | | sampleLengths | | 0 | 80 | | 1 | 52 | | 2 | 81 | | 3 | 67 | | 4 | 65 | | 5 | 6 | | 6 | 68 | | 7 | 65 | | 8 | 8 | | 9 | 53 | | 10 | 58 | | 11 | 96 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 46 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 112 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 701 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.025677603423680456 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007132667617689016 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 46 | | echoCount | 0 | | echoWords | (empty) | |
| 77.62% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 46 | | mean | 15.2 | | std | 5.23 | | cv | 0.344 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 12 | | 3 | 22 | | 4 | 10 | | 5 | 23 | | 6 | 7 | | 7 | 22 | | 8 | 17 | | 9 | 24 | | 10 | 20 | | 11 | 20 | | 12 | 15 | | 13 | 26 | | 14 | 11 | | 15 | 15 | | 16 | 18 | | 17 | 15 | | 18 | 9 | | 19 | 23 | | 20 | 6 | | 21 | 17 | | 22 | 13 | | 23 | 17 | | 24 | 12 | | 25 | 9 | | 26 | 11 | | 27 | 16 | | 28 | 16 | | 29 | 22 | | 30 | 8 | | 31 | 8 | | 32 | 20 | | 33 | 12 | | 34 | 13 | | 35 | 15 | | 36 | 13 | | 37 | 17 | | 38 | 13 | | 39 | 4 | | 40 | 9 | | 41 | 12 | | 42 | 23 | | 43 | 13 | | 44 | 18 | | 45 | 17 |
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| 70.29% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5 | | totalSentences | 46 | | uniqueOpeners | 23 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 46 | | matches | | 0 | "Instead, she moved deeper into" |
| | ratio | 0.022 | |
| 54.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 46 | | matches | | 0 | "Her brown eyes flashed with" | | 1 | "She could see nothing beyond" | | 2 | "His eyes, warm brown and" | | 3 | "His olive skin shone with" | | 4 | "His scar, pale against his" | | 5 | "She didn't understand then, but" | | 6 | "She'd learned too much and" | | 7 | "Her partner's death still hung" | | 8 | "They had entered the Veil" | | 9 | "It thrived on the fringe" | | 10 | "It had been a small" | | 11 | "She had come this far," | | 12 | "She was walking into the" | | 13 | "She glanced at her worn" | | 14 | "She thought of DS Morris," | | 15 | "She had come too far" | | 16 | "She would follow Tomás, even" | | 17 | "She had to know the" | | 18 | "She was Detective Harlow Quinn," |
| | ratio | 0.413 | |
| 46.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 46 | | matches | | 0 | "Detective Harlow Quinn charged through" | | 1 | "Glass towers, gleaming even in" | | 2 | "Her brown eyes flashed with" | | 3 | "Another blast of lightning turned" | | 4 | "Thunder followed, and he vanished" | | 5 | "She could see nothing beyond" | | 6 | "The man she pursued, Tomás" | | 7 | "His eyes, warm brown and" | | 8 | "His olive skin shone with" | | 9 | "His scar, pale against his" | | 10 | "Harlow knew of him, a" | | 11 | "She didn't understand then, but" | | 12 | "She'd learned too much and" | | 13 | "Her partner's death still hung" | | 14 | "The sound of the Tube" | | 15 | "Tomás led her down a" | | 16 | "This place was abandoned, left" | | 17 | "They had entered the Veil" | | 18 | "It thrived on the fringe" | | 19 | "It had been a small" |
| | ratio | 0.826 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 46 | | matches | | 0 | "Yet, as they plunged deeper," | | 1 | "Now, it was her entry" |
| | ratio | 0.043 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 4 | | matches | | 0 | "She could see nothing beyond the harsh orange of sodium vapour lights, ancient sentinels that kept watch over the unwary and unwanted." | | 1 | "His eyes, warm brown and framed by curly dark hair, contradicted the cool calculation that crossed his face before he darted into an alleyway." | | 2 | "Goods of all kinds lay on display, enchanted artifacts, banned alchemical substances, information that could bring down empires or build them up." | | 3 | "She thought of DS Morris, her lost partner, of the supernatural circumstances that led to his death." |
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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 | |