| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 70 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1205 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 70.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1205 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "silk" | | 1 | "weight" | | 2 | "etched" | | 3 | "shattered" | | 4 | "pulsed" | | 5 | "whisper" |
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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 | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 98 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 146 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 10 | | totalWords | 1205 | | ratio | 0.008 | | matches | | 0 | "Powder of Prophecy. Essence of Second Sight. Tincture of Tiresias." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 733 | | uniqueNames | 16 | | maxNameDensity | 2.18 | | worstName | "Webb" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 15 | | Quinn | 2 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Marcus | 1 | | Webb | 16 | | Italian | 1 | | Underground | 1 | | Canary | 1 | | Wharf | 1 | | Prophecy | 1 | | Second | 1 | | Sight | 1 | | Eva | 8 | | Kowalski | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Marcus" | | 4 | "Webb" | | 5 | "Eva" | | 6 | "Kowalski" |
| | places | | | globalScore | 0.409 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.83 | | wordCount | 1205 | | matches | | 0 | "not northward, but toward the platform, toward the tunnels, toward the dark" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 146 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 16.28 | | std | 13.61 | | cv | 0.836 | | sampleLengths | | 0 | 11 | | 1 | 67 | | 2 | 3 | | 3 | 42 | | 4 | 1 | | 5 | 18 | | 6 | 1 | | 7 | 33 | | 8 | 18 | | 9 | 4 | | 10 | 4 | | 11 | 39 | | 12 | 15 | | 13 | 2 | | 14 | 36 | | 15 | 15 | | 16 | 3 | | 17 | 40 | | 18 | 3 | | 19 | 26 | | 20 | 7 | | 21 | 4 | | 22 | 40 | | 23 | 3 | | 24 | 8 | | 25 | 44 | | 26 | 8 | | 27 | 17 | | 28 | 41 | | 29 | 7 | | 30 | 2 | | 31 | 31 | | 32 | 10 | | 33 | 33 | | 34 | 5 | | 35 | 3 | | 36 | 27 | | 37 | 33 | | 38 | 9 | | 39 | 13 | | 40 | 22 | | 41 | 3 | | 42 | 39 | | 43 | 18 | | 44 | 1 | | 45 | 30 | | 46 | 21 | | 47 | 5 | | 48 | 29 | | 49 | 14 |
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| 90.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 98 | | matches | | 0 | "been smashed" | | 1 | "been ransacked" | | 2 | "been sent" | | 3 | "been sent" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 122 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 146 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 734 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.025885558583106268 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006811989100817439 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 146 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 146 | | mean | 8.25 | | std | 6.1 | | cv | 0.74 | | sampleLengths | | 0 | 11 | | 1 | 20 | | 2 | 31 | | 3 | 16 | | 4 | 3 | | 5 | 21 | | 6 | 21 | | 7 | 1 | | 8 | 11 | | 9 | 7 | | 10 | 1 | | 11 | 13 | | 12 | 20 | | 13 | 5 | | 14 | 5 | | 15 | 8 | | 16 | 4 | | 17 | 4 | | 18 | 13 | | 19 | 4 | | 20 | 15 | | 21 | 7 | | 22 | 5 | | 23 | 10 | | 24 | 2 | | 25 | 15 | | 26 | 6 | | 27 | 15 | | 28 | 4 | | 29 | 11 | | 30 | 3 | | 31 | 8 | | 32 | 15 | | 33 | 9 | | 34 | 1 | | 35 | 7 | | 36 | 3 | | 37 | 10 | | 38 | 16 | | 39 | 7 | | 40 | 4 | | 41 | 4 | | 42 | 19 | | 43 | 2 | | 44 | 15 | | 45 | 3 | | 46 | 8 | | 47 | 5 | | 48 | 22 | | 49 | 6 |
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| 90.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5684931506849316 | | totalSentences | 146 | | uniqueOpeners | 83 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | 0 | "Almost too hot to hold." |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 78 | | matches | | 0 | "She lifted the scarf" | | 1 | "She worked the compass free." | | 2 | "She'd seen markings like these" | | 3 | "She walked to the nearest" | | 4 | "She moved to the next" | | 5 | "Her green eyes swept the" | | 6 | "She offered her hand to" | | 7 | "She hadn't been sent to" | | 8 | "She'd been sent to a" |
| | ratio | 0.115 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 78 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "The Veil Market stretched before" | | 2 | "The air hung thick with" | | 3 | "Webb jerked his thumb toward" | | 4 | "Webb shuffled his feet" | | 5 | "Harlow approached the ticket office." | | 6 | "Glass crunched under her boots." | | 7 | "The window had been smashed" | | 8 | "The body lay on its" | | 9 | "She lifted the scarf" | | 10 | "A fine silver chain glinted" | | 11 | "Webb shifted his weight." | | 12 | "The smell in the ticket" | | 13 | "Death had its own perfume," | | 14 | "The sharp, clean scent that" | | 15 | "Webb consulted his notebook" | | 16 | "Harlow studied the body." | | 17 | "The man's left hand clutched" | | 18 | "The needle twitched, not northward," | | 19 | "She worked the compass free." |
| | ratio | 0.833 | |
| 64.10% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 78 | | matches | | | ratio | 0.013 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 2 | | matches | | 0 | "Forensic lamps cast harsh white pools across the platform, illuminating tables still laden with jars of murky liquid, bundles of dried herbs, cages containing t…" | | 1 | "The woman who descended the station steps fifteen minutes later wore round glasses and a worn leather satchel crammed with books." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Webb muttered (mutter)" |
| | dialogueSentences | 70 | | tagDensity | 0.043 | | leniency | 0.086 | | rawRatio | 0.333 | | effectiveRatio | 0.029 | |