| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.472 | | leniency | 0.944 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1286 | | 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) | |
| 49.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1286 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "chill" | | 1 | "echo" | | 2 | "flicked" | | 3 | "traced" | | 4 | "etched" | | 5 | "intricate" | | 6 | "trembled" | | 7 | "synthetic" | | 8 | "tracing" | | 9 | "constructed" | | 10 | "scanned" |
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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 | 59 | | matches | (empty) | |
| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 67 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1275 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 22.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 824 | | uniqueNames | 14 | | maxNameDensity | 2.55 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 3 | | Harlow | 21 | | Quinn | 1 | | Kowalski | 1 | | Eva | 13 | | Veil | 1 | | Market | 1 | | Cramped | 1 | | Lock | 2 | | London | 1 | | Aurora | 1 | | Oxford | 1 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Museum" |
| | places | | 0 | "Veil" | | 1 | "London" | | 2 | "Oxford" | | 3 | "British" |
| | globalScore | 0.226 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.784 | | wordCount | 1275 | | matches | | 0 | "not at a portal, but pointing toward the east tunnel, a disused service passage" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 35.42 | | std | 25.67 | | cv | 0.725 | | sampleLengths | | 0 | 75 | | 1 | 50 | | 2 | 7 | | 3 | 1 | | 4 | 56 | | 5 | 6 | | 6 | 23 | | 7 | 30 | | 8 | 2 | | 9 | 29 | | 10 | 53 | | 11 | 37 | | 12 | 55 | | 13 | 4 | | 14 | 12 | | 15 | 59 | | 16 | 17 | | 17 | 61 | | 18 | 76 | | 19 | 23 | | 20 | 27 | | 21 | 16 | | 22 | 93 | | 23 | 55 | | 24 | 6 | | 25 | 69 | | 26 | 43 | | 27 | 7 | | 28 | 4 | | 29 | 49 | | 30 | 49 | | 31 | 5 | | 32 | 67 | | 33 | 26 | | 34 | 16 | | 35 | 67 |
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| 87.42% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 59 | | matches | | 0 | "was etched" | | 1 | "been moved" | | 2 | "was turned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 129 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 5 | | flaggedSentences | 9 | | totalSentences | 78 | | ratio | 0.115 | | matches | | 0 | "She wore her worn leather watch on her left wrist; it clicked sharply as she moved, measuring intervals rather than minutes." | | 1 | "The air smelled of rust, stale electricity, and something chemical—alchemical residue, perhaps, or something sold at the Veil Market." | | 2 | "Harlow did not look at the symbols; she looked at the blood." | | 3 | "The face was etched with protective sigils—intricate, symmetrical, precise." | | 4 | "Rigor mortis had set in the wrong posture; the limbs were too stiff to have folded like that after death." | | 5 | "The kill site was elsewhere—perhaps the market itself, perhaps a tunnel where the clique conducted business behind the market’s veil." | | 6 | "The water on the floor was not rain; it carried a chemical sweetness, silver nitrate and synthetic mercury compounds, both banned alchemical substances sold through the market’s hidden stalls." | | 7 | "“The water contains synthetic compounds from the market’s inventory. It’s not portal residue; it’s supply chain evidence.” Harlow moved to the turnstile, tracing the blood arc again." | | 8 | "Harlow pointed to a thin fiber caught in the weave—paper, the kind used at the British Museum’s restricted archives." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 832 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.03485576923076923 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006009615384615385 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 16.35 | | std | 12.95 | | cv | 0.793 | | sampleLengths | | 0 | 23 | | 1 | 31 | | 2 | 21 | | 3 | 17 | | 4 | 33 | | 5 | 7 | | 6 | 1 | | 7 | 29 | | 8 | 27 | | 9 | 6 | | 10 | 17 | | 11 | 6 | | 12 | 3 | | 13 | 19 | | 14 | 8 | | 15 | 2 | | 16 | 10 | | 17 | 9 | | 18 | 10 | | 19 | 12 | | 20 | 22 | | 21 | 14 | | 22 | 5 | | 23 | 7 | | 24 | 7 | | 25 | 23 | | 26 | 3 | | 27 | 11 | | 28 | 7 | | 29 | 9 | | 30 | 25 | | 31 | 4 | | 32 | 8 | | 33 | 4 | | 34 | 12 | | 35 | 14 | | 36 | 33 | | 37 | 11 | | 38 | 6 | | 39 | 12 | | 40 | 15 | | 41 | 34 | | 42 | 7 | | 43 | 20 | | 44 | 20 | | 45 | 29 | | 46 | 10 | | 47 | 13 | | 48 | 8 | | 49 | 19 |
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| 48.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.34615384615384615 | | totalSentences | 78 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 53 | | matches | | 0 | "She wore her worn leather" | | 1 | "She clutched her worn leather" | | 2 | "She crouched, her coat hem" | | 3 | "It arced in a thin" | | 4 | "She traced the trajectory with" | | 5 | "She opened her satchel, extracting" | | 6 | "Its needle still pointed east," | | 7 | "She looked at the body," | | 8 | "She walked toward the tunnel," |
| | ratio | 0.17 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 53 | | matches | | 0 | "The rain came down hard" | | 1 | "Detective Harlow Quinn pushed through" | | 2 | "She wore her worn leather" | | 3 | "A corpse lay near the" | | 4 | "The mouth hung open, frozen" | | 5 | "A voice cut through the" | | 6 | "Eva Kowalski stood at the" | | 7 | "She clutched her worn leather" | | 8 | "Eva’s green eyes flicked to" | | 9 | "Harlow stepped closer." | | 10 | "The air smelled of rust," | | 11 | "She crouched, her coat hem" | | 12 | "Eva pointed with a gloved" | | 13 | "Harlow did not look at" | | 14 | "It arced in a thin" | | 15 | "She traced the trajectory with" | | 16 | "Someone had stood over him." | | 17 | "She opened her satchel, extracting" | | 18 | "Harlow crouched lower." | | 19 | "Verdigris coated its casing like" |
| | ratio | 0.943 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 68.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 3 | | matches | | 0 | "The needle trembled, not at a portal, but pointing toward the east tunnel, a disused service passage that led to the surface above Camden Lock." | | 1 | "For a moment, the occult researcher in her fought with the childhood friend who knew Harlow’s grief, the woman who had moved to London two years before Aurora a…" | | 2 | "The sound of their steps blended with the buzz of the lights and the smell of rust and lies, the compass still pointing toward an exit that would not hide them …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 66.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "Eva continued (continue)" | | 1 | "sharp jaw refusing (refuse)" | | 2 | "Eva admitted (admit)" |
| | dialogueSentences | 36 | | tagDensity | 0.194 | | leniency | 0.389 | | rawRatio | 0.429 | | effectiveRatio | 0.167 | |