| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "Reeves stepped back [back]" |
| | dialogueSentences | 33 | | tagDensity | 0.485 | | leniency | 0.97 | | rawRatio | 0.063 | | effectiveRatio | 0.061 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1118 | | 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) | |
| 64.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1118 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "velvet" | | 1 | "perfect" | | 2 | "trembled" | | 3 | "shimmered" | | 4 | "silk" | | 5 | "traced" | | 6 | "magnetic" |
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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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 63 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1107 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 920 | | uniqueNames | 12 | | maxNameDensity | 1.85 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 17 | | Camden | 2 | | Tube | 1 | | Victorian | 3 | | Veil | 1 | | Market | 6 | | Reeves | 7 | | Webb | 5 | | Morris | 7 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Reeves" | | 4 | "Webb" | | 5 | "Morris" |
| | places | | | globalScore | 0.576 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.903 | | wordCount | 1107 | | matches | | 0 | "not random, but radiating outward from the body like spokes" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 96 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 27.68 | | std | 19.34 | | cv | 0.699 | | sampleLengths | | 0 | 64 | | 1 | 91 | | 2 | 31 | | 3 | 46 | | 4 | 1 | | 5 | 27 | | 6 | 3 | | 7 | 55 | | 8 | 25 | | 9 | 41 | | 10 | 4 | | 11 | 10 | | 12 | 35 | | 13 | 1 | | 14 | 27 | | 15 | 26 | | 16 | 38 | | 17 | 12 | | 18 | 4 | | 19 | 46 | | 20 | 33 | | 21 | 23 | | 22 | 9 | | 23 | 39 | | 24 | 36 | | 25 | 16 | | 26 | 22 | | 27 | 16 | | 28 | 14 | | 29 | 43 | | 30 | 26 | | 31 | 21 | | 32 | 25 | | 33 | 12 | | 34 | 22 | | 35 | 8 | | 36 | 54 | | 37 | 63 | | 38 | 21 | | 39 | 17 |
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| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | 0 | "been repointed" | | 1 | "been sacrificed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 157 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 96 | | ratio | 0.052 | | matches | | 0 | "Her boots crunched on broken glass and something softer—black velvet, sodden with damp." | | 1 | "Now wooden crates lay splintered among scattered bone tokens—entry requirements for the supernatural black market that shifted locations every thirty days." | | 2 | "The bone tokens scattered in a pattern—not random, but radiating outward from the body like spokes." | | 3 | "Inside the breast pocket, silver dust shimmered against the silk lining—alchemical residue, banned substances from the Market." | | 4 | "Morris's right hand gripped a bone token—the entry requirement for the Market, clutched so tight it had left indentations in his palm. But his left hand pointed upward, through the brick, toward Camden above. Toward whoever had used Webb to replace the seal, to maintain the Market's concealment from both supernatural and mundane discovery." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 567 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.014109347442680775 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 11.53 | | std | 10.42 | | cv | 0.903 | | sampleLengths | | 0 | 18 | | 1 | 13 | | 2 | 10 | | 3 | 5 | | 4 | 18 | | 5 | 5 | | 6 | 13 | | 7 | 14 | | 8 | 22 | | 9 | 21 | | 10 | 16 | | 11 | 8 | | 12 | 13 | | 13 | 10 | | 14 | 4 | | 15 | 4 | | 16 | 14 | | 17 | 14 | | 18 | 5 | | 19 | 5 | | 20 | 1 | | 21 | 11 | | 22 | 5 | | 23 | 11 | | 24 | 3 | | 25 | 4 | | 26 | 17 | | 27 | 16 | | 28 | 1 | | 29 | 10 | | 30 | 7 | | 31 | 6 | | 32 | 19 | | 33 | 4 | | 34 | 12 | | 35 | 13 | | 36 | 10 | | 37 | 2 | | 38 | 4 | | 39 | 6 | | 40 | 4 | | 41 | 5 | | 42 | 11 | | 43 | 8 | | 44 | 11 | | 45 | 1 | | 46 | 6 | | 47 | 21 | | 48 | 6 | | 49 | 16 |
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| 77.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5 | | totalSentences | 96 | | uniqueOpeners | 48 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Then it trembled toward the" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 74 | | matches | | 0 | "Her boots crunched on broken" | | 1 | "She checked the worn leather" | | 2 | "She descended the iron stairs." | | 3 | "Her torch beam caught her" | | 4 | "He kicked a nearby crate," | | 5 | "Her sharp jaw tightened." | | 6 | "She crouched beside the body," | | 7 | "She studied the blood." | | 8 | "It pooled beneath the torso" | | 9 | "She stood and walked the" | | 10 | "She touched it." | | 11 | "Her finger went numb, cold" | | 12 | "She walked to the blood-spattered" | | 13 | "They converge inward." | | 14 | "She pulled out her phone." | | 15 | "We buried him three years" | | 16 | "I attended the" |
| | ratio | 0.23 | |
| 54.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 74 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "Her boots crunched on broken" | | 2 | "She checked the worn leather" | | 3 | "The tunnel exhaled air that" | | 4 | "She descended the iron stairs." | | 5 | "Each step landed with military" | | 6 | "Her torch beam caught her" | | 7 | "The abandoned station platform stretched" | | 8 | "Constable Reeves stood too close" | | 9 | "He kicked a nearby crate," | | 10 | "Quinn circled the scene." | | 11 | "Her sharp jaw tightened." | | 12 | "She crouched beside the body," | | 13 | "The victim's hands clutched a" | | 14 | "The needle spun counter-clockwise, restless." | | 15 | "Reeves lit a cigarette" | | 16 | "The flame illuminated his skepticism." | | 17 | "She studied the blood." | | 18 | "It pooled beneath the torso" | | 19 | "The splatter on the curved" |
| | ratio | 0.811 | |
| 67.57% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 74 | | matches | | 0 | "Now wooden crates lay splintered" |
| | ratio | 0.014 | |
| 56.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 4 | | matches | | 0 | "Now wooden crates lay splintered among scattered bone tokens—entry requirements for the supernatural black market that shifted locations every thirty days." | | 1 | "The splatter on the curved brick walls arced upward, defying gravity, forming patterns that looked deliberate." | | 2 | "The thumb pressed a specific sigil on the casing, as if activating it." | | 3 | "Officers brought sledgehammers. The first blow cracked the fresh mortar, sending white dust into the damp air. The second blow revealed darkness beyond the Vict…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 33 | | tagDensity | 0.03 | | leniency | 0.061 | | rawRatio | 1 | | effectiveRatio | 0.061 | |