| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1516 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 83.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1516 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "mosaic" | | 1 | "familiar" | | 2 | "pulse" | | 3 | "footsteps" | | 4 | "trembled" |
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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 | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 162 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1516 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1123 | | uniqueNames | 13 | | maxNameDensity | 1.51 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 17 | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Sergeant | 1 | | Baines | 14 | | Croft | 9 | | Kowalski | 1 | | Victorian | 1 | | Compass | 1 | | Eva | 10 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Sergeant" | | 4 | "Baines" | | 5 | "Croft" | | 6 | "Kowalski" | | 7 | "Compass" | | 8 | "Eva" |
| | places | | | globalScore | 0.743 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.66 | | wordCount | 1516 | | matches | | 0 | "Not with a creak, but with the sound of a train passing in the tunnels below" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 162 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 87 | | mean | 17.43 | | std | 21.92 | | cv | 1.258 | | sampleLengths | | 0 | 79 | | 1 | 54 | | 2 | 2 | | 3 | 28 | | 4 | 103 | | 5 | 2 | | 6 | 6 | | 7 | 14 | | 8 | 9 | | 9 | 1 | | 10 | 4 | | 11 | 12 | | 12 | 55 | | 13 | 2 | | 14 | 3 | | 15 | 3 | | 16 | 15 | | 17 | 5 | | 18 | 5 | | 19 | 6 | | 20 | 8 | | 21 | 63 | | 22 | 2 | | 23 | 2 | | 24 | 6 | | 25 | 16 | | 26 | 4 | | 27 | 18 | | 28 | 3 | | 29 | 3 | | 30 | 7 | | 31 | 66 | | 32 | 55 | | 33 | 2 | | 34 | 5 | | 35 | 2 | | 36 | 16 | | 37 | 4 | | 38 | 6 | | 39 | 6 | | 40 | 6 | | 41 | 3 | | 42 | 1 | | 43 | 22 | | 44 | 3 | | 45 | 2 | | 46 | 4 | | 47 | 21 | | 48 | 7 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 118 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 178 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 162 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 639 | | adjectiveStacks | 1 | | stackExamples | | 0 | "white against dark wool." |
| | adverbCount | 4 | | adverbRatio | 0.006259780907668232 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 162 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 162 | | mean | 9.36 | | std | 6.46 | | cv | 0.69 | | sampleLengths | | 0 | 14 | | 1 | 32 | | 2 | 8 | | 3 | 25 | | 4 | 8 | | 5 | 8 | | 6 | 20 | | 7 | 18 | | 8 | 2 | | 9 | 12 | | 10 | 16 | | 11 | 4 | | 12 | 10 | | 13 | 12 | | 14 | 9 | | 15 | 11 | | 16 | 9 | | 17 | 19 | | 18 | 8 | | 19 | 9 | | 20 | 12 | | 21 | 2 | | 22 | 6 | | 23 | 14 | | 24 | 9 | | 25 | 1 | | 26 | 4 | | 27 | 12 | | 28 | 4 | | 29 | 8 | | 30 | 6 | | 31 | 8 | | 32 | 29 | | 33 | 2 | | 34 | 3 | | 35 | 3 | | 36 | 15 | | 37 | 5 | | 38 | 5 | | 39 | 6 | | 40 | 8 | | 41 | 9 | | 42 | 5 | | 43 | 4 | | 44 | 1 | | 45 | 4 | | 46 | 11 | | 47 | 12 | | 48 | 17 | | 49 | 2 |
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| 47.12% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.30864197530864196 | | totalSentences | 162 | | uniqueOpeners | 50 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 112 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 112 | | matches | | 0 | "Her cropped salt-and-pepper hair caught" | | 1 | "She pressed two fingers against" | | 2 | "His notebook hung open in" | | 3 | "His skin had the flat" | | 4 | "Its needle swung, checked, and" | | 5 | "His hands lay crossed without" | | 6 | "His face held no twist," | | 7 | "She checked the left." | | 8 | "She turned the wrist." | | 9 | "It sat high and centered" | | 10 | "She pointed to the black" | | 11 | "It ran from the ring" | | 12 | "His heels had scraped the" | | 13 | "Her curly red hair had" | | 14 | "She tucked a strand behind" | | 15 | "Her green eyes moved over" | | 16 | "It bore a stamp of" | | 17 | "They did not abandon stock" | | 18 | "She walked to the brass" | | 19 | "She lifted the compass." |
| | ratio | 0.259 | |
| 0.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 103 | | totalSentences | 112 | | matches | | 0 | "The old station swallowed the" | | 1 | "Detective Harlow Quinn took the" | | 2 | "Police lamps carved white holes" | | 3 | "Harlow stopped at the edge" | | 4 | "Her cropped salt-and-pepper hair caught" | | 5 | "She pressed two fingers against" | | 6 | "Brown eyes moved from the" | | 7 | "Detective Sergeant Baines stood beside" | | 8 | "His notebook hung open in" | | 9 | "Harlow crossed to him." | | 10 | "The alcove smelled of wet" | | 11 | "A man lay inside with" | | 12 | "His skin had the flat" | | 13 | "A bone token rested on" | | 14 | "Chalk lines ringed him, broken" | | 15 | "The compass casing wore a" | | 16 | "Sigils cut into its face" | | 17 | "Its needle swung, checked, and" | | 18 | "Baines angled the notebook toward" | | 19 | "Harlow crouched at the edge" |
| | ratio | 0.92 | |
| 44.64% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 112 | | matches | | 0 | "If Croft had walked into" |
| | ratio | 0.009 | |
| 84.55% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn took the last step down into the abandoned Tube tunnel beneath Camden and set her boot on mosaic tile worn smooth by feet that had no bus…" | | 1 | "Market traders sold banned alchemical substances, enchanted tools, information that had teeth." | | 2 | "It was cold, but the brass warmed against her palm as if it recognized heat under the surface." | | 3 | "Croft’s bone token, on his chest, gave off heat that fogged in the cold air." |
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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 | |