| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1118 | | 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) | |
| 28.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1118 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "chill" | | 1 | "familiar" | | 2 | "scanned" | | 3 | "silence" | | 4 | "pulse" | | 5 | "vibrated" | | 6 | "weight" | | 7 | "velvet" | | 8 | "rhythmic" | | 9 | "predictable" | | 10 | "tapestry" | | 11 | "pulsed" | | 12 | "warmth" | | 13 | "maw" |
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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 | 1 | | narrationSentences | 102 | | matches | | |
| 86.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 102 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1115 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 1021 | | uniqueNames | 11 | | maxNameDensity | 1.27 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 13 | | Wardour | 1 | | Street | 1 | | Morris | 2 | | London | 1 | | Tube | 1 | | Camden | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Camden" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "London" |
| | globalScore | 0.863 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 1 | | matches | | 0 | "silks that seemed to shimmer without a light source" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.897 | | wordCount | 1115 | | matches | | 0 | "not the stench of sewage, but something sharper" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 26.55 | | std | 16.26 | | cv | 0.612 | | sampleLengths | | 0 | 53 | | 1 | 42 | | 2 | 38 | | 3 | 2 | | 4 | 43 | | 5 | 42 | | 6 | 38 | | 7 | 10 | | 8 | 27 | | 9 | 16 | | 10 | 48 | | 11 | 46 | | 12 | 36 | | 13 | 9 | | 14 | 43 | | 15 | 49 | | 16 | 27 | | 17 | 57 | | 18 | 49 | | 19 | 30 | | 20 | 14 | | 21 | 41 | | 22 | 24 | | 23 | 3 | | 24 | 37 | | 25 | 12 | | 26 | 6 | | 27 | 31 | | 28 | 8 | | 29 | 10 | | 30 | 7 | | 31 | 26 | | 32 | 21 | | 33 | 6 | | 34 | 20 | | 35 | 29 | | 36 | 18 | | 37 | 18 | | 38 | 8 | | 39 | 52 | | 40 | 14 | | 41 | 5 |
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| 94.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 102 | | matches | | 0 | "been transformed" | | 1 | "were obscured" | | 2 | "being circled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 178 | | matches | | |
| 64.94% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 110 | | ratio | 0.027 | | matches | | 0 | "The smell hit her first—not the stench of sewage, but something sharper." | | 1 | "People moved through the haze—some draped in heavy furs, others in silks that seemed to shimmer without a light source." | | 2 | "He held up a small, carved object—a token." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1030 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.011650485436893204 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003883495145631068 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 10.14 | | std | 5.33 | | cv | 0.526 | | sampleLengths | | 0 | 17 | | 1 | 17 | | 2 | 4 | | 3 | 15 | | 4 | 8 | | 5 | 14 | | 6 | 2 | | 7 | 8 | | 8 | 10 | | 9 | 7 | | 10 | 16 | | 11 | 15 | | 12 | 2 | | 13 | 6 | | 14 | 4 | | 15 | 11 | | 16 | 8 | | 17 | 14 | | 18 | 18 | | 19 | 14 | | 20 | 7 | | 21 | 3 | | 22 | 4 | | 23 | 16 | | 24 | 10 | | 25 | 8 | | 26 | 7 | | 27 | 3 | | 28 | 27 | | 29 | 3 | | 30 | 13 | | 31 | 4 | | 32 | 11 | | 33 | 12 | | 34 | 10 | | 35 | 11 | | 36 | 5 | | 37 | 6 | | 38 | 16 | | 39 | 19 | | 40 | 7 | | 41 | 14 | | 42 | 10 | | 43 | 5 | | 44 | 7 | | 45 | 2 | | 46 | 5 | | 47 | 10 | | 48 | 9 | | 49 | 19 |
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| 39.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.2636363636363636 | | totalSentences | 110 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 58.38% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 99 | | matches | | 0 | "She ignored the chill." | | 1 | "Her eyes stayed locked on" | | 2 | "He ducked under a red" | | 3 | "Her lungs burned, a familiar" | | 4 | "It kept the ghosts of" | | 5 | "She jerked her arm free," | | 6 | "He scrambled over a chain-link" | | 7 | "She hauled herself over, ignoring" | | 8 | "He hit the ground on" | | 9 | "It remained locked." | | 10 | "She scanned the perimeter." | | 11 | "She unscrewed the grate." | | 12 | "It felt thick, like walking" | | 13 | "She descended the rusted ladder." | | 14 | "She kept her hand on" | | 15 | "Her thumb brushed the safety." | | 16 | "She wasn't in a basement." | | 17 | "She stood on a ledge" | | 18 | "He stopped at a stall" | | 19 | "She looked at the exit." |
| | ratio | 0.404 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 99 | | matches | | 0 | "Rain lashed against the pavement," | | 1 | "Detective Harlow Quinn gripped the" | | 2 | "She ignored the chill." | | 3 | "Her eyes stayed locked on" | | 4 | "The suspect moved with a" | | 5 | "He ducked under a red" | | 6 | "Her lungs burned, a familiar" | | 7 | "It kept the ghosts of" | | 8 | "The man turned into a" | | 9 | "Quinn vaulted a stack of" | | 10 | "She jerked her arm free," | | 11 | "The shout died in the" | | 12 | "The suspect didn't slow." | | 13 | "He scrambled over a chain-link" | | 14 | "Quinn followed, her fingers catching" | | 15 | "She hauled herself over, ignoring" | | 16 | "He hit the ground on" | | 17 | "The heavy steel door groaned" | | 18 | "Quinn slammed her shoulder against" | | 19 | "It remained locked." |
| | ratio | 0.96 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 99 | | matches | | 0 | "If she went back, the" | | 1 | "If she went down, she" |
| | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 3 | | matches | | 0 | "The light danced over damp brickwork and thick, pulsing veins of ivy that shouldn't have survived the London frost." | | 1 | "The air grew warmer here, vibrating with a low-frequency hum that rattled Quinn's teeth." | | 2 | "A woman at a neighboring stall polished a collection of daggers that appeared to weep black liquid." |
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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 | |