| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "She walked back [back]" |
| | dialogueSentences | 48 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.125 | | effectiveRatio | 0.042 | |
| 72.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1269 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "slightly" | | 1 | "lazily" | | 2 | "sharply" | | 3 | "lightly" | | 4 | "very" |
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| 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) | |
| 68.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1269 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "gloom" | | 1 | "flicker" | | 2 | "familiar" | | 3 | "glinting" | | 4 | "etched" | | 5 | "standard" | | 6 | "warmth" | | 7 | "vibrated" |
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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 | 114 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1262 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 914 | | uniqueNames | 4 | | maxNameDensity | 2.08 | | worstName | "Carter" | | maxWindowNameDensity | 4 | | worstWindowName | "Carter" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 12 | | Carter | 19 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Carter" |
| | places | (empty) | | globalScore | 0.461 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1262 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 20.35 | | std | 15.68 | | cv | 0.77 | | sampleLengths | | 0 | 65 | | 1 | 38 | | 2 | 19 | | 3 | 52 | | 4 | 39 | | 5 | 5 | | 6 | 33 | | 7 | 32 | | 8 | 27 | | 9 | 3 | | 10 | 29 | | 11 | 3 | | 12 | 12 | | 13 | 17 | | 14 | 43 | | 15 | 9 | | 16 | 24 | | 17 | 14 | | 18 | 40 | | 19 | 62 | | 20 | 9 | | 21 | 3 | | 22 | 3 | | 23 | 31 | | 24 | 5 | | 25 | 8 | | 26 | 14 | | 27 | 18 | | 28 | 38 | | 29 | 45 | | 30 | 11 | | 31 | 12 | | 32 | 6 | | 33 | 51 | | 34 | 10 | | 35 | 37 | | 36 | 14 | | 37 | 31 | | 38 | 7 | | 39 | 3 | | 40 | 13 | | 41 | 20 | | 42 | 17 | | 43 | 8 | | 44 | 13 | | 45 | 23 | | 46 | 31 | | 47 | 5 | | 48 | 2 | | 49 | 25 |
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| 96.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 114 | | matches | | 0 | "was frozen" | | 1 | "were scuffed" | | 2 | "were lined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 169 | | matches | | |
| 86.47% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 152 | | ratio | 0.02 | | matches | | 0 | "The air in the abandoned Tube tunnel reeked of damp concrete and something sharper—copper, maybe, or the acrid tang of old batteries." | | 1 | "The victim’s clothes were expensive—tailored wool coat, polished leather shoes—but the fabric was damp, clinging to his skin." | | 2 | "The beam caught on something—a small, curved bone, hanging from a rusted hook." |
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| 92.86% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 920 | | adjectiveStacks | 1 | | stackExamples | | 0 | "illuminating graffiti-scarred walls" |
| | adverbCount | 38 | | adverbRatio | 0.041304347826086954 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.015217391304347827 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 8.3 | | std | 6.02 | | cv | 0.725 | | sampleLengths | | 0 | 22 | | 1 | 17 | | 2 | 26 | | 3 | 14 | | 4 | 24 | | 5 | 16 | | 6 | 3 | | 7 | 4 | | 8 | 31 | | 9 | 17 | | 10 | 7 | | 11 | 18 | | 12 | 10 | | 13 | 2 | | 14 | 2 | | 15 | 5 | | 16 | 15 | | 17 | 18 | | 18 | 10 | | 19 | 5 | | 20 | 10 | | 21 | 7 | | 22 | 10 | | 23 | 13 | | 24 | 4 | | 25 | 2 | | 26 | 1 | | 27 | 14 | | 28 | 10 | | 29 | 5 | | 30 | 3 | | 31 | 8 | | 32 | 4 | | 33 | 3 | | 34 | 14 | | 35 | 7 | | 36 | 16 | | 37 | 11 | | 38 | 4 | | 39 | 3 | | 40 | 2 | | 41 | 7 | | 42 | 2 | | 43 | 5 | | 44 | 19 | | 45 | 2 | | 46 | 3 | | 47 | 9 | | 48 | 6 | | 49 | 34 |
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| 43.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.27631578947368424 | | totalSentences | 152 | | uniqueOpeners | 42 | |
| 96.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 104 | | matches | | 0 | "Instead, she walked to the" | | 1 | "Just a blank stretch of" | | 2 | "Just a clean, unbroken mark." |
| | ratio | 0.029 | |
| 93.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 104 | | matches | | 0 | "She adjusted the worn leather" | | 1 | "He gestured to the body" | | 2 | "She reached out, fingers hovering" | | 3 | "She shifted, angling the beam." | | 4 | "She picked it up, turning" | | 5 | "She tapped the glass face" | | 6 | "She slipped it into her" | | 7 | "She turned to one of" | | 8 | "She did know." | | 9 | "She didn’t answer." | | 10 | "She ran her fingers along" | | 11 | "She ignored him, pressing her" | | 12 | "She pulled her hand back." | | 13 | "He stepped forward, pressing his" | | 14 | "She knew what she’d felt." | | 15 | "She also knew Carter wouldn’t" | | 16 | "She turned back to the" | | 17 | "She crouched again, this time" | | 18 | "She stood, shaking her head." | | 19 | "She pointed to the victim’s" |
| | ratio | 0.317 | |
| 8.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 104 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The beam of her torch" | | 3 | "The voice came from deeper" | | 4 | "DS Carter stood near a" | | 5 | "She adjusted the worn leather" | | 6 | "Carter didn’t look up." | | 7 | "He gestured to the body" | | 8 | "The man’s face was frozen" | | 9 | "Quinn crouched, her knees protesting" | | 10 | "The victim’s clothes were expensive—tailored" | | 11 | "She reached out, fingers hovering" | | 12 | "Carter finally met her gaze," | | 13 | "Quinn’s torchlight caught something glinting" | | 14 | "She shifted, angling the beam." | | 15 | "A small brass compass, its" | | 16 | "The needle spun lazily, not" | | 17 | "She picked it up, turning" | | 18 | "The casing had a patina" | | 19 | "She tapped the glass face" |
| | ratio | 0.904 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 104 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.021 | | leniency | 0.042 | | rawRatio | 0 | | effectiveRatio | 0 | |