| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1074 | | totalAiIsmAdverbs | 2 | | 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) | |
| 86.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1074 | | totalAiIsms | 3 | | found | | | highlights | | |
| 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 | 60 | | matches | (empty) | |
| 95.24% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 69 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1082 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 988 | | uniqueNames | 11 | | maxNameDensity | 0.71 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 7 | | Camden | 1 | | High | 1 | | Street | 1 | | Tube | 1 | | Deptford | 2 | | Transport | 1 | | London | 1 | | Morris | 3 | | Underground | 1 | | Shoreditch | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Underground" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Deptford" | | 4 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 1 | | matches | | 0 | "Cloaks that seemed to drink the lantern light rather than reflect it" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1082 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 69 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 30.06 | | std | 25.81 | | cv | 0.859 | | sampleLengths | | 0 | 50 | | 1 | 2 | | 2 | 6 | | 3 | 26 | | 4 | 17 | | 5 | 27 | | 6 | 57 | | 7 | 52 | | 8 | 7 | | 9 | 72 | | 10 | 10 | | 11 | 80 | | 12 | 6 | | 13 | 95 | | 14 | 39 | | 15 | 12 | | 16 | 1 | | 17 | 34 | | 18 | 74 | | 19 | 33 | | 20 | 7 | | 21 | 4 | | 22 | 56 | | 23 | 38 | | 24 | 3 | | 25 | 4 | | 26 | 5 | | 27 | 63 | | 28 | 15 | | 29 | 29 | | 30 | 46 | | 31 | 8 | | 32 | 3 | | 33 | 57 | | 34 | 23 | | 35 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 60 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 152 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 69 | | ratio | 0.058 | | matches | | 0 | "She glanced at the handset — full bars, full battery — and shoved it back in her coat, cursing under her breath." | | 1 | "She still didn't know what had taken him — the report said gas leak, structural collapse, case closed — but she'd seen the marks on the walls afterward, wide gouges too deliberate to be rubble, and she'd never stopped hearing his voice on the radio, cutting out mid-sentence." | | 2 | "Her suspect — grey coat, blood on his collar from a fight she hadn't witnessed — was forty feet ahead, showing a token to a hooded figure at a rope barrier." | | 3 | "Whatever her suspect had done — and she'd chased him three blocks after a warehouse fire that left two bodies without a mark of flame on them — the answer was down there, walking away from her with every second she stood frozen on the stairs." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 981 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.0326197757390418 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007135575942915392 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 69 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 69 | | mean | 15.68 | | std | 12.41 | | cv | 0.791 | | sampleLengths | | 0 | 23 | | 1 | 27 | | 2 | 2 | | 3 | 3 | | 4 | 3 | | 5 | 16 | | 6 | 10 | | 7 | 17 | | 8 | 3 | | 9 | 2 | | 10 | 22 | | 11 | 31 | | 12 | 26 | | 13 | 27 | | 14 | 25 | | 15 | 7 | | 16 | 5 | | 17 | 19 | | 18 | 48 | | 19 | 10 | | 20 | 40 | | 21 | 15 | | 22 | 3 | | 23 | 22 | | 24 | 6 | | 25 | 29 | | 26 | 32 | | 27 | 8 | | 28 | 12 | | 29 | 14 | | 30 | 31 | | 31 | 8 | | 32 | 5 | | 33 | 3 | | 34 | 4 | | 35 | 1 | | 36 | 19 | | 37 | 2 | | 38 | 13 | | 39 | 7 | | 40 | 21 | | 41 | 46 | | 42 | 4 | | 43 | 6 | | 44 | 23 | | 45 | 7 | | 46 | 4 | | 47 | 24 | | 48 | 32 | | 49 | 19 |
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| 84.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5507246376811594 | | totalSentences | 69 | | uniqueOpeners | 38 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 55 | | matches | | 0 | "Just the tunnel, the dripping" |
| | ratio | 0.018 | |
| 89.09% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 55 | | matches | | 0 | "He didn't stop." | | 1 | "She keyed her radio anyway," | | 2 | "She glanced at the handset" | | 3 | "Her torch beam caught the" | | 4 | "She hesitated at the threshold," | | 5 | "She still didn't know what" | | 6 | "She ducked through the gap" | | 7 | "They didn't end in a" | | 8 | "Her suspect — grey coat," | | 9 | "She hadn't expected one." | | 10 | "She turned back to the" | | 11 | "She thought of Morris." | | 12 | "She went down anyway." | | 13 | "It had no face she" | | 14 | "She thought about Morris again," | | 15 | "She backed up the stairs" | | 16 | "She stood on the wet" | | 17 | "She pressed the button." |
| | ratio | 0.327 | |
| 69.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 55 | | matches | | 0 | "He didn't stop." | | 1 | "Nobody ever stopped." | | 2 | "Quinn's lungs burned, eighteen years" | | 3 | "She keyed her radio anyway," | | 4 | "Static answered her." | | 5 | "She glanced at the handset" | | 6 | "The alley narrowed and dropped," | | 7 | "Quinn followed, one hand on" | | 8 | "Her torch beam caught the" | | 9 | "She hesitated at the threshold," | | 10 | "Morris had hesitated too, once." | | 11 | "She still didn't know what" | | 12 | "She ducked through the gap" | | 13 | "The tunnel swallowed the last" | | 14 | "Water dripped somewhere ahead in" | | 15 | "Something older, strings and a" | | 16 | "They didn't end in a" | | 17 | "Quinn stopped at the lip" | | 18 | "Lanterns hung along the passage" | | 19 | "Jars of something that moved" |
| | ratio | 0.782 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 1 | | matches | | 0 | "Quinn stopped at the lip of the old platform, torch beam falling on descending stairs that had no business existing in any Transport for London blueprint she'd …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |