| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1343 | | 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) | |
| 59.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1343 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "rhythmic" | | 3 | "scanned" | | 4 | "gloom" | | 5 | "pulse" | | 6 | "weight" | | 7 | "silence" | | 8 | "flickered" | | 9 | "dancing" | | 10 | "perfect" |
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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 | 123 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 123 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 9 | | totalWords | 1342 | | ratio | 0.007 | | matches | | 0 | "Turn back, Detective Quinn. Some doors shouldn't be opened." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1206 | | uniqueNames | 12 | | maxNameDensity | 0.58 | | worstName | "Jacket" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Jacket" | | discoveredNames | | Soho | 1 | | London | 1 | | Jacket | 7 | | Morris | 1 | | Harlow | 6 | | Detective | 1 | | Quinn | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Grey | 7 | | Cultured | 1 |
| | persons | | 0 | "Jacket" | | 1 | "Morris" | | 2 | "Harlow" | | 3 | "Detective" | | 4 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Market" | | 3 | "Grey" |
| | globalScore | 1 | | windowScore | 1 | |
| 89.76% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like" | | 1 | "books that seemed to be breathing" |
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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 | 1342 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 27.96 | | std | 21.5 | | cv | 0.769 | | sampleLengths | | 0 | 36 | | 1 | 3 | | 2 | 55 | | 3 | 39 | | 4 | 4 | | 5 | 53 | | 6 | 5 | | 7 | 7 | | 8 | 34 | | 9 | 47 | | 10 | 2 | | 11 | 32 | | 12 | 9 | | 13 | 40 | | 14 | 68 | | 15 | 30 | | 16 | 4 | | 17 | 18 | | 18 | 9 | | 19 | 22 | | 20 | 58 | | 21 | 27 | | 22 | 5 | | 23 | 43 | | 24 | 73 | | 25 | 35 | | 26 | 13 | | 27 | 31 | | 28 | 83 | | 29 | 42 | | 30 | 44 | | 31 | 51 | | 32 | 8 | | 33 | 45 | | 34 | 19 | | 35 | 6 | | 36 | 8 | | 37 | 2 | | 38 | 47 | | 39 | 8 | | 40 | 38 | | 41 | 31 | | 42 | 62 | | 43 | 14 | | 44 | 15 | | 45 | 4 | | 46 | 9 | | 47 | 4 |
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| 93.85% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 123 | | matches | | 0 | "been pulled" | | 1 | "been transformed" | | 2 | "got passed" | | 3 | "was fixed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 200 | | matches | | 0 | "was slowing" | | 1 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1207 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.03148301574150787 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.010770505385252692 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 10.01 | | std | 6.47 | | cv | 0.646 | | sampleLengths | | 0 | 12 | | 1 | 24 | | 2 | 3 | | 3 | 10 | | 4 | 8 | | 5 | 17 | | 6 | 20 | | 7 | 19 | | 8 | 8 | | 9 | 12 | | 10 | 4 | | 11 | 8 | | 12 | 2 | | 13 | 1 | | 14 | 26 | | 15 | 3 | | 16 | 13 | | 17 | 5 | | 18 | 7 | | 19 | 6 | | 20 | 8 | | 21 | 2 | | 22 | 2 | | 23 | 16 | | 24 | 9 | | 25 | 14 | | 26 | 15 | | 27 | 9 | | 28 | 2 | | 29 | 9 | | 30 | 17 | | 31 | 3 | | 32 | 3 | | 33 | 6 | | 34 | 3 | | 35 | 5 | | 36 | 6 | | 37 | 14 | | 38 | 15 | | 39 | 17 | | 40 | 26 | | 41 | 2 | | 42 | 4 | | 43 | 14 | | 44 | 5 | | 45 | 8 | | 46 | 6 | | 47 | 4 | | 48 | 12 | | 49 | 4 |
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| 60.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.40298507462686567 | | totalSentences | 134 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 111 | | matches | | 0 | "All dead now." | | 1 | "All killed with the same" | | 2 | "Then she tucked the phone" | | 3 | "Then he turned and walked" |
| | ratio | 0.036 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 111 | | matches | | 0 | "She kept running." | | 1 | "His footsteps echoed off the" | | 2 | "He knew these streets better" | | 3 | "Her quarry ducked left at" | | 4 | "She followed, boots slapping against" | | 5 | "She pushed harder, closing the" | | 6 | "He disappeared around a corner." | | 7 | "She approached it slowly, hand" | | 8 | "She unclipped her torch, shone" | | 9 | "Her mobile buzzed in her" | | 10 | "She ignored it." | | 11 | "She knew a trap when" | | 12 | "She'd found him three hours" | | 13 | "She crouched by the opening," | | 14 | "Her mobile buzzed again." | | 15 | "She pulled it out, glanced" | | 16 | "She descended slowly, torch clamped" | | 17 | "Her boots touched solid ground." | | 18 | "She was standing on a" | | 19 | "She saw a woman with" |
| | ratio | 0.333 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 111 | | matches | | 0 | "The man in the grey" | | 1 | "Harlow's shoulder caught the edge" | | 2 | "She kept running." | | 3 | "His footsteps echoed off the" | | 4 | "He knew these streets better" | | 5 | "The rain came harder now," | | 6 | "Her quarry ducked left at" | | 7 | "She followed, boots slapping against" | | 8 | "She pushed harder, closing the" | | 9 | "He disappeared around a corner." | | 10 | "Harlow stopped, scanned the narrow" | | 11 | "Brick walls on both sides," | | 12 | "A dead end, except for" | | 13 | "She approached it slowly, hand" | | 14 | "The grate had been pulled" | | 15 | "Something that prickled at the" | | 16 | "She unclipped her torch, shone" | | 17 | "The ladder rungs disappeared into" | | 18 | "A platform, maybe." | | 19 | "Her mobile buzzed in her" |
| | ratio | 0.712 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 111 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 4 | | matches | | 0 | "Harlow's shoulder caught the edge of it, spun her sideways, sent her stumbling into a puddle that splashed filthy water up to her knees." | | 1 | "The grate had been pulled aside, revealing a ladder that descended into absolute darkness." | | 2 | "The proprietor was an ancient woman with clouded eyes who sat motionless while her customers examined her wares." | | 3 | "At the creatures and the stalls and the impossible things that had no business existing in a rational world." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 87.50% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "Grey Jacket continued (continue)" |
| | dialogueSentences | 16 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0.5 | | effectiveRatio | 0.125 | |