| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 52 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1764 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 85.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1764 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanned" | | 1 | "chill" | | 2 | "silk" | | 3 | "etched" | | 4 | "quivered" |
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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 | 155 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 155 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 194 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 14 | | totalWords | 1764 | | ratio | 0.008 | | matches | | 0 | "peripheral necrosis, cause undetermined" | | 1 | "J.A.W." | | 2 | "KOWALSKI, E. Research Assistant." | | 3 | "She knows where it goes." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 1400 | | uniqueNames | 9 | | maxNameDensity | 0.71 | | worstName | "Pryce" | | maxWindowNameDensity | 2 | | worstWindowName | "Pryce" | | discoveredNames | | Northern | 1 | | Tube | 1 | | Kentish | 1 | | Town | 1 | | Ben | 1 | | Pryce | 10 | | Saracens | 1 | | Quinn | 7 | | Deptford | 1 |
| | persons | | | places | | 0 | "Kentish" | | 1 | "Town" | | 2 | "Deptford" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 95 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a spill from a teacup" |
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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 | 1764 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 194 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 87 | | mean | 20.28 | | std | 20.7 | | cv | 1.021 | | sampleLengths | | 0 | 11 | | 1 | 4 | | 2 | 5 | | 3 | 75 | | 4 | 6 | | 5 | 67 | | 6 | 10 | | 7 | 40 | | 8 | 13 | | 9 | 6 | | 10 | 18 | | 11 | 5 | | 12 | 53 | | 13 | 17 | | 14 | 69 | | 15 | 9 | | 16 | 26 | | 17 | 5 | | 18 | 7 | | 19 | 6 | | 20 | 21 | | 21 | 22 | | 22 | 51 | | 23 | 4 | | 24 | 4 | | 25 | 25 | | 26 | 23 | | 27 | 2 | | 28 | 3 | | 29 | 30 | | 30 | 30 | | 31 | 14 | | 32 | 65 | | 33 | 4 | | 34 | 57 | | 35 | 1 | | 36 | 6 | | 37 | 2 | | 38 | 4 | | 39 | 20 | | 40 | 72 | | 41 | 12 | | 42 | 7 | | 43 | 1 | | 44 | 33 | | 45 | 5 | | 46 | 1 | | 47 | 40 | | 48 | 39 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 155 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 217 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 194 | | ratio | 0.005 | | matches | | 0 | "She knew that without thinking; she'd come down the spiral facing it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1405 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.024199288256227757 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0028469750889679717 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 194 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 194 | | mean | 9.09 | | std | 7.28 | | cv | 0.8 | | sampleLengths | | 0 | 11 | | 1 | 4 | | 2 | 5 | | 3 | 4 | | 4 | 19 | | 5 | 12 | | 6 | 19 | | 7 | 7 | | 8 | 2 | | 9 | 1 | | 10 | 11 | | 11 | 2 | | 12 | 3 | | 13 | 1 | | 14 | 13 | | 15 | 21 | | 16 | 13 | | 17 | 4 | | 18 | 16 | | 19 | 10 | | 20 | 18 | | 21 | 16 | | 22 | 6 | | 23 | 13 | | 24 | 6 | | 25 | 11 | | 26 | 7 | | 27 | 5 | | 28 | 10 | | 29 | 43 | | 30 | 3 | | 31 | 14 | | 32 | 1 | | 33 | 17 | | 34 | 14 | | 35 | 8 | | 36 | 21 | | 37 | 8 | | 38 | 9 | | 39 | 8 | | 40 | 3 | | 41 | 2 | | 42 | 13 | | 43 | 5 | | 44 | 7 | | 45 | 6 | | 46 | 4 | | 47 | 5 | | 48 | 12 | | 49 | 18 |
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| 75.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4896907216494845 | | totalSentences | 194 | | uniqueOpeners | 95 | |
| 54.20% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 123 | | matches | | 0 | "Pale, porous, the size of" | | 1 | "Then something on the other" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 123 | | matches | | 0 | "They swung a little whenever" | | 1 | "She counted them." | | 2 | "He brightened when he saw" | | 3 | "He toasted her with the" | | 4 | "He nodded at the corpse" | | 5 | "She stopped three metres short" | | 6 | "Her own boots had left" | | 7 | "She could read the man's" | | 8 | "She could read nothing around" | | 9 | "His mouth opened, closed again." | | 10 | "She crouched at the edge" | | 11 | "He wore a charcoal wool" | | 12 | "His shoes were oxblood brogues," | | 13 | "She pointed with her pen" | | 14 | "She almost smiled." | | 15 | "It made him a decent" | | 16 | "She pulled on gloves and" | | 17 | "His left hand had slipped" | | 18 | "Her hand stopped moving." | | 19 | "Her tone did that to" |
| | ratio | 0.293 | |
| 90.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 123 | | matches | | 0 | "Quinn ducked under the tape" | | 1 | "The constable stepped back." | | 2 | "The stairwell dropped away beneath" | | 3 | "Someone had strung work lights" | | 4 | "They swung a little whenever" | | 5 | "Something sweeter underneath, like fruit" | | 6 | "She counted them." | | 7 | "South Kentish Town had closed" | | 8 | "The platform still wore its" | | 9 | "A faded roundel hung above" | | 10 | "Somebody had chalked a grinning" | | 11 | "The body lay at the" | | 12 | "DS Ben Pryce stood over" | | 13 | "He brightened when he saw" | | 14 | "He toasted her with the" | | 15 | "He nodded at the corpse" | | 16 | "Quinn didn't answer." | | 17 | "She stopped three metres short" | | 18 | "Decades of it, fine and" | | 19 | "The SOCOs had laid their" |
| | ratio | 0.74 | |
| 40.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 123 | | matches | | 0 | "Now she walked the length" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 3 | | matches | | 0 | "So had Pryce's, and the engineer's, a clumsy scuffle of rubber soles that stopped short and turned back in a hurry." | | 1 | "He wore a charcoal wool overcoat, good quality, the kind that cost more than Pryce's suit." | | 2 | "A young woman with round glasses and a cloud of red curls, freckles across her nose, caught mid-laugh as if the photographer had said something daft." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 52 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |