| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "the radio called again [again]" |
| | dialogueSentences | 37 | | tagDensity | 0.189 | | leniency | 0.378 | | rawRatio | 0.143 | | effectiveRatio | 0.054 | |
| 95.04% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2016 | | 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) | |
| 80.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2016 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "effortless" | | 1 | "scanned" | | 2 | "glint" | | 3 | "electric" | | 4 | "velvet" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "lurched" |
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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 | 225 | | matches | | |
| 92.06% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 4 | | narrationSentences | 225 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 255 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2014 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1824 | | uniqueNames | 16 | | maxNameDensity | 2.08 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 38 | | Camden | 1 | | High | 1 | | Street | 1 | | Kentish | 1 | | Town | 1 | | Morris | 3 | | Holborn | 1 | | Tube | 1 | | London | 1 | | Underground | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 7 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Kentish" | | 4 | "Town" | | 5 | "London" |
| | globalScore | 0.458 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 132 | | 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 | 2014 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 255 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 141 | | mean | 14.28 | | std | 16.15 | | cv | 1.13 | | sampleLengths | | 0 | 12 | | 1 | 73 | | 2 | 11 | | 3 | 10 | | 4 | 70 | | 5 | 7 | | 6 | 4 | | 7 | 3 | | 8 | 58 | | 9 | 17 | | 10 | 2 | | 11 | 6 | | 12 | 2 | | 13 | 54 | | 14 | 11 | | 15 | 9 | | 16 | 9 | | 17 | 11 | | 18 | 8 | | 19 | 2 | | 20 | 40 | | 21 | 4 | | 22 | 5 | | 23 | 31 | | 24 | 9 | | 25 | 11 | | 26 | 6 | | 27 | 2 | | 28 | 17 | | 29 | 4 | | 30 | 4 | | 31 | 9 | | 32 | 20 | | 33 | 72 | | 34 | 20 | | 35 | 5 | | 36 | 14 | | 37 | 51 | | 38 | 9 | | 39 | 9 | | 40 | 9 | | 41 | 3 | | 42 | 13 | | 43 | 3 | | 44 | 10 | | 45 | 1 | | 46 | 42 | | 47 | 2 | | 48 | 10 | | 49 | 6 |
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| 97.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 225 | | matches | | 0 | "been found" | | 1 | "been swept" | | 2 | "was left" | | 3 | "been smashed" | | 4 | "was plastered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 309 | | matches | | 0 | "were moving" | | 1 | "were fleeing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 255 | | ratio | 0.008 | | matches | | 0 | "The sound was quiet—a dry click, like a fingernail against glass." | | 1 | "The suspect was ahead, visible in fragments—a black shoulder, a wet hood, the flash of the pale token." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1834 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 51 | | adverbRatio | 0.027808069792802616 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.0054525627044711015 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 255 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 255 | | mean | 7.9 | | std | 5.73 | | cv | 0.726 | | sampleLengths | | 0 | 12 | | 1 | 20 | | 2 | 4 | | 3 | 16 | | 4 | 33 | | 5 | 11 | | 6 | 2 | | 7 | 1 | | 8 | 1 | | 9 | 6 | | 10 | 31 | | 11 | 22 | | 12 | 17 | | 13 | 7 | | 14 | 4 | | 15 | 3 | | 16 | 4 | | 17 | 8 | | 18 | 19 | | 19 | 9 | | 20 | 5 | | 21 | 13 | | 22 | 6 | | 23 | 11 | | 24 | 2 | | 25 | 6 | | 26 | 2 | | 27 | 14 | | 28 | 17 | | 29 | 14 | | 30 | 3 | | 31 | 3 | | 32 | 3 | | 33 | 11 | | 34 | 9 | | 35 | 9 | | 36 | 11 | | 37 | 8 | | 38 | 2 | | 39 | 9 | | 40 | 13 | | 41 | 18 | | 42 | 4 | | 43 | 5 | | 44 | 8 | | 45 | 1 | | 46 | 1 | | 47 | 1 | | 48 | 2 | | 49 | 2 |
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| 47.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.33992094861660077 | | totalSentences | 253 | | uniqueOpeners | 86 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 11 | | totalSentences | 190 | | matches | | 0 | "Then he had run." | | 1 | "Too many windows." | | 2 | "Too many shadows." | | 3 | "Too much rain." | | 4 | "Then he struck the wall" | | 5 | "Somewhere beyond the wall, the" | | 6 | "Somewhere below, people were moving" | | 7 | "Then she holstered the pistol" | | 8 | "Then he stepped aside." | | 9 | "Somewhere in the dark, metal" | | 10 | "Then she looked at her" |
| | ratio | 0.058 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 190 | | matches | | 0 | "Her coat flapped open." | | 1 | "She had found three bodies" | | 2 | "He had looked at her" | | 3 | "He moved with an ugly," | | 4 | "He veered between a taxi" | | 5 | "Her left wrist struck the" | | 6 | "He was already halfway to" | | 7 | "Her breathing came hard, but" | | 8 | "She looked back toward the" | | 9 | "Her radio hissed at her" | | 10 | "She pressed the transmit button." | | 11 | "She had no intention of" | | 12 | "His body had never been" | | 13 | "He had mapped them." | | 14 | "He had carried two torches," | | 15 | "She had called him tired." | | 16 | "She touched the scarred leather" | | 17 | "It was the correct answer." | | 18 | "It was also not the" | | 19 | "She struck the wall with" |
| | ratio | 0.189 | |
| 70.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 148 | | totalSentences | 190 | | matches | | 0 | "Rain silvered the city and" | | 1 | "Detective Harlow Quinn ran beneath" | | 2 | "Her coat flapped open." | | 3 | "Quinn saw the object again" | | 4 | "She had found three bodies" | | 5 | "The victim tonight had been" | | 6 | "The suspect had been there" | | 7 | "He had looked at her" | | 8 | "The suspect glanced back." | | 9 | "Rain streamed off the hood," | | 10 | "He moved with an ugly," | | 11 | "He veered between a taxi" | | 12 | "The taxi braked, horn blaring." | | 13 | "Quinn planted one hand on" | | 14 | "Her left wrist struck the" | | 15 | "The worn leather watch beneath" | | 16 | "The suspect ducked into an" | | 17 | "The alley stank of beer," | | 18 | "He was already halfway to" | | 19 | "Quinn drew her service pistol," |
| | ratio | 0.779 | |
| 78.95% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 190 | | matches | | 0 | "Before he disappeared, he had" | | 1 | "Now her fingers pressed against" | | 2 | "Now the dark place had" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 4 | | matches | | 0 | "Ahead, the suspect cut through the traffic on Camden High Street, a narrow man in a black hooded jacket, one hand clamped around something that flashed bone-whi…" | | 1 | "Enchanted goods lay arranged on velvet: watches with hands moving backward, keys that twitched toward locked doors, black candles burning with blue flames." | | 2 | "His footsteps echoed, then multiplied, as if a dozen men were fleeing in different directions." | | 3 | "For three years, Morris had lived inside the blank spaces of her life: the empty chair at briefings, the untouched mug in the evidence room, the final call that…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 95.95% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn shouted (shout)" | | 1 | "she snarled (snarl)" |
| | dialogueSentences | 37 | | tagDensity | 0.189 | | leniency | 0.378 | | rawRatio | 0.286 | | effectiveRatio | 0.108 | |