| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.073 | | leniency | 0.145 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1277 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 72.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1277 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "traced" | | 1 | "etched" | | 2 | "flickered" | | 3 | "echoed" | | 4 | "silence" | | 5 | "trembled" |
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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 | 104 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 154 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1277 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 51.64% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 915 | | uniqueNames | 11 | | maxNameDensity | 1.97 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 18 | | Tube | 1 | | Camden | 2 | | Reeves | 8 | | Morris | 1 | | Jameson | 1 | | Latin | 1 | | Greek | 1 | | Kowalski | 1 | | Silence | 1 | | Eva | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Reeves" | | 2 | "Morris" | | 3 | "Jameson" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | (empty) | | globalScore | 0.516 | | windowScore | 0.667 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 3 | | matches | | 0 | "patterns that seemed to shift when she looked at them from the corner of her eye" | | 1 | "something like this before" | | 2 | "forensics had apparently swept and found not" |
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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 | 1277 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 154 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 87 | | mean | 14.68 | | std | 15.78 | | cv | 1.075 | | sampleLengths | | 0 | 8 | | 1 | 42 | | 2 | 6 | | 3 | 29 | | 4 | 8 | | 5 | 2 | | 6 | 3 | | 7 | 24 | | 8 | 4 | | 9 | 50 | | 10 | 3 | | 11 | 1 | | 12 | 9 | | 13 | 48 | | 14 | 2 | | 15 | 8 | | 16 | 10 | | 17 | 5 | | 18 | 38 | | 19 | 1 | | 20 | 53 | | 21 | 4 | | 22 | 2 | | 23 | 2 | | 24 | 63 | | 25 | 6 | | 26 | 5 | | 27 | 1 | | 28 | 6 | | 29 | 39 | | 30 | 9 | | 31 | 29 | | 32 | 10 | | 33 | 41 | | 34 | 18 | | 35 | 4 | | 36 | 3 | | 37 | 34 | | 38 | 5 | | 39 | 28 | | 40 | 4 | | 41 | 2 | | 42 | 1 | | 43 | 31 | | 44 | 1 | | 45 | 5 | | 46 | 27 | | 47 | 36 | | 48 | 5 | | 49 | 1 |
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| 88.39% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 104 | | matches | | 0 | "was torn" | | 1 | "been drawn" | | 2 | "been forbidden" | | 3 | "was clenched" | | 4 | "was etched" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 156 | | matches | | 0 | "was spinning" | | 1 | "was searching" | | 2 | "was speaking" | | 3 | "was spinning" | | 4 | "was coming" | | 5 | "was pointing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 154 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 919 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.029379760609358 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004352557127312296 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 154 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 154 | | mean | 8.29 | | std | 7.27 | | cv | 0.876 | | sampleLengths | | 0 | 8 | | 1 | 20 | | 2 | 16 | | 3 | 1 | | 4 | 5 | | 5 | 6 | | 6 | 21 | | 7 | 5 | | 8 | 3 | | 9 | 8 | | 10 | 2 | | 11 | 3 | | 12 | 15 | | 13 | 9 | | 14 | 4 | | 15 | 25 | | 16 | 9 | | 17 | 5 | | 18 | 3 | | 19 | 8 | | 20 | 3 | | 21 | 1 | | 22 | 9 | | 23 | 10 | | 24 | 5 | | 25 | 2 | | 26 | 8 | | 27 | 23 | | 28 | 2 | | 29 | 3 | | 30 | 5 | | 31 | 10 | | 32 | 5 | | 33 | 7 | | 34 | 8 | | 35 | 4 | | 36 | 4 | | 37 | 10 | | 38 | 5 | | 39 | 1 | | 40 | 16 | | 41 | 12 | | 42 | 1 | | 43 | 1 | | 44 | 23 | | 45 | 4 | | 46 | 2 | | 47 | 2 | | 48 | 12 | | 49 | 4 |
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| 72.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.461038961038961 | | totalSentences | 154 | | uniqueOpeners | 71 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 88 | | matches | | 0 | "Of course it was Camden." | | 1 | "Probably had been." | | 2 | "Too precise for vandalism." | | 3 | "Just for a moment." | | 4 | "Then she turned back to" |
| | ratio | 0.057 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 88 | | matches | | 0 | "His suit looked slept in." | | 1 | "He gestured toward the body" | | 2 | "His eyes were still open." | | 3 | "His mouth too." | | 4 | "It was recognition." | | 5 | "His fingernails trimmed, no dirt," | | 6 | "She swept her torch across" | | 7 | "They'd been drawn in chalk" | | 8 | "His face told her he" | | 9 | "She turned back to the" | | 10 | "She'd never seen anything like" | | 11 | "She nodded toward the victim" | | 12 | "She'd met her once, at" | | 13 | "She'd never used it." | | 14 | "He looked up." | | 15 | "She held his gaze until" | | 16 | "It pointed into the tunnel," |
| | ratio | 0.193 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 88 | | matches | | 0 | "The blood on the wall" | | 1 | "Harlow Quinn ducked under the" | | 2 | "The abandoned Tube station stretched" | | 3 | "His suit looked slept in." | | 4 | "Reeves shrugged, a gesture that" | | 5 | "He gestured toward the body" | | 6 | "Quinn followed the light." | | 7 | "The victim lay crumpled against" | | 8 | "His eyes were still open." | | 9 | "His mouth too." | | 10 | "The expression frozen on his" | | 11 | "It was recognition." | | 12 | "Quinn crouched, her knees protesting" | | 13 | "The victim's hands were clean." | | 14 | "His fingernails trimmed, no dirt," | | 15 | "Reeves leaned in." | | 16 | "Quinn stood, her joints clicking" | | 17 | "She swept her torch across" | | 18 | "The tiles were intact." | | 19 | "They'd been drawn in chalk" |
| | ratio | 0.75 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 2 | | matches | | 0 | "DCI Reeves stood beneath the half-broken platform sign, his bulk casting a shadow that swallowed the feeble light from his torch." | | 1 | "The young woman had approached her afterward, curly red hair bouncing, round glasses catching the overhead lights, and had handed her a card with the quiet conf…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 55 | | tagDensity | 0.018 | | leniency | 0.036 | | rawRatio | 1 | | effectiveRatio | 0.036 | |