| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1086 | | totalAiIsmAdverbs | 1 | | 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) | |
| 49.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1086 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "fluttered" | | 1 | "synthetic" | | 2 | "intricate" | | 3 | "shimmered" | | 4 | "etched" | | 5 | "stomach" | | 6 | "sense of" | | 7 | "chaotic" | | 8 | "traced" | | 9 | "flickered" |
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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 | 94 | | matches | (empty) | |
| 82.07% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1082 | | ratio | 0 | | matches | (empty) | |
| 89.29% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 1 | | matches | | 0 | "In the sudden, absolute darkness, Davies shouted her name, his voice swallowed by the crushing sound." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 936 | | uniqueNames | 14 | | maxNameDensity | 0.96 | | worstName | "Davies" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Davies" | | discoveredNames | | Northern | 1 | | Line | 1 | | Veil | 2 | | Market | 1 | | Sergeant | 1 | | Davies | 9 | | Fae | 1 | | Quinn | 4 | | Shade | 1 | | Compass | 1 | | Morris | 1 | | Eva | 1 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Sergeant" | | 1 | "Davies" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Museum" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 74.24% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like tourists who’d stumbled into" | | 1 | "note that seemed to vibrate up from the flagstones through the soles of her boots" |
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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 | 104 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 36.07 | | std | 21.66 | | cv | 0.6 | | sampleLengths | | 0 | 61 | | 1 | 56 | | 2 | 38 | | 3 | 4 | | 4 | 5 | | 5 | 35 | | 6 | 70 | | 7 | 30 | | 8 | 59 | | 9 | 31 | | 10 | 16 | | 11 | 45 | | 12 | 24 | | 13 | 39 | | 14 | 68 | | 15 | 14 | | 16 | 9 | | 17 | 74 | | 18 | 7 | | 19 | 15 | | 20 | 26 | | 21 | 74 | | 22 | 33 | | 23 | 37 | | 24 | 40 | | 25 | 17 | | 26 | 45 | | 27 | 46 | | 28 | 4 | | 29 | 60 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 94 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 144 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 104 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 592 | | adjectiveStacks | 1 | | stackExamples | | 0 | "long, grey-streaked beard" |
| | adverbCount | 22 | | adverbRatio | 0.037162162162162164 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.010135135135135136 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 10.4 | | std | 6.18 | | cv | 0.594 | | sampleLengths | | 0 | 19 | | 1 | 17 | | 2 | 25 | | 3 | 9 | | 4 | 19 | | 5 | 18 | | 6 | 10 | | 7 | 15 | | 8 | 9 | | 9 | 10 | | 10 | 4 | | 11 | 4 | | 12 | 5 | | 13 | 25 | | 14 | 10 | | 15 | 13 | | 16 | 17 | | 17 | 18 | | 18 | 19 | | 19 | 3 | | 20 | 7 | | 21 | 23 | | 22 | 10 | | 23 | 5 | | 24 | 9 | | 25 | 10 | | 26 | 4 | | 27 | 15 | | 28 | 4 | | 29 | 2 | | 30 | 9 | | 31 | 22 | | 32 | 2 | | 33 | 14 | | 34 | 19 | | 35 | 15 | | 36 | 9 | | 37 | 2 | | 38 | 11 | | 39 | 13 | | 40 | 3 | | 41 | 17 | | 42 | 5 | | 43 | 3 | | 44 | 6 | | 45 | 5 | | 46 | 7 | | 47 | 7 | | 48 | 12 | | 49 | 7 |
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| 45.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3557692307692308 | | totalSentences | 104 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 44.72% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 89 | | matches | | 0 | "They looked like tourists who’d" | | 1 | "He was a good cop," | | 2 | "He saw the world in" | | 3 | "He gestured with his chin" | | 4 | "He led her past stalls" | | 5 | "He was a man in" | | 6 | "He looked asleep." | | 7 | "She didn't touch anything yet." | | 8 | "Her gaze swept the scene," | | 9 | "His clothes were undisturbed." | | 10 | "It was too neat." | | 11 | "she said, her voice low" | | 12 | "She leaned closer, her sharp" | | 13 | "It shimmered under the magical" | | 14 | "She pulled a small evidence" | | 15 | "It felt weightless, almost insubstantial." | | 16 | "It wasn't plaster." | | 17 | "It was too fine, too" | | 18 | "It felt like cold smoke." | | 19 | "She stood and walked around" |
| | ratio | 0.438 | |
| 10.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 89 | | matches | | 0 | "The bone token grew cold" | | 1 | "The wrought iron gate, disguised" | | 2 | "The air that rushed out" | | 3 | "They looked like tourists who’d" | | 4 | "Detective Sergeant Davies met her" | | 5 | "He was a good cop," | | 6 | "He saw the world in" | | 7 | "This place bent both." | | 8 | "He gestured with his chin" | | 9 | "He led her past stalls" | | 10 | "The traders, a motley assortment" | | 11 | "The victim lay on the" | | 12 | "He was a man in" | | 13 | "He looked asleep." | | 14 | "Davies said, stating the obvious" | | 15 | "Quinn crouched, the worn leather" | | 16 | "She didn't touch anything yet." | | 17 | "Her gaze swept the scene," | | 18 | "The victim’s hands were clean," | | 19 | "His clothes were undisturbed." |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 73.17% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 4 | | matches | | 0 | "The air that rushed out to meet her smelled of ozone, damp earth, and something else, something sharp and metallic like blood on a coin." | | 1 | "Uniformed officers stood guard, their faces pale under the flickering, enchanted lanterns that provided the market’s only illumination." | | 2 | "A low hum started, a single resonant note that seemed to vibrate up from the flagstones through the soles of her boots." | | 3 | "The humming escalated into a deafening roar that shook the very foundations of the abandoned station." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 4 | | matches | | 0 | "she said, her voice low" | | 1 | "She leaned, her sharp jawline tight with concentration" | | 2 | "she said, her voice flat" | | 3 | "Davies took, his hand instinctively going to his sidearm" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |