| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1129 | | 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) | |
| 69.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1129 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "stark" | | 1 | "stomach" | | 2 | "weight" | | 3 | "flicked" | | 4 | "traced" | | 5 | "could feel" | | 6 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 99.75% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 33 | | matches | | |
| 99.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 33 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 39 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 86 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1129 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 809 | | uniqueNames | 10 | | maxNameDensity | 0.62 | | worstName | "Eva" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 5 | | Moreau | 1 | | Lucien | 3 | | Cardiff | 1 | | Evan | 2 | | Golden | 1 | | Empress | 1 | | Ptolemy | 2 | | London | 1 | | Bengali | 1 |
| | persons | | 0 | "Eva" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Evan" | | 4 | "Ptolemy" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Bengali" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 33 | | 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 | 1129 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 39 | | matches | (empty) | |
| 58.94% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 94.08 | | std | 33.52 | | cv | 0.356 | | sampleLengths | | 0 | 53 | | 1 | 6 | | 2 | 98 | | 3 | 100 | | 4 | 101 | | 5 | 111 | | 6 | 136 | | 7 | 105 | | 8 | 128 | | 9 | 112 | | 10 | 101 | | 11 | 78 |
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| 52.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 33 | | matches | | 0 | "been sewn" | | 1 | "being asked" | | 2 | "was scared" | | 3 | "been gone" | | 4 | "been broken" |
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| 98.99% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 132 | | matches | | 0 | "was waiting" | | 1 | "were finally going" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 571 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.02276707530647986 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0070052539404553416 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 39 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 39 | | mean | 28.95 | | std | 19.14 | | cv | 0.661 | | sampleLengths | | 0 | 34 | | 1 | 19 | | 2 | 6 | | 3 | 24 | | 4 | 26 | | 5 | 48 | | 6 | 6 | | 7 | 21 | | 8 | 26 | | 9 | 47 | | 10 | 6 | | 11 | 10 | | 12 | 50 | | 13 | 22 | | 14 | 13 | | 15 | 40 | | 16 | 8 | | 17 | 27 | | 18 | 36 | | 19 | 28 | | 20 | 80 | | 21 | 28 | | 22 | 76 | | 23 | 29 | | 24 | 16 | | 25 | 4 | | 26 | 86 | | 27 | 22 | | 28 | 30 | | 29 | 40 | | 30 | 20 | | 31 | 22 | | 32 | 26 | | 33 | 34 | | 34 | 17 | | 35 | 24 | | 36 | 13 | | 37 | 18 | | 38 | 47 |
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| 68.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.48717948717948717 | | totalSentences | 39 | | uniqueOpeners | 19 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 33 | | matches | | 0 | "Then the crash came from" |
| | ratio | 0.03 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 33 | | matches | | 0 | "It wasn’t Eva on the" | | 1 | "His charcoal tailored suit fit" | | 2 | "I slammed the door half" | | 3 | "His cane slid into the" | | 4 | "I shoved my weight against" | | 5 | "He stepped over the threshold," | | 6 | "He shut the door behind" | | 7 | "I crossed my arms over" | | 8 | "His eyes flicked to the" | | 9 | "His voice was lower, rough" | | 10 | "I kicked a stray post-it" | | 11 | "He leaned his cane against" | | 12 | "I stepped closer, my bright" | | 13 | "He reached out, his hand" | | 14 | "I remembered the way he’d" | | 15 | "I stepped closer, the space" | | 16 | "He brushed a strand of" | | 17 | "He twisted the ivory handle," | | 18 | "He stepped in front of" |
| | ratio | 0.576 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 30 | | totalSentences | 33 | | matches | | 0 | "The third deadbolt grated open" | | 1 | "The words I’d rehearsed to" | | 2 | "It wasn’t Eva on the" | | 3 | "Lucien Moreau stood there, his" | | 4 | "His charcoal tailored suit fit" | | 5 | "I slammed the door half" | | 6 | "His cane slid into the" | | 7 | "Ptolemy, Eva’s tabby, wound around" | | 8 | "The scent of chicken tikka" | | 9 | "I shoved my weight against" | | 10 | "He stepped over the threshold," | | 11 | "A leather bound scroll rolled" | | 12 | "He shut the door behind" | | 13 | "That small, stupid detail made" | | 14 | "I crossed my arms over" | | 15 | "His eyes flicked to the" | | 16 | "His voice was lower, rough" | | 17 | "I kicked a stray post-it" | | 18 | "Ptolemy trotted past, jumping onto" | | 19 | "He leaned his cane against" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 33 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 6 | | matches | | 0 | "Ptolemy, Eva’s tabby, wound around my ankles, his purr cutting out when he hissed at the threshold, sensing the wrongness of the half-demon on my doorstep." | | 1 | "The scent of chicken tikka masala from the curry house below drifted up the stairwell, sharp and warm, and it clashed with the cold ozone that clung to Lucien’s…" | | 2 | "He stepped over the threshold, and I stumbled back, my shoulder hitting the stack of grimoires that teetered by the coat rack." | | 3 | "Ptolemy trotted past, jumping onto the arm of the threadbare sofa, his tail flicking as he watched us, like he was waiting for a fight to break out." | | 4 | "The anger in my chest melted, just a little, leaving only the hurt and the raw attraction that had never gone away, not for a single day he’d been gone." | | 5 | "He stepped in front of me, his body blocking me from the door, and the black of his demon eye swallowed the amber, the full power of his half-demon heritage fla…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 1 | | effectiveRatio | 0.182 | |