| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.163 | | leniency | 0.327 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1035 | | 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) | |
| 66.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1035 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "warmth" | | 1 | "efficient" | | 2 | "weight" | | 3 | "silence" | | 4 | "unreadable" | | 5 | "jaw clenched" | | 6 | "reminder" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 75 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | 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 | 5 | | markdownWords | 6 | | totalWords | 1027 | | ratio | 0.006 | | matches | | 0 | "we" | | 1 | "Evan Whitaker" | | 2 | "vanished" | | 3 | "trusted" | | 4 | "trusted" |
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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 | 13 | | wordCount | 706 | | uniqueNames | 9 | | maxNameDensity | 0.71 | | worstName | "Aurora" | | maxWindowNameDensity | 1 | | worstWindowName | "Aurora" | | discoveredNames | | Golden | 1 | | Empress | 1 | | Earl | 1 | | Grey | 1 | | Ptolemy | 1 | | Ghosted | 1 | | London | 1 | | Cardiff | 1 | | Aurora | 5 |
| | persons | | 0 | "Earl" | | 1 | "Grey" | | 2 | "Ptolemy" | | 3 | "Aurora" |
| | places | | 0 | "Golden" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 55.66% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 2 | | matches | | 0 | "Not quite a smile, but close" | | 1 | "looked like they might avalanche at a lou" |
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| 5.26% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.947 | | wordCount | 1027 | | matches | | 0 | "not in surprise, but calculation" | | 1 | "Not quite a smile, but close" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 18.67 | | std | 13.33 | | cv | 0.714 | | sampleLengths | | 0 | 11 | | 1 | 56 | | 2 | 14 | | 3 | 16 | | 4 | 23 | | 5 | 19 | | 6 | 50 | | 7 | 47 | | 8 | 17 | | 9 | 22 | | 10 | 24 | | 11 | 35 | | 12 | 25 | | 13 | 16 | | 14 | 5 | | 15 | 19 | | 16 | 40 | | 17 | 35 | | 18 | 13 | | 19 | 1 | | 20 | 2 | | 21 | 11 | | 22 | 20 | | 23 | 23 | | 24 | 34 | | 25 | 6 | | 26 | 9 | | 27 | 24 | | 28 | 6 | | 29 | 3 | | 30 | 3 | | 31 | 7 | | 32 | 17 | | 33 | 13 | | 34 | 41 | | 35 | 8 | | 36 | 9 | | 37 | 11 | | 38 | 5 | | 39 | 2 | | 40 | 28 | | 41 | 5 | | 42 | 6 | | 43 | 26 | | 44 | 4 | | 45 | 32 | | 46 | 4 | | 47 | 25 | | 48 | 37 | | 49 | 30 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 75 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 131 | | matches | (empty) | |
| 18.63% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 115 | | ratio | 0.043 | | matches | | 0 | "Her bright blue eyes narrowed, not in surprise, but calculation—like she was already deciding whether to slam the door in my face or hear me out." | | 1 | "The deadbolts had already been thrown back when I arrived—three of them, just like the codex said." | | 2 | "A tabby cat—presumably Ptolemy—watched me from the back of the sofa, tail flicking like a metronome set to distrust." | | 3 | "The way she said my name—like it was something bitter on her tongue—made my jaw tighten." | | 4 | "I was halfway down the stairs when I heard it—the sound of something heavy hitting the wall." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 714 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.036414565826330535 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0056022408963585435 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 8.93 | | std | 5.8 | | cv | 0.649 | | sampleLengths | | 0 | 11 | | 1 | 18 | | 2 | 12 | | 3 | 26 | | 4 | 14 | | 5 | 12 | | 6 | 4 | | 7 | 3 | | 8 | 9 | | 9 | 11 | | 10 | 3 | | 11 | 4 | | 12 | 12 | | 13 | 7 | | 14 | 6 | | 15 | 7 | | 16 | 17 | | 17 | 13 | | 18 | 9 | | 19 | 19 | | 20 | 19 | | 21 | 9 | | 22 | 8 | | 23 | 17 | | 24 | 5 | | 25 | 14 | | 26 | 10 | | 27 | 1 | | 28 | 5 | | 29 | 20 | | 30 | 9 | | 31 | 11 | | 32 | 6 | | 33 | 8 | | 34 | 10 | | 35 | 6 | | 36 | 5 | | 37 | 10 | | 38 | 9 | | 39 | 16 | | 40 | 6 | | 41 | 15 | | 42 | 3 | | 43 | 9 | | 44 | 19 | | 45 | 7 | | 46 | 4 | | 47 | 7 | | 48 | 2 | | 49 | 1 |
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| 55.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3652173913043478 | | totalSentences | 115 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 72 | | matches | | 0 | "Then, slowly, she reached out" | | 1 | "Just stared at the wall," | | 2 | "Then, softer, the unmistakable sound" |
| | ratio | 0.042 | |
| 25.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 72 | | matches | | 0 | "Her bright blue eyes narrowed," | | 1 | "My name came out flat," | | 2 | "I adjusted my grip on" | | 3 | "She didn’t move." | | 4 | "I inclined my head." | | 5 | "She stepped aside, the movement" | | 6 | "I set my cane against" | | 7 | "She pulled out two containers," | | 8 | "I’d left London abruptly six" | | 9 | "I took the chair opposite" | | 10 | "She peeled the lid off" | | 11 | "She snorted, but there was" | | 12 | "I’d missed that, in a" | | 13 | "I leaned forward, resting my" | | 14 | "She set the dumpling down." | | 15 | "She straightened, crossing her arms" | | 16 | "I reached into my jacket" | | 17 | "She didn’t touch it." | | 18 | "She laughed, but there was" | | 19 | "I picked up the paper," |
| | ratio | 0.486 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 72 | | matches | | 0 | "The door swung open before" | | 1 | "Aurora stood there, a takeaway" | | 2 | "The scent of soy sauce" | | 3 | "Her bright blue eyes narrowed," | | 4 | "My name came out flat," | | 5 | "I adjusted my grip on" | | 6 | "She didn’t move." | | 7 | "The keys jingled as her" | | 8 | "A fair question." | | 9 | "I inclined my head." | | 10 | "That got a twitch of" | | 11 | "She stepped aside, the movement" | | 12 | "The deadbolts had already been" | | 13 | "The flat smelled of old" | | 14 | "Books teetered on every surface," | | 15 | "A tabby cat—presumably Ptolemy—watched me" | | 16 | "Aurora dumped the takeaway bag" | | 17 | "I set my cane against" | | 18 | "She pulled out two containers," | | 19 | "A crude word, but accurate." |
| | ratio | 0.889 | |
| 69.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 72 | | matches | | 0 | "Either she’d been expecting someone," |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "She pulled out, not bothering to offer me one" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 49 | | tagDensity | 0.02 | | leniency | 0.041 | | rawRatio | 1 | | effectiveRatio | 0.041 | |