| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2175 | | 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) | |
| 77.01% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2175 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "predictable" | | 1 | "flickered" | | 2 | "silence" | | 3 | "charged" | | 4 | "pulse" | | 5 | "flicked" | | 6 | "chill" | | 7 | "traced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 169 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 169 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 258 | | 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 | 2174 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 73.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1301 | | uniqueNames | 7 | | maxNameDensity | 1.54 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Moreau | 1 | | Eva | 3 | | Ptolemy | 7 | | Brick | 2 | | Lane | 2 | | Lucien | 16 | | Rory | 20 |
| | persons | | 0 | "Moreau" | | 1 | "Eva" | | 2 | "Ptolemy" | | 3 | "Lucien" | | 4 | "Rory" |
| | places | | | globalScore | 0.731 | | windowScore | 0.833 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like admitting too much" | | 1 | "not quite touching" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.46 | | wordCount | 2174 | | matches | | 0 | "Not close enough to touch, but close enough" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 258 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 182 | | mean | 11.95 | | std | 8.96 | | cv | 0.751 | | sampleLengths | | 0 | 13 | | 1 | 34 | | 2 | 8 | | 3 | 1 | | 4 | 13 | | 5 | 2 | | 6 | 12 | | 7 | 6 | | 8 | 2 | | 9 | 7 | | 10 | 6 | | 11 | 10 | | 12 | 3 | | 13 | 6 | | 14 | 17 | | 15 | 12 | | 16 | 32 | | 17 | 7 | | 18 | 6 | | 19 | 9 | | 20 | 8 | | 21 | 5 | | 22 | 5 | | 23 | 16 | | 24 | 8 | | 25 | 16 | | 26 | 29 | | 27 | 7 | | 28 | 18 | | 29 | 8 | | 30 | 10 | | 31 | 26 | | 32 | 19 | | 33 | 4 | | 34 | 2 | | 35 | 23 | | 36 | 10 | | 37 | 13 | | 38 | 19 | | 39 | 19 | | 40 | 12 | | 41 | 3 | | 42 | 5 | | 43 | 1 | | 44 | 19 | | 45 | 14 | | 46 | 15 | | 47 | 6 | | 48 | 1 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 169 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 250 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 258 | | ratio | 0.004 | | matches | | 0 | "His eyes—one the colour of aged whisky, the other void-black—met hers." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1307 | | adjectiveStacks | 1 | | stackExamples | | 0 | "other void-black-met hers." |
| | adverbCount | 41 | | adverbRatio | 0.03136954858454476 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0038255547054322878 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 258 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 258 | | mean | 8.43 | | std | 6.38 | | cv | 0.757 | | sampleLengths | | 0 | 8 | | 1 | 5 | | 2 | 15 | | 3 | 8 | | 4 | 11 | | 5 | 8 | | 6 | 1 | | 7 | 4 | | 8 | 9 | | 9 | 2 | | 10 | 12 | | 11 | 6 | | 12 | 2 | | 13 | 7 | | 14 | 6 | | 15 | 10 | | 16 | 3 | | 17 | 6 | | 18 | 5 | | 19 | 4 | | 20 | 8 | | 21 | 4 | | 22 | 3 | | 23 | 5 | | 24 | 15 | | 25 | 4 | | 26 | 5 | | 27 | 8 | | 28 | 7 | | 29 | 6 | | 30 | 9 | | 31 | 8 | | 32 | 5 | | 33 | 5 | | 34 | 3 | | 35 | 13 | | 36 | 8 | | 37 | 5 | | 38 | 11 | | 39 | 29 | | 40 | 2 | | 41 | 5 | | 42 | 18 | | 43 | 8 | | 44 | 10 | | 45 | 5 | | 46 | 21 | | 47 | 5 | | 48 | 8 | | 49 | 6 |
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| 44.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.2713178294573643 | | totalSentences | 258 | | uniqueOpeners | 70 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 156 | | matches | (empty) | | ratio | 0 | |
| 53.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 65 | | totalSentences | 156 | | matches | | 0 | "His eyes—one the colour of" | | 1 | "She froze, one hand still" | | 2 | "He tilted his head." | | 3 | "She stepped aside anyway." | | 4 | "He crossed the threshold." | | 5 | "He set the cane against" | | 6 | "His jaw tightened." | | 7 | "He moved deeper into the" | | 8 | "She scooped Ptolemy up, the" | | 9 | "She set the cat down." | | 10 | "She rubbed it with her" | | 11 | "She yanked open a cupboard," | | 12 | "She needed something to do" | | 13 | "His mouth quirked." | | 14 | "She slammed it onto the" | | 15 | "He reached past her, turned" | | 16 | "His sleeve brushed her arm." | | 17 | "She didn't pull away." | | 18 | "She looked up." | | 19 | "His heterochromatic eyes held hers." |
| | ratio | 0.417 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 144 | | totalSentences | 156 | | matches | | 0 | "The third lock gave with" | | 1 | "Rory swung the door wide." | | 2 | "Lucien Moreau stood on the" | | 3 | "Platinum blond hair gleamed under" | | 4 | "His eyes—one the colour of" | | 5 | "She froze, one hand still" | | 6 | "He tilted his head." | | 7 | "The ivory handle of his" | | 8 | "Ptolemy darted between her feet" | | 9 | "Lucien's gaze dropped to the" | | 10 | "Rory blocked the doorway with" | | 11 | "Heat crawled up her neck." | | 12 | "She stepped aside anyway." | | 13 | "He crossed the threshold." | | 14 | "The door shut." | | 15 | "The three bolts stayed undone." | | 16 | "The flat smelled of old" | | 17 | "Books teetered in towers." | | 18 | "Scrolls unrolled across the sofa." | | 19 | "Notes in Eva's looping script" |
| | ratio | 0.923 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 156 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 1 | | matches | | 0 | "The particular human-style wards that had stumped even him." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |