| 97.44% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "Lucien said quietly [quietly]" | | 1 | "he said softly [softly]" |
| | dialogueSentences | 39 | | tagDensity | 0.436 | | leniency | 0.872 | | rawRatio | 0.118 | | effectiveRatio | 0.103 | |
| 67.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1380 | | totalAiIsmAdverbs | 9 | | found | | | highlights | | 0 | "quickly" | | 1 | "very" | | 2 | "precisely" | | 3 | "slowly" | | 4 | "softly" |
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| 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) | |
| 85.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1380 | | totalAiIsms | 4 | | found | | 0 | | | 1 | | word | "down her spine" | | count | 1 |
| | 2 | | | 3 | |
| | highlights | | 0 | "tension" | | 1 | "down her spine" | | 2 | "resolve" | | 3 | "efficient" |
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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 | 1 | | narrationSentences | 76 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 63 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1394 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 953 | | uniqueNames | 8 | | maxNameDensity | 0.84 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Three" | | discoveredNames | | Ptolemy | 3 | | Eva | 6 | | Glastonbury | 1 | | Moreau | 3 | | Three | 5 | | London | 1 | | Lucien | 8 | | Rory | 5 |
| | persons | | 0 | "Ptolemy" | | 1 | "Eva" | | 2 | "Moreau" | | 3 | "Lucien" | | 4 | "Rory" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 41.30% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a man who had run out of door" | | 1 | "tasted like rain and cigarette smoke and" |
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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 | 1394 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 30.98 | | std | 27.86 | | cv | 0.899 | | sampleLengths | | 0 | 20 | | 1 | 70 | | 2 | 19 | | 3 | 22 | | 4 | 78 | | 5 | 32 | | 6 | 94 | | 7 | 6 | | 8 | 29 | | 9 | 8 | | 10 | 5 | | 11 | 61 | | 12 | 12 | | 13 | 4 | | 14 | 7 | | 15 | 30 | | 16 | 13 | | 17 | 72 | | 18 | 14 | | 19 | 7 | | 20 | 30 | | 21 | 44 | | 22 | 51 | | 23 | 17 | | 24 | 1 | | 25 | 50 | | 26 | 3 | | 27 | 40 | | 28 | 30 | | 29 | 98 | | 30 | 91 | | 31 | 34 | | 32 | 4 | | 33 | 5 | | 34 | 7 | | 35 | 17 | | 36 | 50 | | 37 | 22 | | 38 | 5 | | 39 | 11 | | 40 | 97 | | 41 | 29 | | 42 | 26 | | 43 | 14 | | 44 | 15 |
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| 86.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 76 | | matches | | 0 | "get caught" | | 1 | "been dragged" | | 2 | "been breached" | | 3 | "was made" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 169 | | matches | | 0 | "was housesitting" | | 1 | "was standing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 97 | | ratio | 0.103 | | matches | | 0 | "Eva had drilled that into her before leaving for Glastonbury — nobody knocks, nobody knows this flat exists, and if anyone does knock, you don't answer." | | 1 | "That was the first thing she noticed — rain silvering the narrow landing, and a man standing in it without an umbrella, which was so wrong that for a second she doubted her own eyes." | | 2 | "His eyes — one amber, one black as a closed door — moved past her shoulder, checking the flat, the windows, the fire escape, before they ever settled on her face." | | 3 | "He dripped on the doormat, taking in the chaos — books on every surface, notes pinned to the walls with string like a madwoman's crossword, Ptolemy watching from atop a tottering pile of grimoires with the flat disapproval of a cat whose territory had been breached." | | 4 | "She barely knew it herself — it surfaced in dreams, in the margins of Eva's research, in the way certain things in certain rooms went quiet when she entered them." | | 5 | "Something moved through his face — the amber eye darkening, the black one catching the lamp like wet tar." | | 6 | "His hand came up — slowly, the way you move toward something that might bolt — and his fingers circled her left wrist, his thumb finding the small crescent scar without looking, like it was a place he'd already memorized." | | 7 | "She'd planned, in some distant part of her mind, to make him work for it — but three months was long enough, and his mouth was right there, and he made a sound low in his chest like a lock finally giving way, and his other hand came up to frame her jaw like she was made of the same ivory as his cane." | | 8 | "He tasted like rain and cigarette smoke and something older, and he kissed her the way he did everything else — precisely at first, and then not precisely at all." | | 9 | "Ptolemy landed on the counter with an offended yowl, knocking the kettle askew, and Rory broke away laughing — a real laugh, unsteady, her forehead against Lucien's soaked shoulder." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 584 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.02910958904109589 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.005136986301369863 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 14.37 | | std | 13.96 | | cv | 0.971 | | sampleLengths | | 0 | 20 | | 1 | 15 | | 2 | 5 | | 3 | 26 | | 4 | 24 | | 5 | 4 | | 6 | 1 | | 7 | 1 | | 8 | 13 | | 9 | 22 | | 10 | 1 | | 11 | 35 | | 12 | 9 | | 13 | 7 | | 14 | 26 | | 15 | 32 | | 16 | 9 | | 17 | 2 | | 18 | 49 | | 19 | 14 | | 20 | 20 | | 21 | 6 | | 22 | 6 | | 23 | 3 | | 24 | 20 | | 25 | 4 | | 26 | 4 | | 27 | 5 | | 28 | 23 | | 29 | 31 | | 30 | 3 | | 31 | 4 | | 32 | 12 | | 33 | 4 | | 34 | 7 | | 35 | 18 | | 36 | 11 | | 37 | 1 | | 38 | 6 | | 39 | 7 | | 40 | 46 | | 41 | 26 | | 42 | 14 | | 43 | 3 | | 44 | 4 | | 45 | 30 | | 46 | 29 | | 47 | 5 | | 48 | 4 | | 49 | 6 |
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| 59.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.4329896907216495 | | totalSentences | 97 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 63 | | matches | | 0 | "Then the second." | | 1 | "Just her name, and it" |
| | ratio | 0.032 | |
| 29.52% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 63 | | matches | | 0 | "She crossed the cluttered sitting" | | 1 | "She should not open the" | | 2 | "She slid the first deadbolt" | | 3 | "She hesitated at the third," | | 4 | "She opened the door." | | 5 | "His voice was rougher than" | | 6 | "His eyes — one amber," | | 7 | "she said, when he didn't" | | 8 | "His hand tightened on the" | | 9 | "She'd never heard him say" | | 10 | "She stepped back and let" | | 11 | "He dripped on the doormat," | | 12 | "She turned toward the kitchenette" | | 13 | "Her hands were steady." | | 14 | "She was proud of her" | | 15 | "She kept her back to" | | 16 | "She'd never told anyone that" | | 17 | "She barely knew it herself" | | 18 | "She turned around." | | 19 | "She laughed, and it came" |
| | ratio | 0.476 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 63 | | matches | | 0 | "The knock came at half" | | 1 | "Rory looked up from Ptolemy's" | | 2 | "Nobody knocked on Eva's door." | | 3 | "Eva had drilled that into" | | 4 | "Rory was housesitting for two" | | 5 | "The knock came again." | | 6 | "She crossed the cluttered sitting" | | 7 | "That was the first thing" | | 8 | "Lucien Moreau did not get" | | 9 | "Lucien Moreau did not stand" | | 10 | "Lucien Moreau glided through the" | | 11 | "Rory put her forehead against" | | 12 | "She should not open the" | | 13 | "She slid the first deadbolt" | | 14 | "She hesitated at the third," | | 15 | "The third bolt gave." | | 16 | "She opened the door." | | 17 | "His voice was rougher than" | | 18 | "His eyes — one amber," | | 19 | "That was Lucien." |
| | ratio | 0.857 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 63 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 2 | | matches | | 0 | "That was the first thing she noticed — rain silvering the narrow landing, and a man standing in it without an umbrella, which was so wrong that for a second she…" | | 1 | "His hand came up — slowly, the way you move toward something that might bolt — and his fingers circled her left wrist, his thumb finding the small crescent scar…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 3 | | fancyTags | | 0 | "he agreed (agree)" | | 1 | "She laughed (laugh)" | | 2 | "he murmured (murmur)" |
| | dialogueSentences | 39 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.231 | | effectiveRatio | 0.154 | |