| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 52 | | tagDensity | 0.135 | | leniency | 0.269 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1103 | | totalAiIsmAdverbs | 3 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | | | 2 | |
| | highlights | | 0 | "deliberately" | | 1 | "suddenly" | | 2 | "very" |
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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) | |
| 77.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1103 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "could feel" | | 1 | "pulse" | | 2 | "silence" | | 3 | "charm" |
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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 | 60 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 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 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 84 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1108 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 753 | | uniqueNames | 7 | | maxNameDensity | 0.4 | | worstName | "Eva" | | maxWindowNameDensity | 1 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 2 | | Eva | 3 | | Ptolemy | 2 | | Silence | 1 | | Brick | 1 | | Lane | 1 | | Lucien | 3 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Silence" | | 3 | "Lucien" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 7.14% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 35 | | glossingSentenceCount | 2 | | matches | | 0 | "not quite a smile" | | 1 | "looked like a photograph someone had stag" |
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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 | 1108 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 17.59 | | std | 22.1 | | cv | 1.257 | | sampleLengths | | 0 | 25 | | 1 | 5 | | 2 | 60 | | 3 | 2 | | 4 | 28 | | 5 | 3 | | 6 | 59 | | 7 | 5 | | 8 | 5 | | 9 | 2 | | 10 | 31 | | 11 | 3 | | 12 | 58 | | 13 | 13 | | 14 | 1 | | 15 | 1 | | 16 | 59 | | 17 | 1 | | 18 | 9 | | 19 | 2 | | 20 | 20 | | 21 | 35 | | 22 | 6 | | 23 | 22 | | 24 | 1 | | 25 | 2 | | 26 | 6 | | 27 | 78 | | 28 | 14 | | 29 | 4 | | 30 | 1 | | 31 | 80 | | 32 | 5 | | 33 | 13 | | 34 | 3 | | 35 | 29 | | 36 | 10 | | 37 | 34 | | 38 | 7 | | 39 | 11 | | 40 | 2 | | 41 | 89 | | 42 | 10 | | 43 | 4 | | 44 | 4 | | 45 | 16 | | 46 | 11 | | 47 | 23 | | 48 | 9 | | 49 | 5 |
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| 93.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 60 | | matches | | 0 | "been ruined" | | 1 | "been caught" |
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| 98.99% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 132 | | matches | | 0 | "was shouting" | | 1 | "was watching" |
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| 87.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 104 | | ratio | 0.019 | | matches | | 0 | "He walked past her with that deliberate, unhurried stride, and the smell of him — tobacco, something greener beneath it, the cold rain — moved through the flat like a guest who already knew the furniture." | | 1 | "He only inclined his head, picked up the cane, and crossed to the window to look down at the wet street — giving her, she realised, the small mercy of his back, so she could finish her drink without him watching her hand shake." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 753 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.0398406374501992 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.013280212483399735 | |
| 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 | 10.65 | | std | 13.03 | | cv | 1.223 | | sampleLengths | | 0 | 25 | | 1 | 5 | | 2 | 33 | | 3 | 11 | | 4 | 5 | | 5 | 11 | | 6 | 2 | | 7 | 10 | | 8 | 18 | | 9 | 3 | | 10 | 24 | | 11 | 4 | | 12 | 3 | | 13 | 28 | | 14 | 5 | | 15 | 5 | | 16 | 2 | | 17 | 6 | | 18 | 25 | | 19 | 3 | | 20 | 12 | | 21 | 36 | | 22 | 7 | | 23 | 3 | | 24 | 4 | | 25 | 2 | | 26 | 7 | | 27 | 1 | | 28 | 1 | | 29 | 25 | | 30 | 4 | | 31 | 6 | | 32 | 24 | | 33 | 1 | | 34 | 9 | | 35 | 2 | | 36 | 12 | | 37 | 5 | | 38 | 3 | | 39 | 10 | | 40 | 25 | | 41 | 6 | | 42 | 22 | | 43 | 1 | | 44 | 2 | | 45 | 6 | | 46 | 13 | | 47 | 8 | | 48 | 57 | | 49 | 14 |
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| 63.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4326923076923077 | | totalSentences | 104 | | uniqueOpeners | 45 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Somewhere in the flat, a" |
| | ratio | 0.019 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 54 | | matches | | 0 | "He had one shoulder against" | | 1 | "His mouth moved, not quite" | | 2 | "She didn't move." | | 3 | "She could feel the cold" | | 4 | "He inclined his head, conceding" | | 5 | "She stepped back." | | 6 | "It wasn't a decision so" | | 7 | "He walked past her with" | | 8 | "He set the cane against" | | 9 | "He didn't sit." | | 10 | "She took her time with" | | 11 | "She poured whiskey into a" | | 12 | "His fingers grazed hers." | | 13 | "He had done it on" | | 14 | "She had known he would" | | 15 | "He laughed once, low, and" | | 16 | "She watched his throat work." | | 17 | "She looked away." | | 18 | "He set the mug down" | | 19 | "He caught the papers with" |
| | ratio | 0.593 | |
| 34.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 54 | | matches | | 0 | "The third deadbolt gave with" | | 1 | "Lucien stood on the landing." | | 2 | "He had one shoulder against" | | 3 | "The stairwell light turned his" | | 4 | "Both of them landing on" | | 5 | "His mouth moved, not quite" | | 6 | "She didn't move." | | 7 | "Ptolemy wound between her ankles," | | 8 | "The cat was warm." | | 9 | "Lucien was not." | | 10 | "She could feel the cold" | | 11 | "He inclined his head, conceding" | | 12 | "She stepped back." | | 13 | "It wasn't a decision so" | | 14 | "He walked past her with" | | 15 | "He set the cane against" | | 16 | "He didn't sit." | | 17 | "Rory shut the door." | | 18 | "She took her time with" | | 19 | "She poured whiskey into a" |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 3 | | matches | | 0 | "The kettle escalated and she crossed the room to deal with it only because staying still had become impossible, and he turned to watch her go, and in the galley…" | | 1 | "He hadn't moved from where she'd left him, one hand resting on the back of Eva's reading chair, the other hanging at his side with the loose grace of a man who …" | | 2 | "Did not offer him a refill, though he was watching the bottle with faint amusement, as though he could see the shape of her next sentence forming before she mad…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 2 | | matches | | 0 | "His mouth moved, not quite a smile" | | 1 | "he said, quiet," |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 52 | | tagDensity | 0.115 | | leniency | 0.231 | | rawRatio | 0.167 | | effectiveRatio | 0.038 | |