| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2579 | | totalAiIsmAdverbs | 4 | | 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) | |
| 88.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2579 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "unsettled" | | 1 | "silence" | | 2 | "warmth" | | 3 | "footsteps" |
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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 | 148 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 148 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 277 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2579 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1716 | | uniqueNames | 9 | | maxNameDensity | 0.82 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Lucien | 6 | | Moreau | 1 | | Eva | 6 | | Ptolemy | 9 | | Welsh | 1 | | Golden | 1 | | Empress | 1 | | Marseille | 2 | | Rory | 14 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Eva" | | 3 | "Ptolemy" | | 4 | "Empress" | | 5 | "Rory" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 86.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 118 | | glossingSentenceCount | 3 | | matches | | 0 | "smelled like: orange blossom and petrol" | | 1 | "went into him visibly" | | 2 | "smelled like chalk" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.388 | | wordCount | 2579 | | matches | | 0 | "not in guilt, but as if he had a ledger open on his knees" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 277 | | matches | | 0 | "knew that look" | | 1 | "let that be" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 183 | | mean | 14.09 | | std | 16.06 | | cv | 1.14 | | sampleLengths | | 0 | 65 | | 1 | 39 | | 2 | 14 | | 3 | 14 | | 4 | 11 | | 5 | 9 | | 6 | 41 | | 7 | 4 | | 8 | 1 | | 9 | 1 | | 10 | 12 | | 11 | 11 | | 12 | 3 | | 13 | 1 | | 14 | 2 | | 15 | 6 | | 16 | 51 | | 17 | 22 | | 18 | 3 | | 19 | 12 | | 20 | 5 | | 21 | 66 | | 22 | 1 | | 23 | 1 | | 24 | 40 | | 25 | 43 | | 26 | 3 | | 27 | 3 | | 28 | 9 | | 29 | 38 | | 30 | 6 | | 31 | 2 | | 32 | 22 | | 33 | 4 | | 34 | 7 | | 35 | 26 | | 36 | 9 | | 37 | 62 | | 38 | 4 | | 39 | 1 | | 40 | 10 | | 41 | 21 | | 42 | 5 | | 43 | 5 | | 44 | 12 | | 45 | 7 | | 46 | 24 | | 47 | 5 | | 48 | 4 | | 49 | 1 |
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| 98.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 148 | | matches | | 0 | "were required" | | 1 | "were undone" | | 2 | "was smeared" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 276 | | matches | | 0 | "were presenting" | | 1 | "were waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 277 | | ratio | 0.007 | | matches | | 0 | "His amber eye held steady; the black one caught the hallway bulb and went flat as a sealed envelope." | | 1 | "The amber one was steady; the black one watched the movement of her hands." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1718 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 56 | | adverbRatio | 0.03259604190919674 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004074505238649592 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 277 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 277 | | mean | 9.31 | | std | 6.89 | | cv | 0.74 | | sampleLengths | | 0 | 21 | | 1 | 25 | | 2 | 19 | | 3 | 8 | | 4 | 19 | | 5 | 12 | | 6 | 14 | | 7 | 14 | | 8 | 11 | | 9 | 9 | | 10 | 11 | | 11 | 6 | | 12 | 5 | | 13 | 19 | | 14 | 4 | | 15 | 1 | | 16 | 1 | | 17 | 12 | | 18 | 11 | | 19 | 3 | | 20 | 1 | | 21 | 2 | | 22 | 6 | | 23 | 9 | | 24 | 8 | | 25 | 6 | | 26 | 17 | | 27 | 11 | | 28 | 7 | | 29 | 6 | | 30 | 9 | | 31 | 3 | | 32 | 12 | | 33 | 5 | | 34 | 8 | | 35 | 18 | | 36 | 17 | | 37 | 23 | | 38 | 1 | | 39 | 1 | | 40 | 18 | | 41 | 4 | | 42 | 12 | | 43 | 6 | | 44 | 5 | | 45 | 15 | | 46 | 5 | | 47 | 18 | | 48 | 3 | | 49 | 3 |
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| 42.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.19855595667870035 | | totalSentences | 277 | | uniqueOpeners | 55 | |
| 23.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 143 | | matches | | 0 | "Then he folded his hand" |
| | ratio | 0.007 | |
| 1.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 78 | | totalSentences | 143 | | matches | | 0 | "He stood on the landing" | | 1 | "His amber eye held steady;" | | 2 | "He stepped forward before she" | | 3 | "She did not close the" | | 4 | "Her fingers whitened on it." | | 5 | "His gaze settled on the" | | 6 | "She closed the door and" | | 7 | "He did not move from" | | 8 | "His coat dripped on the" | | 9 | "She could smell rain under" | | 10 | "She shut that smell out" | | 11 | "His fingers touched the wood" | | 12 | "He withdrew his hand and" | | 13 | "He folded the coat over" | | 14 | "He did this with care," | | 15 | "He set the cane against" | | 16 | "It sagged under him." | | 17 | "Her delivery jacket still had" | | 18 | "She laughed once and hated" | | 19 | "It made Ptolemy's ears flatten." |
| | ratio | 0.545 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 133 | | totalSentences | 143 | | matches | | 0 | "The door opened on Lucien" | | 1 | "He stood on the landing" | | 2 | "His amber eye held steady;" | | 3 | "Rory kept her hand on" | | 4 | "Lucien looked past her to" | | 5 | "He stepped forward before she" | | 6 | "She did not close the" | | 7 | "Her fingers whitened on it." | | 8 | "Ptolemy slid out from under" | | 9 | "His gaze settled on the" | | 10 | "She closed the door and" | | 11 | "He did not move from" | | 12 | "His coat dripped on the" | | 13 | "She could smell rain under" | | 14 | "She shut that smell out" | | 15 | "His fingers touched the wood" | | 16 | "The space between them grew" | | 17 | "The smile moved his mouth," | | 18 | "He withdrew his hand and" | | 19 | "He folded the coat over" |
| | ratio | 0.93 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 143 | | matches | (empty) | | ratio | 0 | |
| 76.19% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 7 | | matches | | 0 | "He stood on the landing with his ivory cane laid across his arm as though he were presenting a gift to no one in particular." | | 1 | "Three were required to shut Eva's flat against the street, against curiosity, against the people who owed Eva money." | | 2 | "He did this with care, as though the chair belonged to a museum and not to a cramped one-bedroom above a curry house." | | 3 | "The flat shrank around him, his height filling it, his suit making the shelves look smaller." | | 4 | "He caught the cat by the scruff and lifted him onto the floor with one hand, as though the animal had committed only a mild social error." | | 5 | "He stayed still, one hand resting on the pocket, his face softening only around one side." | | 6 | "Outside, someone laughed under the window, and the stairwell filled with footsteps that did not stop at Eva's door." |
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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 | |