| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 148 | | tagDensity | 0.101 | | leniency | 0.203 | | rawRatio | 0.067 | | effectiveRatio | 0.014 | |
| 83.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2409 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "gently" | | 1 | "carefully" | | 2 | "quickly" | | 3 | "very" | | 4 | "slowly" |
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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) | |
| 79.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2409 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "footsteps" | | 1 | "stomach" | | 2 | "comforting" | | 3 | "traced" | | 4 | "silence" | | 5 | "charm" | | 6 | "vibrated" | | 7 | "pulse" | | 8 | "familiar" | | 9 | "effortless" |
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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 | 195 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 195 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 329 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2406 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 35 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1683 | | uniqueNames | 13 | | maxNameDensity | 0.95 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 16 | | Moreau | 1 | | Rory | 12 | | Ptolemy | 4 | | Eva | 6 | | Cardiff | 1 | | London | 3 | | Golden | 1 | | Empress | 1 | | Evan | 2 | | Yu-Fei | 1 | | French | 1 | | Silas | 2 |
| | persons | | 0 | "Lucien" | | 1 | "Moreau" | | 2 | "Rory" | | 3 | "Ptolemy" | | 4 | "Eva" | | 5 | "Evan" | | 6 | "Yu-Fei" | | 7 | "Silas" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Golden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 121 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed almost tender" | | 1 | "sounded like something a person could actu" |
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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 | 2406 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 329 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 235 | | mean | 10.24 | | std | 11.74 | | cv | 1.146 | | sampleLengths | | 0 | 21 | | 1 | 10 | | 2 | 6 | | 3 | 8 | | 4 | 19 | | 5 | 1 | | 6 | 62 | | 7 | 3 | | 8 | 5 | | 9 | 4 | | 10 | 55 | | 11 | 7 | | 12 | 13 | | 13 | 4 | | 14 | 19 | | 15 | 3 | | 16 | 2 | | 17 | 10 | | 18 | 47 | | 19 | 10 | | 20 | 1 | | 21 | 2 | | 22 | 9 | | 23 | 47 | | 24 | 2 | | 25 | 2 | | 26 | 8 | | 27 | 1 | | 28 | 20 | | 29 | 1 | | 30 | 1 | | 31 | 10 | | 32 | 20 | | 33 | 8 | | 34 | 1 | | 35 | 1 | | 36 | 24 | | 37 | 4 | | 38 | 4 | | 39 | 4 | | 40 | 20 | | 41 | 7 | | 42 | 10 | | 43 | 3 | | 44 | 29 | | 45 | 23 | | 46 | 12 | | 47 | 4 | | 48 | 4 | | 49 | 3 |
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| 99.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 195 | | matches | | 0 | "been gone" | | 1 | "was wedged" | | 2 | "were fastened" |
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| 51.85% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 315 | | matches | | 0 | "was looking" | | 1 | "was rubbing" | | 2 | "wasn’t offering" | | 3 | "was trying" | | 4 | "was holding" | | 5 | "was buttoning" | | 6 | "was telling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 329 | | ratio | 0.009 | | matches | | 0 | "The blood on his cuff belonged to him; more had soaked through the left side of his shirt." | | 1 | "It was wedged between washing-up liquid and a jar labelled SALT—DO NOT COOK WITH." | | 2 | "Downstairs, someone laughed; crockery clattered, followed by a burst of music." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 711 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.016877637130801686 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004219409282700422 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 329 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 329 | | mean | 7.31 | | std | 5.9 | | cv | 0.807 | | sampleLengths | | 0 | 21 | | 1 | 10 | | 2 | 6 | | 3 | 8 | | 4 | 9 | | 5 | 3 | | 6 | 7 | | 7 | 1 | | 8 | 12 | | 9 | 23 | | 10 | 8 | | 11 | 19 | | 12 | 3 | | 13 | 5 | | 14 | 4 | | 15 | 14 | | 16 | 19 | | 17 | 22 | | 18 | 7 | | 19 | 13 | | 20 | 4 | | 21 | 3 | | 22 | 1 | | 23 | 15 | | 24 | 3 | | 25 | 2 | | 26 | 4 | | 27 | 6 | | 28 | 7 | | 29 | 6 | | 30 | 24 | | 31 | 10 | | 32 | 10 | | 33 | 1 | | 34 | 2 | | 35 | 9 | | 36 | 7 | | 37 | 23 | | 38 | 11 | | 39 | 6 | | 40 | 2 | | 41 | 2 | | 42 | 8 | | 43 | 1 | | 44 | 8 | | 45 | 2 | | 46 | 10 | | 47 | 1 | | 48 | 1 | | 49 | 10 |
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| 44.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.2765957446808511 | | totalSentences | 329 | | uniqueOpeners | 91 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 175 | | matches | | 0 | "Then she remembered the last" | | 1 | "Of course he knew." | | 2 | "Then he had decided she" | | 3 | "At least he wasn’t offering" | | 4 | "Then she messaged Yu-Fei, asking" | | 5 | "Twice, after he used another." | | 6 | "Instead, she moved her thumb" |
| | ratio | 0.04 | |
| 30.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 83 | | totalSentences | 175 | | matches | | 0 | "she said, and pushed the" | | 1 | "His cane caught it before" | | 2 | "She stared at the strip" | | 3 | "He had said it in" | | 4 | "He withdrew the cane immediately." | | 5 | "She shut the door." | | 6 | "She closed her eyes." | | 7 | "She had deliveries at eleven" | | 8 | "She had a clean apron" | | 9 | "She did not have room" | | 10 | "She slid back the chain" | | 11 | "He had one shoulder against" | | 12 | "His slicked-back hair had come" | | 13 | "She looked past him down" | | 14 | "He stepped into the flat." | | 15 | "She closed the door and" | | 16 | "She knew that particular nothing." | | 17 | "It had lived between them" | | 18 | "He took off his jacket" | | 19 | "She fetched Eva’s first-aid box" |
| | ratio | 0.474 | |
| 82.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 132 | | totalSentences | 175 | | matches | | 0 | "The door opened as far" | | 1 | "she said, and pushed the" | | 2 | "His cane caught it before" | | 3 | "She stared at the strip" | | 4 | "He had said it in" | | 5 | "He withdrew the cane immediately." | | 6 | "She shut the door." | | 7 | "The flat smelled of cumin" | | 8 | "Eva had been gone for" | | 9 | "Rory had managed two of" | | 10 | "She closed her eyes." | | 11 | "An excellent word for a" | | 12 | "Lucien made his living knowing" | | 13 | "Rory rested her forehead against" | | 14 | "She had deliveries at eleven" | | 15 | "She had a clean apron" | | 16 | "She did not have room" | | 17 | "Something struck the landing wall" | | 18 | "She slid back the chain" | | 19 | "He had one shoulder against" |
| | ratio | 0.754 | |
| 28.57% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 175 | | matches | | | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 3 | | matches | | 0 | "She had spent months making London ordinary: shifts at the Golden Empress, shopping lists, keys that belonged to her." | | 1 | "She could remember the exact pressure of his hand at her waist, which seemed an unfair thing for a body to retain while the mind was trying to conduct a sensibl…" | | 2 | "She turned and found him sitting awkwardly in Eva’s cluttered kitchen, his shirt open, his hair disordered, watching her as though she could do something worse …" |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "he said again, quieter" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 148 | | tagDensity | 0.095 | | leniency | 0.189 | | rawRatio | 0 | | effectiveRatio | 0 | |