| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva turned back [back]" |
| | dialogueSentences | 63 | | tagDensity | 0.238 | | leniency | 0.476 | | rawRatio | 0.067 | | effectiveRatio | 0.032 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1249 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 87.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1249 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "warmth" | | 1 | "silence" | | 2 | "traced" |
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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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 106 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 154 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1249 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 84 | | wordCount | 935 | | uniqueNames | 13 | | maxNameDensity | 3.64 | | worstName | "Aurora" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Carter | 1 | | London | 2 | | Blackwood | 1 | | Aurora | 34 | | Cardiff | 3 | | Eva | 28 | | Prague | 1 | | Silas | 9 | | Brendan | 1 | | Evan | 1 |
| | persons | | 0 | "Raven" | | 1 | "Carter" | | 2 | "Blackwood" | | 3 | "Aurora" | | 4 | "Eva" | | 5 | "Silas" | | 6 | "Brendan" | | 7 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "Prague" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | 0 | "quite flush with the wall" |
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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 | 1249 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 154 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 83 | | mean | 15.05 | | std | 16.62 | | cv | 1.104 | | sampleLengths | | 0 | 49 | | 1 | 78 | | 2 | 7 | | 3 | 17 | | 4 | 48 | | 5 | 12 | | 6 | 66 | | 7 | 9 | | 8 | 8 | | 9 | 3 | | 10 | 3 | | 11 | 44 | | 12 | 4 | | 13 | 7 | | 14 | 41 | | 15 | 8 | | 16 | 14 | | 17 | 14 | | 18 | 26 | | 19 | 6 | | 20 | 6 | | 21 | 9 | | 22 | 21 | | 23 | 1 | | 24 | 4 | | 25 | 60 | | 26 | 5 | | 27 | 17 | | 28 | 4 | | 29 | 7 | | 30 | 7 | | 31 | 5 | | 32 | 60 | | 33 | 6 | | 34 | 2 | | 35 | 4 | | 36 | 10 | | 37 | 3 | | 38 | 8 | | 39 | 8 | | 40 | 3 | | 41 | 15 | | 42 | 6 | | 43 | 4 | | 44 | 3 | | 45 | 27 | | 46 | 3 | | 47 | 7 | | 48 | 10 | | 49 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 106 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 179 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 154 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 940 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.02446808510638298 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002127659574468085 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 154 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 154 | | mean | 8.11 | | std | 6.24 | | cv | 0.77 | | sampleLengths | | 0 | 20 | | 1 | 8 | | 2 | 12 | | 3 | 9 | | 4 | 10 | | 5 | 23 | | 6 | 17 | | 7 | 20 | | 8 | 8 | | 9 | 7 | | 10 | 4 | | 11 | 13 | | 12 | 6 | | 13 | 13 | | 14 | 29 | | 15 | 9 | | 16 | 3 | | 17 | 9 | | 18 | 21 | | 19 | 14 | | 20 | 22 | | 21 | 9 | | 22 | 4 | | 23 | 2 | | 24 | 2 | | 25 | 3 | | 26 | 2 | | 27 | 1 | | 28 | 24 | | 29 | 12 | | 30 | 4 | | 31 | 4 | | 32 | 4 | | 33 | 5 | | 34 | 2 | | 35 | 13 | | 36 | 18 | | 37 | 10 | | 38 | 4 | | 39 | 4 | | 40 | 8 | | 41 | 6 | | 42 | 9 | | 43 | 5 | | 44 | 12 | | 45 | 7 | | 46 | 7 | | 47 | 6 | | 48 | 6 | | 49 | 9 |
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| 41.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.23376623376623376 | | totalSentences | 154 | | uniqueOpeners | 36 | |
| 37.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 88 | | matches | | 0 | "Then the calls stopped." |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 88 | | matches | | 0 | "His grey-streaked auburn hair caught" | | 1 | "He lifted his head when" | | 2 | "She had her usual corner" | | 3 | "She set her bag down," | | 4 | "Her hair had gone shorter," | | 5 | "She wore a tailored coat" | | 6 | "They hadn’t spoken since the" | | 7 | "His left leg gave a" | | 8 | "He watched them with hazel" | | 9 | "He gave her a small" | | 10 | "He had watched her move" | | 11 | "He had given her a" | | 12 | "Her mouth twitched." | | 13 | "They sat with the space" | | 14 | "They had practiced arguments at" | | 15 | "It was small, but Aurora" | | 16 | "She lifted the whisky finally" | | 17 | "He said nothing, only turned" | | 18 | "She thought of mornings when" | | 19 | "She thought of the scar" |
| | ratio | 0.295 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 88 | | matches | | 0 | "The green neon sign stuttered" | | 1 | "Rain had turned the street" | | 2 | "Aurora Carter ducked under the" | | 3 | "The bell over the door" | | 4 | "Maps covered the walls in" | | 5 | "Silas Blackwood stood behind the" | | 6 | "His grey-streaked auburn hair caught" | | 7 | "He lifted his head when" | | 8 | "Aurora smiled without warmth." | | 9 | "She had her usual corner" | | 10 | "The one that faced the" | | 11 | "She set her bag down," | | 12 | "A chair scraped at the" | | 13 | "Aurora looked up." | | 14 | "Eva sat there with a" | | 15 | "Her hair had gone shorter," | | 16 | "She wore a tailored coat" | | 17 | "The years had thinned her" | | 18 | "Aurora froze with her hand" | | 19 | "Eva’s eyes found hers." |
| | ratio | 0.955 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "Silas Blackwood stood behind the bar, wiping a glass with a cloth that had seen better decades." | | 1 | "Aurora looked at the photographs on the wall, men in trench coats standing in front of buildings that had been bombed flat." | | 2 | "Aurora watched her go, watched the door swing shut, watched Silas return to his cloth and his quiet bar and the photographs of men who had also waited too long …" |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 63 | | tagDensity | 0.19 | | leniency | 0.381 | | rawRatio | 0 | | effectiveRatio | 0 | |