| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn leaned close [close]" |
| | dialogueSentences | 76 | | tagDensity | 0.105 | | leniency | 0.211 | | rawRatio | 0.125 | | effectiveRatio | 0.026 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1593 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 90.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1593 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "stomach" | | 1 | "etched" | | 2 | "velvet" |
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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 | 120 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 188 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1593 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 25.99% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1008 | | uniqueNames | 7 | | maxNameDensity | 2.48 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 25 | | Eva | 13 | | Kowalski | 1 | | Fletcher | 8 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Fletcher" |
| | places | (empty) | | globalScore | 0.26 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1593 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 188 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 133 | | mean | 11.98 | | std | 11.98 | | cv | 1 | | sampleLengths | | 0 | 35 | | 1 | 10 | | 2 | 9 | | 3 | 62 | | 4 | 11 | | 5 | 14 | | 6 | 13 | | 7 | 50 | | 8 | 5 | | 9 | 6 | | 10 | 10 | | 11 | 6 | | 12 | 35 | | 13 | 2 | | 14 | 11 | | 15 | 10 | | 16 | 9 | | 17 | 23 | | 18 | 4 | | 19 | 4 | | 20 | 1 | | 21 | 9 | | 22 | 27 | | 23 | 17 | | 24 | 8 | | 25 | 9 | | 26 | 6 | | 27 | 43 | | 28 | 28 | | 29 | 47 | | 30 | 5 | | 31 | 5 | | 32 | 2 | | 33 | 33 | | 34 | 1 | | 35 | 3 | | 36 | 5 | | 37 | 17 | | 38 | 6 | | 39 | 26 | | 40 | 3 | | 41 | 7 | | 42 | 10 | | 43 | 17 | | 44 | 3 | | 45 | 10 | | 46 | 15 | | 47 | 2 | | 48 | 14 | | 49 | 4 |
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| 96.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 120 | | matches | | 0 | "been tied" | | 1 | "been hung" | | 2 | "been scrubbed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 158 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 188 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1012 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.018774703557312252 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0029644268774703555 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 188 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 188 | | mean | 8.47 | | std | 6.18 | | cv | 0.729 | | sampleLengths | | 0 | 24 | | 1 | 11 | | 2 | 10 | | 3 | 9 | | 4 | 5 | | 5 | 15 | | 6 | 12 | | 7 | 19 | | 8 | 11 | | 9 | 11 | | 10 | 4 | | 11 | 10 | | 12 | 7 | | 13 | 6 | | 14 | 17 | | 15 | 18 | | 16 | 15 | | 17 | 5 | | 18 | 6 | | 19 | 10 | | 20 | 6 | | 21 | 15 | | 22 | 7 | | 23 | 13 | | 24 | 2 | | 25 | 11 | | 26 | 10 | | 27 | 9 | | 28 | 3 | | 29 | 13 | | 30 | 7 | | 31 | 4 | | 32 | 4 | | 33 | 1 | | 34 | 9 | | 35 | 9 | | 36 | 18 | | 37 | 17 | | 38 | 8 | | 39 | 9 | | 40 | 6 | | 41 | 7 | | 42 | 5 | | 43 | 15 | | 44 | 16 | | 45 | 9 | | 46 | 6 | | 47 | 8 | | 48 | 5 | | 49 | 12 |
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| 62.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3882978723404255 | | totalSentences | 188 | | uniqueOpeners | 73 | |
| 30.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 111 | | matches | | 0 | "Somewhere beyond the stalls, a" |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 111 | | matches | | 0 | "Her curly red hair had" | | 1 | "She tucked a strand behind" | | 2 | "His skull shone beneath the" | | 3 | "She stepped over the chalk" | | 4 | "Its amber eyes watched Quinn" | | 5 | "His shoes pointed toward the" | | 6 | "His hands rested on his" | | 7 | "He wore a grey three-piece" | | 8 | "He had a narrow, bloodless" | | 9 | "He looked eighteen, pale, and" | | 10 | "She touched nothing." | | 11 | "Her eyes travelled down his" | | 12 | "She had opened a small" | | 13 | "Its mouth framed a blackness" | | 14 | "She took out a penlight," | | 15 | "They ran parallel for six" | | 16 | "Its nails had been scrubbed" | | 17 | "She stood and walked to" | | 18 | "Its green flame burned without" | | 19 | "It pointed down." |
| | ratio | 0.198 | |
| 27.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 111 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Tonight, somebody had painted over" | | 2 | "Detective Harlow Quinn stopped at" | | 3 | "Canvas stalls crowded the rails." | | 4 | "Lanterns hung from bent signal" | | 5 | "A woman with a fox’s" | | 6 | "The air smelled of wet" | | 7 | "Quinn checked her watch." | | 8 | "The leather strap pressed a" | | 9 | "Eva Kowalski stood beside a" | | 10 | "Her curly red hair had" | | 11 | "She tucked a strand behind" | | 12 | "Eva glanced at the tall" | | 13 | "His skull shone beneath the" | | 14 | "A strip of yellow police" | | 15 | "A shout rose from the" | | 16 | "Quinn looked down." | | 17 | "A chalked circle ran around" | | 18 | "Eva caught Quinn’s sleeve." | | 19 | "Quinn looked at her hand" |
| | ratio | 0.865 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 111 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 1 | | matches | | 0 | "Two men in funeral coats haggled over a stoppered bottle that contained a thundercloud no larger than a fist." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Eva murmured (murmur)" | | 2 | "Fletcher blurted (blurt)" |
| | dialogueSentences | 76 | | tagDensity | 0.066 | | leniency | 0.132 | | rawRatio | 0.6 | | effectiveRatio | 0.079 | |