| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 852 | | totalAiIsmAdverbs | 1 | | 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) | |
| 88.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 852 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 52 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 52 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 852 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 778 | | uniqueNames | 11 | | maxNameDensity | 0.64 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 5 | | Parkway | 1 | | Soho | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Christopher | 1 | | Herrera | 4 | | Underground | 1 | | Morris | 1 | | Hackney | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Christopher" | | 2 | "Herrera" | | 3 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Hackney" |
| | globalScore | 1 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 1 | | matches | | 0 | "something close to pity" |
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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 | 852 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 40.57 | | std | 24.57 | | cv | 0.606 | | sampleLengths | | 0 | 50 | | 1 | 71 | | 2 | 60 | | 3 | 7 | | 4 | 37 | | 5 | 94 | | 6 | 39 | | 7 | 67 | | 8 | 78 | | 9 | 23 | | 10 | 46 | | 11 | 8 | | 12 | 6 | | 13 | 28 | | 14 | 21 | | 15 | 24 | | 16 | 56 | | 17 | 27 | | 18 | 39 | | 19 | 61 | | 20 | 10 |
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| 91.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 52 | | matches | | 0 | "was supposed" | | 1 | "been welded" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 129 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 379 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.021108179419525065 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 14.44 | | std | 9.41 | | cv | 0.652 | | sampleLengths | | 0 | 20 | | 1 | 10 | | 2 | 20 | | 3 | 18 | | 4 | 29 | | 5 | 8 | | 6 | 16 | | 7 | 24 | | 8 | 13 | | 9 | 2 | | 10 | 21 | | 11 | 7 | | 12 | 5 | | 13 | 11 | | 14 | 4 | | 15 | 17 | | 16 | 2 | | 17 | 19 | | 18 | 20 | | 19 | 4 | | 20 | 49 | | 21 | 19 | | 22 | 6 | | 23 | 14 | | 24 | 16 | | 25 | 13 | | 26 | 19 | | 27 | 3 | | 28 | 16 | | 29 | 12 | | 30 | 21 | | 31 | 12 | | 32 | 33 | | 33 | 3 | | 34 | 14 | | 35 | 6 | | 36 | 17 | | 37 | 29 | | 38 | 8 | | 39 | 6 | | 40 | 14 | | 41 | 14 | | 42 | 21 | | 43 | 16 | | 44 | 8 | | 45 | 4 | | 46 | 41 | | 47 | 6 | | 48 | 5 | | 49 | 7 |
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| 63.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.423728813559322 | | totalSentences | 59 | | uniqueOpeners | 25 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 49 | | matches | | 0 | "Then he had clocked her" | | 1 | "Maybe the walls were thick" | | 2 | "Maybe something else had already" |
| | ratio | 0.061 | |
| 24.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 49 | | matches | | 0 | "Her worn leather watch slapped" | | 1 | "She had tailed it from" | | 2 | "He had not looked back" | | 3 | "He glanced over his shoulder." | | 4 | "He did not stop." | | 5 | "He ducked left into a" | | 6 | "Her shoes skidded on the" | | 7 | "Her breath came hard." | | 8 | "She reached the wall, hauled" | | 9 | "She had seen it on" | | 10 | "His other hand held something" | | 11 | "He pressed it against a" | | 12 | "She drew her weapon and" | | 13 | "He turned, and for a" | | 14 | "He had a scar running" | | 15 | "He shook his head, water" | | 16 | "He lifted both hands, bone" | | 17 | "Her palm caught the cold" | | 18 | "She had never found the" | | 19 | "She had never found him." |
| | ratio | 0.49 | |
| 51.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 49 | | matches | | 0 | "The rain had settled into" | | 1 | "Harlow Quinn tasted it on" | | 2 | "Her worn leather watch slapped" | | 3 | "Herrera's car sat abandoned on" | | 4 | "She had tailed it from" | | 5 | "He had not looked back" | | 6 | "Saint Christopher, patron of travellers," | | 7 | "He glanced over his shoulder." | | 8 | "He did not stop." | | 9 | "He ducked left into a" | | 10 | "Her shoes skidded on the" | | 11 | "The air smelled of frying" | | 12 | "Her breath came hard." | | 13 | "The lane opened onto a" | | 14 | "Herrera vaulted a low wall" | | 15 | "She reached the wall, hauled" | | 16 | "The old station." | | 17 | "She had seen it on" | | 18 | "Herrera stood at the iron" | | 19 | "His other hand held something" |
| | ratio | 0.816 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 49 | | matches | | | ratio | 0.02 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 4 | | matches | | 0 | "Herrera's car sat abandoned on Parkway, driver's door hanging open, hazard lights blinking amber across the wet tarmac." | | 1 | "Now he was ahead of her, a dark shape cutting between a bus shelter and a row of bins, his coat flaring behind him." | | 2 | "He had a scar running the length of his left forearm, white and raised in the wet light, and he flexed his fingers over it as though it ached." | | 3 | "A stairwell fell away beneath her, lit by flickering blue lamps, and from far below came the sound of a low, bowed instrument and laughter that did not sound en…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "He shook, water flying from his curls" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0 | | effectiveRatio | 0 | |