| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.385 | | leniency | 0.769 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1106 | | 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) | |
| 50.27% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1106 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "mechanical" | | 1 | "flickered" | | 2 | "echoing" | | 3 | "footsteps" | | 4 | "silence" | | 5 | "shimmered" | | 6 | "pounding" | | 7 | "pulsed" | | 8 | "familiar" | | 9 | "echoed" |
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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 | 103 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1102 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.34% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1021 | | uniqueNames | 12 | | maxNameDensity | 1.27 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Quinn | 13 | | Morris | 5 | | Lock | 1 | | Tube | 1 | | Whitechapel | 1 | | Raven | 1 | | Nest | 1 | | English | 1 | | Camden | 2 |
| | persons | | | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "Whitechapel" | | 4 | "Raven" |
| | globalScore | 0.863 | | windowScore | 1 | |
| 32.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 3 | | matches | | 0 | "quite blood" | | 1 | "looked like human teeth strung on copper" | | 2 | "Something between a laugh and a scream" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.907 | | wordCount | 1102 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 111 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 23.96 | | std | 19.04 | | cv | 0.795 | | sampleLengths | | 0 | 29 | | 1 | 3 | | 2 | 45 | | 3 | 39 | | 4 | 45 | | 5 | 40 | | 6 | 5 | | 7 | 20 | | 8 | 34 | | 9 | 73 | | 10 | 19 | | 11 | 62 | | 12 | 5 | | 13 | 5 | | 14 | 42 | | 15 | 40 | | 16 | 13 | | 17 | 50 | | 18 | 11 | | 19 | 7 | | 20 | 48 | | 21 | 8 | | 22 | 58 | | 23 | 7 | | 24 | 10 | | 25 | 53 | | 26 | 51 | | 27 | 6 | | 28 | 25 | | 29 | 7 | | 30 | 2 | | 31 | 46 | | 32 | 16 | | 33 | 33 | | 34 | 13 | | 35 | 5 | | 36 | 12 | | 37 | 11 | | 38 | 16 | | 39 | 10 | | 40 | 6 | | 41 | 26 | | 42 | 18 | | 43 | 6 | | 44 | 18 | | 45 | 4 |
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| 98.45% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 103 | | matches | | 0 | "got caught" | | 1 | "been closed" |
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| 40.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 167 | | matches | | 0 | "was sprinting" | | 1 | "were running" | | 2 | "was talking" | | 3 | "was happening" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1025 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.02926829268292683 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007804878048780488 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 9.93 | | std | 6.45 | | cv | 0.65 | | sampleLengths | | 0 | 10 | | 1 | 19 | | 2 | 3 | | 3 | 4 | | 4 | 18 | | 5 | 23 | | 6 | 12 | | 7 | 13 | | 8 | 14 | | 9 | 12 | | 10 | 13 | | 11 | 17 | | 12 | 3 | | 13 | 14 | | 14 | 1 | | 15 | 25 | | 16 | 5 | | 17 | 3 | | 18 | 17 | | 19 | 9 | | 20 | 9 | | 21 | 16 | | 22 | 9 | | 23 | 18 | | 24 | 27 | | 25 | 2 | | 26 | 2 | | 27 | 15 | | 28 | 5 | | 29 | 5 | | 30 | 9 | | 31 | 4 | | 32 | 19 | | 33 | 24 | | 34 | 15 | | 35 | 5 | | 36 | 5 | | 37 | 13 | | 38 | 23 | | 39 | 5 | | 40 | 1 | | 41 | 13 | | 42 | 22 | | 43 | 5 | | 44 | 4 | | 45 | 9 | | 46 | 12 | | 47 | 19 | | 48 | 2 | | 49 | 17 |
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| 65.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.44144144144144143 | | totalSentences | 111 | | uniqueOpeners | 49 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 91 | | matches | | 0 | "Then came the warehouse in" | | 1 | "Just his radio, still warm," | | 2 | "Just like the warehouse." | | 3 | "Just like Morris." |
| | ratio | 0.044 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 91 | | matches | | 0 | "He cut left into a" | | 1 | "Her jacket was soaked through," | | 2 | "Her knee protested, that old" | | 3 | "She ignored it." | | 4 | "He did it." | | 5 | "She wiped it away with" | | 6 | "Her hand found her mobile." | | 7 | "She should call it in." | | 8 | "She'd seen it in his" | | 9 | "He'd bolted before she could" | | 10 | "She took the stairs instead," | | 11 | "She rounded a corner and" | | 12 | "She spotted the suspect thirty" | | 13 | "He was talking to someone," | | 14 | "She stepped out of the" | | 15 | "She was nothing here, just" | | 16 | "Their eyes met." | | 17 | "She emerged into a smaller" | | 18 | "His chest heaved." | | 19 | "His eyes were wild." |
| | ratio | 0.22 | |
| 91.87% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 91 | | matches | | 0 | "The suspect's trainers slapped against" | | 1 | "Quinn's lungs burned as she" | | 2 | "The figure didn't slow." | | 3 | "He cut left into a" | | 4 | "Her jacket was soaked through," | | 5 | "The suspect vaulted a low" | | 6 | "Quinn was over it two" | | 7 | "Her knee protested, that old" | | 8 | "She ignored it." | | 9 | "Camden Lock sprawled before her," | | 10 | "He did it." | | 11 | "Quinn pulled up short at" | | 12 | "Rain dripped from her close-cropped" | | 13 | "She wiped it away with" | | 14 | "Her hand found her mobile." | | 15 | "She should call it in." | | 16 | "The suspect knew something." | | 17 | "She'd seen it in his" | | 18 | "He'd bolted before she could" | | 19 | "Whatever operation they were running" |
| | ratio | 0.736 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 65.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 5 | | matches | | 0 | "Tall, male, moving with the desperate energy of someone who knew exactly what waited if he got caught." | | 1 | "The platform stretched out before her, empty except for old ticket machines and faded advertisements for shows that had closed years ago." | | 2 | "Stalls crowded every available space, their canopies made of materials that shimmered and shifted in the dim light." | | 3 | "Hundreds of people moved between them, haggling, arguing, exchanging things that glinted and smoked and occasionally screamed." | | 4 | "A woman with skin the colour of wet slate brushed past, carrying a cage full of something that had too many legs." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "he whispered (whisper)" |
| | dialogueSentences | 13 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0.667 | | effectiveRatio | 0.308 | |