| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said quietly [quietly]" | | 1 | "she said slowly [slowly]" |
| | dialogueSentences | 44 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0.083 | | effectiveRatio | 0.083 | |
| 87.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2418 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slowly" | | 1 | "lazily" | | 2 | "perfectly" | | 3 | "carefully" | | 4 | "completely" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 56.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2418 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "silence" | | 1 | "fluttered" | | 2 | "familiar" | | 3 | "intricate" | | 4 | "weight" | | 5 | "scanning" | | 6 | "standard" | | 7 | "furrowed" | | 8 | "measured" | | 9 | "gloom" | | 10 | "etched" | | 11 | "stomach" | | 12 | "perfect" | | 13 | "raced" | | 14 | "flickered" | | 15 | "parameters" | | 16 | "database" |
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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 | 217 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 217 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 237 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2418 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1812 | | uniqueNames | 15 | | maxNameDensity | 0.99 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Patel" | | discoveredNames | | Tube | 2 | | Camden | 3 | | London | 4 | | Harlow | 1 | | Quinn | 18 | | Patel | 15 | | Italian | 1 | | Morris | 6 | | Underground | 2 | | Victorian | 2 | | Transport | 1 | | Market | 1 | | British | 1 | | Museum | 1 | | Three | 4 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Patel" | | 4 | "Morris" | | 5 | "Underground" | | 6 | "Museum" | | 7 | "Three" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 144 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared burned into the skin, edges raised and blackened as if cauterized by something incredibly hot" | | 1 | "looked like archaic lettering" |
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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 | 2418 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 237 | | matches | | |
| 73.90% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 41.69 | | std | 17.04 | | cv | 0.409 | | sampleLengths | | 0 | 64 | | 1 | 80 | | 2 | 45 | | 3 | 46 | | 4 | 43 | | 5 | 66 | | 6 | 58 | | 7 | 35 | | 8 | 63 | | 9 | 21 | | 10 | 7 | | 11 | 61 | | 12 | 55 | | 13 | 24 | | 14 | 46 | | 15 | 46 | | 16 | 64 | | 17 | 4 | | 18 | 62 | | 19 | 26 | | 20 | 27 | | 21 | 49 | | 22 | 65 | | 23 | 30 | | 24 | 36 | | 25 | 39 | | 26 | 54 | | 27 | 25 | | 28 | 54 | | 29 | 57 | | 30 | 44 | | 31 | 62 | | 32 | 8 | | 33 | 30 | | 34 | 13 | | 35 | 59 | | 36 | 41 | | 37 | 50 | | 38 | 21 | | 39 | 55 | | 40 | 43 | | 41 | 9 | | 42 | 9 | | 43 | 45 | | 44 | 35 | | 45 | 40 | | 46 | 40 | | 47 | 35 | | 48 | 21 | | 49 | 55 |
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| 93.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 217 | | matches | | 0 | "been dropped" | | 1 | "been rolled" | | 2 | "been etched" | | 3 | "been asphyxiated" | | 4 | "been projected" | | 5 | "been placed" | | 6 | "been brought" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 325 | | matches | | 0 | "was forming" | | 1 | "was only beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 237 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1825 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 62 | | adverbRatio | 0.03397260273972603 | | lyAdverbCount | 28 | | lyAdverbRatio | 0.015342465753424657 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 237 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 237 | | mean | 10.2 | | std | 7.64 | | cv | 0.748 | | sampleLengths | | 0 | 14 | | 1 | 19 | | 2 | 15 | | 3 | 6 | | 4 | 2 | | 5 | 8 | | 6 | 12 | | 7 | 17 | | 8 | 14 | | 9 | 13 | | 10 | 1 | | 11 | 1 | | 12 | 22 | | 13 | 12 | | 14 | 11 | | 15 | 22 | | 16 | 12 | | 17 | 14 | | 18 | 9 | | 19 | 11 | | 20 | 15 | | 21 | 28 | | 22 | 12 | | 23 | 4 | | 24 | 17 | | 25 | 9 | | 26 | 7 | | 27 | 9 | | 28 | 7 | | 29 | 1 | | 30 | 12 | | 31 | 8 | | 32 | 3 | | 33 | 5 | | 34 | 10 | | 35 | 5 | | 36 | 15 | | 37 | 6 | | 38 | 29 | | 39 | 6 | | 40 | 12 | | 41 | 7 | | 42 | 2 | | 43 | 2 | | 44 | 13 | | 45 | 9 | | 46 | 12 | | 47 | 7 | | 48 | 5 | | 49 | 9 |
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| 54.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.35443037974683544 | | totalSentences | 237 | | uniqueOpeners | 84 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 180 | | matches | | 0 | "Instead, it jerked toward the" | | 1 | "More like he'd been asphyxiated." | | 2 | "Instead, she examined the platform" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 180 | | matches | | 0 | "She checked her worn leather" | | 1 | "His face carried the familiar" | | 2 | "She approached the body, noting" | | 3 | "They appeared burned into the" | | 4 | "She tilted her head, studying" | | 5 | "she asked, not looking up" | | 6 | "Her sharp jaw tightened as" | | 7 | "His fingernails were clean, manicured." | | 8 | "She leaned closer, catching a" | | 9 | "she said quietly" | | 10 | "She rose in one fluid" | | 11 | "Her salt-and-pepper hair caught the" | | 12 | "She crushed it down." | | 13 | "She'd reviewed old maps of" | | 14 | "she said, pointing to the" | | 15 | "She clicked on her torch," | | 16 | "She approached slowly, boots crunching" | | 17 | "She pulled on nitrile gloves" | | 18 | "She slipped it into an" | | 19 | "She returned to the body," |
| | ratio | 0.272 | |
| 71.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 140 | | totalSentences | 180 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "The air tasted of damp" | | 3 | "She checked her worn leather" | | 4 | "The call had come in" | | 5 | "Someone had already set up" | | 6 | "Quinn paused at the tape," | | 7 | "A body lay sprawled near" | | 8 | "DS Patel straightened from where" | | 9 | "His face carried the familiar" | | 10 | "Quinn ducked under the tape," | | 11 | "She approached the body, noting" | | 12 | "The fine layer of grime" | | 13 | "Patel gestured toward the dead" | | 14 | "Quinn knelt, the fabric of" | | 15 | "The symbols weren't tattoos." | | 16 | "They appeared burned into the" | | 17 | "She tilted her head, studying" | | 18 | "The burns showed no blistering" | | 19 | "she asked, not looking up" |
| | ratio | 0.778 | |
| 83.33% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 180 | | matches | | 0 | "As if something had been" | | 1 | "Now the pieces shifted, forming" | | 2 | "No matter what waited on" |
| | ratio | 0.017 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn descended the rusted service stairs, her boots ringing against metal that hadn't carried passengers in decades." | | 1 | "They appeared burned into the skin, edges raised and blackened as if cauterized by something incredibly hot." | | 2 | "Yet here it was, complete with Victorian tilework and those strange brass fixtures that didn't match standard London Transport design." | | 3 | "Quinn shot him a look that made him take half a step back." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 4 | | matches | | 0 | "Quinn ducked, her movements crisp with military precision" | | 1 | "she asked, not looking up" | | 2 | "she murmured, more to herself than Patel" | | 3 | "she said, turning to face Patel directly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 2 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "he muttered (mutter)" |
| | dialogueSentences | 44 | | tagDensity | 0.227 | | leniency | 0.455 | | rawRatio | 0.2 | | effectiveRatio | 0.091 | |