| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said softly [softly]" |
| | dialogueSentences | 7 | | tagDensity | 0.714 | | leniency | 1 | | rawRatio | 0.2 | | effectiveRatio | 0.2 | |
| 55.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 451 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "softly" | | 1 | "quickly" | | 2 | "really" | | 3 | "cautiously" |
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| 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) | |
| 55.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 451 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "furrowed" | | 1 | "scanned" | | 2 | "pulse" | | 3 | "transfixed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 92.95% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 26 | | matches | | |
| 32.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 26 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 29 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 451 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 383 | | uniqueNames | 9 | | maxNameDensity | 1.57 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 6 | | Tube | 1 | | Detective | 3 | | Boyce | 3 | | Mills | 4 | | Platform | 1 | | Morrison | 1 | | Truth | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Detective" | | 3 | "Boyce" | | 4 | "Mills" | | 5 | "Platform" | | 6 | "Morrison" | | 7 | "Truth" |
| | places | (empty) | | globalScore | 0.717 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 23 | | glossingSentenceCount | 2 | | matches | | 0 | "gashes that seemed to pulse with a sicklyfried green" | | 1 | "to his notes, seemingly disinterested in th" |
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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 | 451 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 29 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 12 | | mean | 37.58 | | std | 19.28 | | cv | 0.513 | | sampleLengths | | 0 | 48 | | 1 | 15 | | 2 | 83 | | 3 | 12 | | 4 | 27 | | 5 | 24 | | 6 | 31 | | 7 | 45 | | 8 | 44 | | 9 | 61 | | 10 | 35 | | 11 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 26 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 70 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 29 | | ratio | 0.069 | | matches | | 0 | "But it was the creature itself that captured Quinn's attention - a massive, misshapen beast, its bulk crushing the train tracks beneath it." | | 1 | "The cases going back decades, all of which had ended unsolved - until now." |
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| 89.46% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 245 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.036734693877551024 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0326530612244898 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 29 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 29 | | mean | 15.55 | | std | 7.19 | | cv | 0.462 | | sampleLengths | | 0 | 26 | | 1 | 11 | | 2 | 11 | | 3 | 15 | | 4 | 6 | | 5 | 11 | | 6 | 14 | | 7 | 29 | | 8 | 23 | | 9 | 12 | | 10 | 14 | | 11 | 13 | | 12 | 16 | | 13 | 8 | | 14 | 3 | | 15 | 14 | | 16 | 14 | | 17 | 12 | | 18 | 13 | | 19 | 20 | | 20 | 25 | | 21 | 8 | | 22 | 11 | | 23 | 17 | | 24 | 28 | | 25 | 16 | | 26 | 35 | | 27 | 14 | | 28 | 12 |
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| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.6896551724137931 | | totalSentences | 29 | | uniqueOpeners | 20 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 26 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 26 | | matches | | 0 | "Her partner, Detective Boyce, hovered" | | 1 | "he asked, gesturing at the" | | 2 | "she said softly" | | 3 | "She knelt beside the first" | | 4 | "She had seen this symbol" |
| | ratio | 0.192 | |
| 94.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 19 | | totalSentences | 26 | | matches | | 0 | "Detective Harlow Quinn stepped into" | | 1 | "This was no ordinary crime" | | 2 | "Her partner, Detective Boyce, hovered" | | 3 | "he asked, gesturing at the" | | 4 | "Quinn's eyes scanned the scene" | | 5 | "Blood spattered the crumbling walls," | | 6 | "she said softly" | | 7 | "She knelt beside the first" | | 8 | "Boyce moved closer, squinting" | | 9 | "Quinn nodded grimly." | | 10 | "She had seen this symbol" | | 11 | "The cases going back decades," | | 12 | "Detective Mills, she remembered, though" | | 13 | "Mills stood hunched in the" | | 14 | "These superstition-peddling fools, I knew" | | 15 | "The carnage unfolded before I" | | 16 | "Pain for its arrogance, he" | | 17 | "Mills shook his head at" | | 18 | "Quinn muttered after a long" |
| | ratio | 0.731 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 26 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 3 | | matches | | 0 | "One had what appeared to be a cauterized hole where its heart should have been, while the other bore deep gashes that seemed to pulse with a sicklyfried green." | | 1 | "But it was the creature itself that captured Quinn's attention - a massive, misshapen beast, its bulk crushing the train tracks beneath it." | | 2 | "Mills shook his head at the sight and turned back to his notes, making careful tally of the items and bodies, while Quinn and Boyce prowled closer, seeing some …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn muttered (mutter)" |
| | dialogueSentences | 7 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.333 | | effectiveRatio | 0.286 | |