| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 54 | | tagDensity | 0.407 | | leniency | 0.815 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1311 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 69.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1311 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "systematic" | | 1 | "familiar" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "comfortable" | | 5 | "silence" | | 6 | "flickered" | | 7 | "whisper" |
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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 | 70 | | matches | (empty) | |
| 81.63% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 70 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1298 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 728 | | uniqueNames | 10 | | maxNameDensity | 2.2 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Metropolitan | 1 | | Police | 1 | | Patterson | 10 | | Harlow | 16 | | Trent | 4 | | Morris | 4 | | Eva | 10 | | Marcus | 4 |
| | persons | | 0 | "Camden" | | 1 | "Police" | | 2 | "Patterson" | | 3 | "Harlow" | | 4 | "Trent" | | 5 | "Morris" | | 6 | "Eva" | | 7 | "Marcus" |
| | places | (empty) | | globalScore | 0.401 | | windowScore | 0.167 | |
| 51.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like chalk dust, forming an incomp" | | 1 | "appeared, small and freckled, with curly red hair escaping from under a knit cap" |
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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 | 1298 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 101 | | matches | | 0 | "heard that explanation" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 26.49 | | std | 18.38 | | cv | 0.694 | | sampleLengths | | 0 | 62 | | 1 | 23 | | 2 | 43 | | 3 | 13 | | 4 | 6 | | 5 | 42 | | 6 | 27 | | 7 | 58 | | 8 | 15 | | 9 | 27 | | 10 | 6 | | 11 | 14 | | 12 | 66 | | 13 | 15 | | 14 | 27 | | 15 | 29 | | 16 | 11 | | 17 | 55 | | 18 | 10 | | 19 | 4 | | 20 | 56 | | 21 | 45 | | 22 | 19 | | 23 | 16 | | 24 | 8 | | 25 | 57 | | 26 | 6 | | 27 | 44 | | 28 | 9 | | 29 | 60 | | 30 | 16 | | 31 | 28 | | 32 | 4 | | 33 | 22 | | 34 | 32 | | 35 | 35 | | 36 | 7 | | 37 | 7 | | 38 | 20 | | 39 | 53 | | 40 | 13 | | 41 | 32 | | 42 | 21 | | 43 | 5 | | 44 | 48 | | 45 | 38 | | 46 | 10 | | 47 | 9 | | 48 | 25 |
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| 90.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 70 | | matches | | 0 | "been found" | | 1 | "been drawn" | | 2 | "been interrupted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 130 | | matches | | |
| 86.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 101 | | ratio | 0.02 | | matches | | 0 | "Not occult imagery from films or television—these were precise, geometric, almost architectural in their complexity." | | 1 | "Eva met her eyes, and Harlow saw recognition there—the look of someone who'd seen past the comfortable explanations into something darker underneath." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 732 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.030054644808743168 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009562841530054645 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 12.85 | | std | 8.39 | | cv | 0.653 | | sampleLengths | | 0 | 22 | | 1 | 17 | | 2 | 23 | | 3 | 23 | | 4 | 30 | | 5 | 13 | | 6 | 13 | | 7 | 6 | | 8 | 5 | | 9 | 23 | | 10 | 10 | | 11 | 2 | | 12 | 2 | | 13 | 7 | | 14 | 20 | | 15 | 3 | | 16 | 30 | | 17 | 12 | | 18 | 4 | | 19 | 9 | | 20 | 15 | | 21 | 27 | | 22 | 6 | | 23 | 5 | | 24 | 9 | | 25 | 11 | | 26 | 12 | | 27 | 17 | | 28 | 15 | | 29 | 11 | | 30 | 5 | | 31 | 10 | | 32 | 13 | | 33 | 8 | | 34 | 6 | | 35 | 15 | | 36 | 14 | | 37 | 7 | | 38 | 4 | | 39 | 22 | | 40 | 33 | | 41 | 10 | | 42 | 4 | | 43 | 17 | | 44 | 19 | | 45 | 20 | | 46 | 6 | | 47 | 16 | | 48 | 23 | | 49 | 18 |
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| 93.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5841584158415841 | | totalSentences | 101 | | uniqueOpeners | 59 | |
| 50.51% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 66 | | matches | | 0 | "Just for a second, barely" |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 66 | | matches | | 0 | "She crouched beside the corpse." | | 1 | "She'd heard that explanation before," | | 2 | "She'd never believed it." | | 3 | "She stood, boots scraping against" | | 4 | "He pointed his torch beam" | | 5 | "She'd seen similar markings once" | | 6 | "She'd kept copies of everything" | | 7 | "She carried a worn leather" | | 8 | "Her green eyes fixed on" | | 9 | "She pointed at the gap" | | 10 | "She'd taken her own pictures" | | 11 | "She showed Eva the image" | | 12 | "His pen had stopped clicking" |
| | ratio | 0.197 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 66 | | matches | | 0 | "The cordon tape stretched across" | | 1 | "Harlow ducked beneath it, her" | | 2 | "The Metropolitan Police's temporary halogen" | | 3 | "DS Patterson met her at" | | 4 | "Patterson gestured toward the body" | | 5 | "Harlow moved closer, her military-trained" | | 6 | "She crouched beside the corpse." | | 7 | "Marcus Trent stared upward with" | | 8 | "The expression frozen on his" | | 9 | "Patterson continued, scribbling" | | 10 | "Harlow's jaw tightened." | | 11 | "She'd heard that explanation before," | | 12 | "The official report had listed" | | 13 | "She'd never believed it." | | 14 | "Morris hadn't touched drugs in" | | 15 | "She stood, boots scraping against" | | 16 | "Patterson's pen clicked faster" | | 17 | "He pointed his torch beam" | | 18 | "Harlow followed the light and" | | 19 | "Symbols had been drawn in" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 60.44% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 3 | | matches | | 0 | "DS Patterson met her at the platform level, his notebook already out, pen clicking in that nervous pattern that signalled a complicated scene." | | 1 | "The symbols tugged at memory, their angular precision familiar in a way that made her skull ache." | | 2 | "Eva met her eyes, and Harlow saw recognition there—the look of someone who'd seen past the comfortable explanations into something darker underneath." |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 2 | | matches | | 0 | "She stood, boots scraping against the gritty platform surface" | | 1 | "The woman stopped, seeming to recalibrate" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Patterson continued (continue)" | | 1 | "Patterson laughed (laugh)" |
| | dialogueSentences | 54 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 0.667 | | effectiveRatio | 0.074 | |