| 42.11% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 3 | | adverbTags | | 0 | "Davies shrugged already [already]" | | 1 | "The needle spun wildly [wildly]" | | 2 | "Eva explained patiently [patiently]" |
| | dialogueSentences | 38 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.158 | | effectiveRatio | 0.158 | |
| 94.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 885 | | 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) | |
| 43.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 885 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "footfall" | | 1 | "echoing" | | 2 | "amidst" | | 3 | "perfect" | | 4 | "chill" | | 5 | "could feel" | | 6 | "scanning" | | 7 | "whisper" | | 8 | "etched" | | 9 | "stomach" |
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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 | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 882 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 634 | | uniqueNames | 13 | | maxNameDensity | 1.58 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Quinn | 1 | | Town | 1 | | Saturday | 1 | | Veil | 1 | | Market | 2 | | Inspector | 1 | | Davies | 9 | | Italian | 1 | | City | 1 | | Harlow | 10 | | Eva | 6 | | Kowalski | 1 | | London | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Inspector" | | 2 | "Davies" | | 3 | "Harlow" | | 4 | "Eva" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.711 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 86.62% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.134 | | wordCount | 882 | | matches | | 0 | "not to Davies, but to the space around her" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 26.73 | | std | 18.43 | | cv | 0.689 | | sampleLengths | | 0 | 54 | | 1 | 48 | | 2 | 23 | | 3 | 45 | | 4 | 14 | | 5 | 18 | | 6 | 66 | | 7 | 30 | | 8 | 24 | | 9 | 15 | | 10 | 9 | | 11 | 56 | | 12 | 5 | | 13 | 26 | | 14 | 17 | | 15 | 15 | | 16 | 64 | | 17 | 27 | | 18 | 8 | | 19 | 35 | | 20 | 2 | | 21 | 14 | | 22 | 10 | | 23 | 51 | | 24 | 15 | | 25 | 15 | | 26 | 12 | | 27 | 22 | | 28 | 41 | | 29 | 11 | | 30 | 10 | | 31 | 60 | | 32 | 20 |
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| 91.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 74 | | matches | | 0 | "been placed" | | 1 | "been cleared" | | 2 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 112 | | matches | | |
| 81.41% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 93 | | ratio | 0.022 | | matches | | 0 | "A cluster of figures huddled near the center of the platform—uniformed officers attempting to look professional amidst the grime." | | 1 | "A gust of cold air rushed out, carrying with it a scent that made her stomach clench – the metallic tang of blood, magnified tenfold, mixed with the cloying sweetness of decay." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 636 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.031446540880503145 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.015723270440251572 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 9.48 | | std | 6.51 | | cv | 0.686 | | sampleLengths | | 0 | 12 | | 1 | 14 | | 2 | 12 | | 3 | 8 | | 4 | 3 | | 5 | 5 | | 6 | 10 | | 7 | 19 | | 8 | 19 | | 9 | 17 | | 10 | 6 | | 11 | 5 | | 12 | 11 | | 13 | 8 | | 14 | 5 | | 15 | 5 | | 16 | 11 | | 17 | 12 | | 18 | 2 | | 19 | 7 | | 20 | 11 | | 21 | 9 | | 22 | 11 | | 23 | 3 | | 24 | 1 | | 25 | 3 | | 26 | 4 | | 27 | 2 | | 28 | 2 | | 29 | 18 | | 30 | 8 | | 31 | 5 | | 32 | 6 | | 33 | 18 | | 34 | 6 | | 35 | 14 | | 36 | 10 | | 37 | 15 | | 38 | 2 | | 39 | 7 | | 40 | 9 | | 41 | 28 | | 42 | 5 | | 43 | 2 | | 44 | 12 | | 45 | 5 | | 46 | 10 | | 47 | 10 | | 48 | 6 | | 49 | 10 |
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| 79.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5053763440860215 | | totalSentences | 93 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 66 | | matches | | 0 | "She hated coming down here." | | 1 | "She moved toward the body," | | 2 | "she observed, not to Davies," | | 3 | "It wasn’t pooling naturally." | | 4 | "She touched the edge of" | | 5 | "she stated, rising" | | 6 | "She swept her hand across" | | 7 | "She gestured to a small," | | 8 | "It wasn’t a scuff mark." | | 9 | "She could feel it now," | | 10 | "She spotted Eva Kowalski near" | | 11 | "She'd spent enough time researching" | | 12 | "She walked towards the wall" | | 13 | "She ran her fingers along" | | 14 | "She braced herself and pushed." | | 15 | "she said, her eyes fixed" |
| | ratio | 0.242 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 66 | | matches | | 0 | "The air tasted of dust" | | 1 | "Harlow Quinn descended the slick," | | 2 | "Camden Town above, a thrum" | | 3 | "This was another world, layered" | | 4 | "The Veil Market." | | 5 | "She hated coming down here." | | 6 | "The abandoned tube station reeked" | | 7 | "A cluster of figures huddled" | | 8 | "Detective Inspector Davies, a man" | | 9 | "Harlow didn't return the gesture." | | 10 | "She moved toward the body," | | 11 | "A man, mid-thirties, sprawled on" | | 12 | "A single, precise wound marred" | | 13 | "she observed, not to Davies," | | 14 | "Davies offered, his voice booming" | | 15 | "Harlow crouched, ignoring the protesting" | | 16 | "The victim’s hands were empty," | | 17 | "It wasn’t pooling naturally." | | 18 | "She touched the edge of" | | 19 | "A faint coppery scent, yes," |
| | 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 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 1 | | matches | | 0 | "A gust of cold air rushed out, carrying with it a scent that made her stomach clench – the metallic tang of blood, magnified tenfold, mixed with the cloying swe…" |
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| 19.74% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 4 | | matches | | 0 | "Davies offered, his voice booming" | | 1 | "Harlow countered, her voice a low rasp," | | 2 | "Eva said, her voice barely a whisper" | | 3 | "Harlow said, her voice sharp" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 6 | | fancyTags | | 0 | "she observed (observe)" | | 1 | "she stated (state)" | | 2 | "A worn leather watch she noted (note)" | | 3 | "Eva explained patiently (explain)" | | 4 | "she murmured (murmur)" | | 5 | "Davies warned (warn)" |
| | dialogueSentences | 38 | | tagDensity | 0.289 | | leniency | 0.579 | | rawRatio | 0.545 | | effectiveRatio | 0.316 | |