| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.22 | | leniency | 0.44 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1393 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 60.00% | 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) | |
| 64.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1393 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "blown wide" | | 1 | "footsteps" | | 2 | "echoing" | | 3 | "etched" | | 4 | "scanning" | | 5 | "standard" | | 6 | "whisper" | | 7 | "flickered" | | 8 | "wavering" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1404 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 799 | | uniqueNames | 11 | | maxNameDensity | 2.13 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 17 | | London | 1 | | Marcus | 1 | | Chen | 9 | | Eva | 10 | | Veil | 1 | | Compass | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Marcus" | | 4 | "Chen" | | 5 | "Eva" |
| | places | | | globalScore | 0.436 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.712 | | wordCount | 1404 | | matches | | 0 | "not footprints, but something narrower" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 24.63 | | std | 16.13 | | cv | 0.655 | | sampleLengths | | 0 | 57 | | 1 | 57 | | 2 | 52 | | 3 | 5 | | 4 | 28 | | 5 | 24 | | 6 | 13 | | 7 | 22 | | 8 | 8 | | 9 | 7 | | 10 | 56 | | 11 | 11 | | 12 | 1 | | 13 | 31 | | 14 | 30 | | 15 | 48 | | 16 | 27 | | 17 | 19 | | 18 | 18 | | 19 | 34 | | 20 | 14 | | 21 | 12 | | 22 | 25 | | 23 | 32 | | 24 | 22 | | 25 | 11 | | 26 | 35 | | 27 | 9 | | 28 | 37 | | 29 | 4 | | 30 | 26 | | 31 | 18 | | 32 | 46 | | 33 | 22 | | 34 | 7 | | 35 | 68 | | 36 | 3 | | 37 | 35 | | 38 | 5 | | 39 | 13 | | 40 | 20 | | 41 | 18 | | 42 | 22 | | 43 | 10 | | 44 | 2 | | 45 | 45 | | 46 | 52 | | 47 | 16 | | 48 | 25 | | 49 | 43 |
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| 87.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 78 | | matches | | 0 | "been parted" | | 1 | "been sealed" | | 2 | "was streaked" | | 3 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 138 | | matches | | |
| 70.22% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 118 | | ratio | 0.025 | | matches | | 0 | "She straightened and examined the man’s hands — soft, uncalloused, a wedding ring gold and polished." | | 1 | "A single line of faint impressions caught her eye — not footprints, but something narrower." | | 2 | "Behind the stone, she heard something — a whisper of air, a drip of water, the distant rustle of movement." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 723 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.022130013831258646 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004149377593360996 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 11.9 | | std | 8.05 | | cv | 0.676 | | sampleLengths | | 0 | 29 | | 1 | 17 | | 2 | 11 | | 3 | 17 | | 4 | 17 | | 5 | 14 | | 6 | 9 | | 7 | 10 | | 8 | 13 | | 9 | 16 | | 10 | 13 | | 11 | 5 | | 12 | 12 | | 13 | 16 | | 14 | 24 | | 15 | 6 | | 16 | 7 | | 17 | 22 | | 18 | 8 | | 19 | 4 | | 20 | 3 | | 21 | 11 | | 22 | 14 | | 23 | 9 | | 24 | 8 | | 25 | 2 | | 26 | 2 | | 27 | 10 | | 28 | 11 | | 29 | 1 | | 30 | 31 | | 31 | 23 | | 32 | 7 | | 33 | 7 | | 34 | 15 | | 35 | 15 | | 36 | 11 | | 37 | 7 | | 38 | 1 | | 39 | 19 | | 40 | 19 | | 41 | 2 | | 42 | 16 | | 43 | 11 | | 44 | 15 | | 45 | 8 | | 46 | 10 | | 47 | 4 | | 48 | 12 | | 49 | 5 |
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| 65.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4067796610169492 | | totalSentences | 118 | | uniqueOpeners | 48 | |
| 46.95% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 71 | | matches | | 0 | "Just the sharp, hungry look" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 71 | | matches | | 0 | "She ducked under the police" | | 1 | "She straightened and examined the" | | 2 | "His eyes were open, fixed" | | 3 | "He’d been her partner for" | | 4 | "She walked the perimeter, her" | | 5 | "She knelt, running her palm" | | 6 | "she asked, more to herself" | | 7 | "She followed them to the" | | 8 | "She pressed her palm against" | | 9 | "He held up a small" | | 10 | "She took the bag, turning" | | 11 | "She handed it back" | | 12 | "She crouched, her fingers hovering" | | 13 | "She walked to it, pressed" | | 14 | "She stepped back, her hand" | | 15 | "She heard Chen’s sharp intake" | | 16 | "she said, her voice steady" | | 17 | "It pointed straight into the" |
| | ratio | 0.254 | |
| 37.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 71 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "She ducked under the police" | | 2 | "The air hung thick and" | | 3 | "The victim’s throat had been" | | 4 | "Quinn crouched, running a gloved" | | 5 | "The flesh had been parted" | | 6 | "She straightened and examined the" | | 7 | "His eyes were open, fixed" | | 8 | "DS Marcus Chen stepped forward" | | 9 | "He’d been her partner for" | | 10 | "Quinn nodded, pointed to the" | | 11 | "Chen’s pen stopped moving." | | 12 | "She walked the perimeter, her" | | 13 | "The station had been sealed" | | 14 | "She knelt, running her palm" | | 15 | "Someone had swept this place" | | 16 | "she asked, more to herself" | | 17 | "Chen shone his torch down" | | 18 | "Quinn’s gaze drifted to the" | | 19 | "A single line of faint" |
| | ratio | 0.845 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 1 | | matches | | 0 | "She crouched, her fingers hovering over the wound without touching." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 5 | | matches | | 0 | "she asked, more to herself than to Chen" | | 1 | "the woman said, her voice sharp" | | 2 | "Eva walked, her satchel swinging against her hip" | | 3 | "She heard, the shuffle of officers’ boots on the platform" | | 4 | "she said, her voice steady" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 50 | | tagDensity | 0.14 | | leniency | 0.28 | | rawRatio | 0.143 | | effectiveRatio | 0.04 | |