| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said finally [finally]" |
| | dialogueSentences | 27 | | tagDensity | 0.481 | | leniency | 0.963 | | rawRatio | 0.077 | | effectiveRatio | 0.074 | |
| 86.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1139 | | totalAiIsmAdverbs | 3 | | 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) | |
| 69.27% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1139 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "echoed" | | 1 | "almost alive" | | 2 | "implication" | | 3 | "familiar" | | 4 | "quivered" | | 5 | "could feel" | | 6 | "pulse" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 54 | | matches | | |
| 37.04% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 54 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "managed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1129 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 776 | | uniqueNames | 13 | | maxNameDensity | 1.8 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 14 | | Camden | 2 | | Passage | 1 | | Tube | 1 | | Morris | 3 | | Officer | 1 | | Eva | 12 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Officer" | | 4 | "Eva" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.598 | | windowScore | 0.333 | |
| 94.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed straightforward enough: 23B Camden Passage, an old converted warehouse district where the narrow alleyways still echoed with the clang of the old shipping days" |
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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 | 1129 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 41.81 | | std | 23.37 | | cv | 0.559 | | sampleLengths | | 0 | 71 | | 1 | 73 | | 2 | 70 | | 3 | 50 | | 4 | 63 | | 5 | 8 | | 6 | 15 | | 7 | 54 | | 8 | 3 | | 9 | 78 | | 10 | 45 | | 11 | 7 | | 12 | 18 | | 13 | 42 | | 14 | 61 | | 15 | 68 | | 16 | 8 | | 17 | 37 | | 18 | 51 | | 19 | 32 | | 20 | 61 | | 21 | 44 | | 22 | 40 | | 23 | 5 | | 24 | 50 | | 25 | 57 | | 26 | 18 |
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| 72.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 54 | | matches | | 0 | "were boarded" | | 1 | "been called" | | 2 | "been found" | | 3 | "was propped" | | 4 | "been told" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 140 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 68 | | ratio | 0.103 | | matches | | 0 | "What wasn't normal was the faint, almost imperceptible shimmer that hung just above the entrance—a distortion in the air that made her eyes water slightly." | | 1 | "Eva looked different without her usual collection of books tucked under her arm—instead, she held a small brass compass that caught the dim light filtering through the grimy windows." | | 2 | "\"The coroner thinks it might not have been murder, actually.\" Eva's green eyes were bright behind her round glasses, and she kept tucking a curl of her red hair behind her left ear—a nervous habit Quinn had come to recognize." | | 3 | "\"Abandoned Tube station. Platform three. They found him slumped against the pillar near what used to be the ticket barriers.\" Eva set the compass down on a crate and picked up a photograph instead—a grainy image of the station, taken from the platform." | | 4 | "They were fresh—too fresh for someone who'd supposedly been dead for six hours." | | 5 | "Quinn felt the familiar tingle at the base of her skull—the same sensation she'd gotten the night DS Morris died." | | 6 | "Three days ago, another body had turned up in the same condition—found in an impossible location, clutching an artifact from the Veil Market." |
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| 91.42% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 783 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.04980842911877394 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.019157088122605363 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 16.6 | | std | 10.3 | | cv | 0.62 | | sampleLengths | | 0 | 33 | | 1 | 28 | | 2 | 10 | | 3 | 15 | | 4 | 25 | | 5 | 14 | | 6 | 2 | | 7 | 17 | | 8 | 26 | | 9 | 25 | | 10 | 19 | | 11 | 18 | | 12 | 22 | | 13 | 10 | | 14 | 15 | | 15 | 19 | | 16 | 29 | | 17 | 6 | | 18 | 2 | | 19 | 12 | | 20 | 3 | | 21 | 40 | | 22 | 14 | | 23 | 3 | | 24 | 43 | | 25 | 35 | | 26 | 12 | | 27 | 12 | | 28 | 21 | | 29 | 7 | | 30 | 3 | | 31 | 15 | | 32 | 10 | | 33 | 16 | | 34 | 16 | | 35 | 18 | | 36 | 43 | | 37 | 12 | | 38 | 13 | | 39 | 15 | | 40 | 28 | | 41 | 8 | | 42 | 12 | | 43 | 18 | | 44 | 7 | | 45 | 12 | | 46 | 39 | | 47 | 11 | | 48 | 9 | | 49 | 12 |
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| 68.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.4117647058823529 | | totalSentences | 68 | | uniqueOpeners | 28 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 50 | | matches | | 0 | "Too late for any legitimate" | | 1 | "Instead, she reached into her" |
| | ratio | 0.04 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 50 | | matches | | 0 | "She'd been called to what" | | 1 | "Her hand moved almost automatically" | | 2 | "She'd kept it anyway, a" | | 3 | "Her partner, Officer Eva Kowalski," | | 4 | "she said finally" | | 5 | "She'd seen Eva use one" | | 6 | "They were fresh—too fresh for" | | 7 | "It gleamed dully in the" | | 8 | "She picked up the compass" | | 9 | "She'd been told to drop" |
| | ratio | 0.2 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 50 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The address had seemed straightforward" | | 2 | "The windows were boarded up," | | 3 | "Quinn reached into her coat" | | 4 | "She'd been called to what" | | 5 | "The problem was, the body" | | 6 | "Her hand moved almost automatically" | | 7 | "DS Morris had always teased" | | 8 | "She'd kept it anyway, a" | | 9 | "The door to the warehouse" | | 10 | "Her partner, Officer Eva Kowalski," | | 11 | "Eva looked different without her" | | 12 | "Eva said, not looking up" | | 13 | "Quinn kept her voice low" | | 14 | "Eva's green eyes were bright" | | 15 | "Eva set the compass down" | | 16 | "Quinn studied the photograph, her" | | 17 | "The station name was barely" | | 18 | "she said finally" | | 19 | "Eva looked surprised." |
| | ratio | 0.8 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn stepped off the squad car and into the gathering dusk, her polished boots hitting the pavement with the steady rhythm of someone who'd sp…" | | 1 | "What wasn't normal was the faint, almost imperceptible shimmer that hung just above the entrance—a distortion in the air that made her eyes water slightly." | | 2 | "Eva looked different without her usual collection of books tucked under her arm—instead, she held a small brass compass that caught the dim light filtering thro…" | | 3 | "They were fresh—too fresh for someone who'd supposedly been dead for six hours." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva said, not looking up" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "Eva explained (explain)" |
| | dialogueSentences | 27 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.167 | | effectiveRatio | 0.074 | |